TY - JOUR AU - Schranz, Madlen AU - Rupprecht, Mirjam AU - Aigner, Annette AU - Benning, Leo AU - Schlump, Carmen AU - Charfeddine, Nesrine AU - Diercke, Michaela AU - Grabenhenrich, Linus AU - Ullrich, Alexander AU - Neuhauser, Hannelore AU - Maier, Birga AU - AU - Hans, Patricius Felix AU - Blaschke, Sabine PY - 2025/2/25 TI - Establishing Syndromic Surveillance of Acute Coronary Syndrome, Myocardial Infarction, and Stroke: Registry Study Based on Routine Data From German Emergency Departments JO - JMIR Public Health Surveill SP - e66218 VL - 11 KW - emergency medicine KW - routinely collected health data KW - public health surveillance KW - syndromic surveillance KW - acute coronary syndrome KW - myocardial infarction KW - stroke KW - routine data KW - Germany KW - emergency department KW - accuracy KW - syndrome KW - diagnosis KW - public health KW - health surveillance N2 - Background: Emergency department (ED) routine data offer a unique opportunity for syndromic surveillance of communicable and noncommunicable diseases (NCDs). In 2020, the Robert Koch Institute established a syndromic surveillance system using ED data from the AKTIN registry. The system provides daily insights into ED utilization for infectious diseases. Adding NCD indicators to the surveillance is of great public health importance, especially during acute events, where timely monitoring enables targeted public health responses and communication. Objective: This study aimed to develop and validate syndrome definitions for the NCD indicators of acute coronary syndrome (ACS), myocardial infarction (MI), and stroke (STR). Methods: First, syndrome definitions were developed with clinical experts combining ED diagnosis, chief complaints, diagnostic certainty, and discharge information. Then, using the multicenter retrospective routine ED data provided by the AKTIN registry, we conducted internal validation by linking ED cases fulfilling the syndrome definition with the hospital discharge diagnoses and calculating sensitivity, specificity, and accuracy. Lastly, external validation comprised the comparison of the ED cases fulfilling the syndrome definition with the federal German hospital diagnosis statistic. Ratios comparing the relative number of cases for all syndrome definitions were calculated and stratified by age and sex. Results: We analyzed data from 9 EDs, totaling 704,797 attendances from January 1, 2019, to March 5, 2021. Syndrome definitions were based on ICD-10 (International Statistical Classification of Diseases and Related Health Problems 10th Revision-German Modification) diagnoses, chief complaints, and discharge information. We identified 4.3% of all cases as ACS, 0.6% as MI, and 3.2% as STR. Patients with ACS and MI were more likely to be male (58.3% and 64.7%), compared to the overall attendances (52.7%). For all syndrome definitions, the prevalence was higher in the older age groups (60?79 years and >80 years), and the highest proportions of cases were assigned an urgency level (3=urgent or 2=very urgent). The internal validation showed accuracy and specificity levels above 96% for all syndrome definitions. The sensitivity was 85.3% for ACS, 56.6% for MI, and 80.5% for STR. The external validation showed high levels of correspondence between the ED data and the German hospital statistics, with most ratios ranging around 1, indicating congruence, particularly in older age groups. The highest differences were noted in younger age groups, with the highest ratios in women aged between 20 and 39 years (4.57 for MI and 4.17 for ACS). Conclusions: We developed NCD indicators for ACS, MI, and STR that showed high levels of internal and external validity. The integration of these indicators into the syndromic surveillance system for EDs could enable daily monitoring of NCD patterns and trends to enhance timely public health surveillance in Germany. UR - https://publichealth.jmir.org/2025/1/e66218 UR - http://dx.doi.org/10.2196/66218 ID - info:doi/10.2196/66218 ER - TY - JOUR AU - DiCaro, Vincent Michael AU - Yee, Brianna AU - Lei, KaChon AU - Batra, Kavita AU - Dawn, Buddhadeb PY - 2025/2/6 TI - Mesenchymal Stem Cell Therapy for Acute Myocardial Infarction: Protocol for a Systematic Review and Meta-Analysis JO - JMIR Res Protoc SP - e60591 VL - 14 KW - mesenchymal stem cells KW - mesenchymal stromal cells KW - progenitor cells KW - acute myocardial infarction KW - outcomes KW - stem cell KW - myocardial KW - protocol KW - systematic review KW - meta-analysis KW - medical therapy KW - therapy KW - cardiac KW - efficacy N2 - Background: Medical therapy and interventional approaches have improved outcomes in patients with acute myocardial infarction (MI). However, these strategies are inadequate for replacing cells lost during tissue ischemia, thereby leaving behind noncontractile scar tissue. The anti-inflammatory and immune modulating properties of mesenchymal stem cells (MSCs) may prove useful in inducing functional cardiac regeneration following acute MI. Objective: This is a protocol for systematic review and meta-analysis that will aggregate and synthesize high-level clinical data on the effects of MSC therapy for acute MI. The findings of this study may serve as evidence for clinicians and researchers in guiding the use of MSC therapy as an adjunct to reperfusion and optimal medical therapy in patients with acute MI. Methods: The proposed systematic review is registered with PROSPERO (International Prospective Register of Systematic Reviews). A systematic search of bibliographical databases, including Embase, PubMed, and Cochrane was conducted from inception to June 2023 to identify English-language human studies with adult patients receiving MSC therapy and optimal medical therapy for acute MI in comparison with respective controls. Article screening was performed using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Data on functional cardiac outcomes and major adverse cardiac events were extracted and analyzed as primary outcomes. Results: Literature search and article screening commenced in June 2023. Data extraction and analysis will be completed by October 2024. The findings will be synthesized and reported by the end of November 2024. Conclusions: This systematic review and meta-analysis will summarize the best available updated evidence from published randomized controlled trials on the effects of MSC therapy for the treatment of acute MI. The findings of this systematic review and meta-analysis may shed light on the efficacy of MSC therapy in improving cardiac functional and structural parameters and reducing adverse cardiac events following acute MI. Trial Registration: PROSPERO CRD42024522398; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=522398 International Registered Report Identifier (IRRID): DERR1-10.2196/60591 UR - https://www.researchprotocols.org/2025/1/e60591 UR - http://dx.doi.org/10.2196/60591 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/60591 ER - TY - JOUR AU - Chin, Wei-Chih AU - Chu, Pao-Hsien AU - Wu, Lung-Sheng AU - Lee, Kuang-Tso AU - Lin, Chen AU - Ho, Chien-Te AU - Yang, Wei-Sheng AU - Chung, I-Hang AU - Huang, Yu-Shu PY - 2025/2/4 TI - The Prognostic Significance of Sleep and Circadian Rhythm for Myocardial Infarction Outcomes: Case-Control Study JO - J Med Internet Res SP - e63897 VL - 27 KW - myocardial infarction KW - circadian rhythm KW - actigraphy KW - nonparametric analysis KW - prognosis KW - sleep KW - heart rate variability KW - activity N2 - Background: Myocardial infarction (MI) is a medical emergency resulting from coronary artery occlusion. Patients with acute MI often experience disturbed sleep and circadian rhythm. Most previous studies assessed the premorbid sleep and circadian rhythm of patients with MI and their correlations with cardiovascular disease. However, little is known about post-MI sleep and circadian rhythm and their impacts on prognosis. The use of actigraphy with different algorithms to evaluate sleep and circadian rhythm after acute MI has the potential for predicting outcomes and preventing future disease progression. Objective: We aimed to evaluate how sleep patterns and disrupted circadian rhythm affect the prognosis of MI, using actigraphy and heart rate variability (HRV). Nonparametric analysis of actigraphy data was performed to examine the circadian rhythm of patients. Methods: Patients with MI in the intensive care unit (ICU) were enrolled alongside age- and gender-matched healthy controls. Actigraphy was used to evaluate sleep and circadian rhythm, while HRV was monitored for 24 hours to assess autonomic nerve function. Nonparametric indicators were calculated to quantify the active-rest patterns, including interdaily stability, intradaily variability, the most active 10 consecutive hours (M10), the least active 5 consecutive hours (L5), the relative amplitude, and the actigraphic dichotomy index. Follow-ups were conducted at 3 and 6 months after discharge to evaluate prognosis, including the duration of current admission, the number and duration of readmission and ICU admission, and catheterization. Independent sample t tests and analysis of covariance were used to compare group differences. Pearson correlation tests were used to explore the correlations of the parameters of actigraphy and HRV with prognosis. Results: The study included 34 patients with MI (mean age 57.65, SD 9.03 years) and 17 age- and gender-matched controls. MI patients had significantly more wake after sleep onset, an increased number of awakenings, and a lower sleep efficiency than controls. Circadian rhythm analysis revealed significantly lower daytime activity in MI patients. Moreover, these patients had a lower relative amplitude and dichotomy index and a higher intradaily variability and midpoint of M10, suggesting less sleep and wake activity changes, more fragmentation of the rest-activity patterns, and a more delayed circadian rhythm. Furthermore, significant correlations were found between the parameters of circadian rhythm analysis, including nighttime activity, time of M10 and L5, and daytime and nighttime activitySD, and patient prognosis. Conclusions: Patients with acute MI experienced significantly worse sleep and disturbed circadian rhythm compared with healthy controls. Our actigraphy-based analysis revealed a disturbed circadian rhythm, including reduced daytime activities, greater fluctuation in hourly activities, and a weak rest-activity rhythm, which were correlated with prognosis. The evaluation of sleep and circadian rhythm in patients with acute MI can serve as a valuable indicator for prognosis and should be further studied. UR - https://www.jmir.org/2025/1/e63897 UR - http://dx.doi.org/10.2196/63897 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63897 ER - TY - JOUR AU - Kou, Yanqi AU - Ye, Shicai AU - Tian, Yuan AU - Yang, Ke AU - Qin, Ling AU - Huang, Zhe AU - Luo, Botao AU - Ha, Yanping AU - Zhan, Liping AU - Ye, Ruyin AU - Huang, Yujie AU - Zhang, Qing AU - He, Kun AU - Liang, Mouji AU - Zheng, Jieming AU - Huang, Haoyuan AU - Wu, Chunyi AU - Ge, Lei AU - Yang, Yuping PY - 2025/1/30 TI - Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study JO - J Med Internet Res SP - e67346 VL - 27 KW - acute myocardial infarction KW - gastrointestinal bleeding KW - machine learning KW - in-hospital KW - prediction model N2 - Background: Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making. Objective: This study aimed to develop and validate a machine learning (ML)?based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support. Methods: A multicenter retrospective cohort study was conducted, including 1910 patients with AMI from the Affiliated Hospital of Guangdong Medical University (2005-2024). Patients were divided into training (n=1575) and testing (n=335) cohorts based on admission dates. For external validation, 1746 patients with AMI were included in the publicly available MIMIC-IV (Medical Information Mart for Intensive Care IV) database. Propensity score matching was adjusted for demographics, and the Boruta algorithm identified key predictors. A total of 7 ML algorithms?logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest (RF), extreme gradient boosting, and neural networks?were trained using 10-fold cross-validation. The models were evaluated for the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, recall, F1-score, and decision curve analysis. Shapley additive explanations analysis ranked variable importance. Kaplan-Meier survival analysis evaluated the impact of GIB on short-term survival. Multivariate logistic regression assessed the relationship between coronary heart disease (CHD) and in-hospital GIB after adjusting for clinical variables. Results: The RF model outperformed other ML models, achieving an area under the receiver operating characteristic curve of 0.77 in the training cohort, 0.77 in the testing cohort, and 0.75 in the validation cohort. Key predictors included red blood cell count, hemoglobin, maximal myoglobin, hematocrit, CHD, and other variables, all of which were strongly associated with GIB risk. Decision curve analysis demonstrated the clinical use of the RF model for early risk stratification. Kaplan-Meier survival analysis showed no significant differences in 7- and 15-day survival rates between patients with AMI with and without GIB (P=.83 for 7-day survival and P=.87 for 15-day survival). Multivariate logistic regression showed that CHD was an independent risk factor for in-hospital GIB (odds ratio 2.79, 95% CI 2.09-3.74). Stratified analyses by sex, age, occupation, marital status, and other subgroups consistently showed that the association between CHD and GIB remained robust across all subgroups. Conclusions: The ML-based RF model provides a robust and clinically applicable tool for predicting in-hospital GIB in patients with AMI. By leveraging routinely available clinical and laboratory data, the model supports early risk stratification and personalized preventive strategies. UR - https://www.jmir.org/2025/1/e67346 UR - http://dx.doi.org/10.2196/67346 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/67346 ER - TY - JOUR AU - Ngaruiya, Christine AU - Samad, Zainab AU - Tajuddin, Salma AU - Nasim, Zarmeen AU - Leff, Rebecca AU - Farhad, Awais AU - Pires, Kyle AU - Khan, Alamgir Muhammad AU - Hartz, Lauren AU - Safdar, Basmah PY - 2024/12/20 TI - Identification of Gender Differences in Acute Myocardial Infarction Presentation and Management at Aga Khan University Hospital-Pakistan: Natural Language Processing Application in a Dataset of Patients With Cardiovascular Disease JO - JMIR Form Res SP - e42774 VL - 8 KW - natural language processing KW - gender-based differences KW - acute coronary syndrome KW - global health KW - Pakistan KW - gender KW - data KW - dataset KW - clinical KW - research KW - management KW - patient KW - medication KW - women KW - tool N2 - Background: Ischemic heart disease is a leading cause of death globally with a disproportionate burden in low- and middle-income countries (LMICs). Natural language processing (NLP) allows for data enrichment in large datasets to facilitate key clinical research. We used NLP to assess gender differences in symptoms and management of patients hospitalized with acute myocardial infarction (AMI) at Aga Khan University Hospital-Pakistan. Objective: The primary objective of this study was to use NLP to assess gender differences in the symptoms and management of patients hospitalized with AMI at a tertiary care hospital in Pakistan. Methods: We developed an NLP-based methodology to extract AMI symptoms and medications from 5358 discharge summaries spanning the years 1988 to 2018. This dataset included patients admitted and discharged between January 1, 1988, and December 31, 2018, who were older than 18 years with a primary discharge diagnosis of AMI (using ICD-9 [International Classification of Diseases, Ninth Revision], diagnostic codes). The methodology used a fuzzy keyword-matching algorithm to extract AMI symptoms from the discharge summaries automatically. It first preprocesses the free text within the discharge summaries to extract passages indicating the presenting symptoms. Then, it applies fuzzy matching techniques to identify relevant keywords or phrases indicative of AMI symptoms, incorporating negation handling to minimize false positives. After manually reviewing the quality of extracted symptoms in a subset of discharge summaries through preliminary experiments, a similarity threshold of 80% was determined. Results: Among 1769 women and 3589 men with AMI, women had higher odds of presenting with shortness of breath (odds ratio [OR] 1.46, 95% CI 1.26-1.70) and lower odds of presenting with chest pain (OR 0.65, 95% CI 0.55-0.75), even after adjustment for diabetes and age. Presentation with abdominal pain, nausea, or vomiting was much less frequent but consistently more common in women (P<.001). ?Ghabrahat,? a culturally distinct term for a feeling of impending doom was used by 5.09% of women and 3.69% of men as presenting symptom for AMI (P=.06). First-line medication prescription (statin and ?-blockers) was lower in women: women had nearly 30% lower odds (OR 0.71, 95% CI 0.57-0.90) of being prescribed statins, and they had 40% lower odds (OR 0.67, 95% CI 0.57-0.78) of being prescribed ?-blockers. Conclusions: Gender-based differences in clinical presentation and medication management were demonstrated in patients with AMI at a tertiary care hospital in Pakistan. The use of NLP for the identification of culturally nuanced clinical characteristics and management is feasible in LMICs and could be used as a tool to understand gender disparities and address key clinical priorities in LMICs. UR - https://formative.jmir.org/2024/1/e42774 UR - http://dx.doi.org/10.2196/42774 UR - http://www.ncbi.nlm.nih.gov/pubmed/39705071 ID - info:doi/10.2196/42774 ER - TY - JOUR AU - Hertz, T. Julian AU - Sakita, M. Francis AU - Rahim, O. Faraan AU - Mmbaga, T. Blandina AU - Shayo, Frida AU - Kaboigora, Vivian AU - Mtui, Julius AU - Bloomfield, S. Gerald AU - Bosworth, B. Hayden AU - Bettger, P. Janet AU - Thielman, M. Nathan PY - 2024/9/24 TI - Multicomponent Intervention to Improve Acute Myocardial Infarction Care in Tanzania: Protocol for a Pilot Implementation Trial JO - JMIR Res Protoc SP - e59917 VL - 13 KW - myocardial infarction KW - Tanzania KW - sub-Saharan Africa KW - implementation science KW - quality improvement N2 - Background: Although the incidence of acute myocardial infarction (AMI) is rising in sub-Saharan Africa, the uptake of evidence-based care for the diagnosis and treatment of AMI is limited throughout the region. In Tanzania, studies have revealed common misdiagnosis of AMI, infrequent administration of aspirin, and high short-term mortality rates following AMI. Objective: This study aims to evaluate the implementation and efficacy outcomes of an intervention, the Multicomponent Intervention to Improve Acute Myocardial Infarction Care (MIMIC), which was developed to improve the delivery of evidence-based AMI care in Tanzania. Methods: This single-arm pilot trial will be conducted in the emergency department (ED) at a referral hospital in northern Tanzania. The MIMIC intervention will be implemented by the ED staff for 1 year. Approximately 400 adults presenting to the ED with possible AMI symptoms will be enrolled, and research assistants will observe their care. Thirty days later, a follow-up survey will be administered to assess mortality and medication use. The primary outcome will be the acceptability of the MIMIC intervention, which will be measured by the Acceptability of Intervention Measurement (AIM) instrument. Acceptability will further be assessed via in-depth interviews with key stakeholders. Secondary implementation outcomes will include feasibility and fidelity. Secondary efficacy outcomes will include the following: the proportion of participants who receive electrocardiogram and cardiac biomarker testing, the proportion of participants with AMI who receive aspirin, 30-day mortality among participants with AMI, and the proportion of participants with AMI taking aspirin 30 days following enrollment. Results: Implementation of MIMIC began on September 1, 2023. Enrollment is expected to be completed by September 1, 2024, and the first results are expected to be published by December 31, 2024. Conclusions: This study will be the first to evaluate an intervention for improving AMI care in sub-Saharan Africa. If MIMIC is found to be acceptable, the findings from this study will inform a future cluster-randomized trial to assess effectiveness and scalability. Trial Registration: ClinicalTrials.gov NCT04563546; https://clinicaltrials.gov/study/NCT04563546 International Registered Report Identifier (IRRID): DERR1-10.2196/59917 UR - https://www.researchprotocols.org/2024/1/e59917 UR - http://dx.doi.org/10.2196/59917 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59917 ER - TY - JOUR AU - Kim, Kwan Yun AU - Seo, Won-Doo AU - Lee, Jung Sun AU - Koo, Hyung Ja AU - Kim, Chul Gyung AU - Song, Seok Hee AU - Lee, Minji PY - 2024/9/17 TI - Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study JO - J Med Internet Res SP - e62890 VL - 26 KW - early cardiac arrest warning system KW - electric medical record KW - explainable clinical decision support system KW - pseudo-real-time evaluation KW - ensemble learning KW - cost-sensitive learning N2 - Background: Cardiac arrest (CA) is one of the leading causes of death among patients in the intensive care unit (ICU). Although many CA prediction models with high sensitivity have been developed to anticipate CA, their practical application has been challenging due to a lack of generalization and validation. Additionally, the heterogeneity among patients in different ICU subtypes has not been adequately addressed. Objective: This study aims to propose a clinically interpretable ensemble approach for the timely and accurate prediction of CA within 24 hours, regardless of patient heterogeneity, including variations across different populations and ICU subtypes. Additionally, we conducted patient-independent evaluations to emphasize the model?s generalization performance and analyzed interpretable results that can be readily adopted by clinicians in real-time. Methods: Patients were retrospectively analyzed using data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and the eICU-Collaborative Research Database (eICU-CRD). To address the problem of underperformance, we constructed our framework using feature sets based on vital signs, multiresolution statistical analysis, and the Gini index, with a 12-hour window to capture the unique characteristics of CA. We extracted 3 types of features from each database to compare the performance of CA prediction between high-risk patient groups from MIMIC-IV and patients without CA from eICU-CRD. After feature extraction, we developed a tabular network (TabNet) model using feature screening with cost-sensitive learning. To assess real-time CA prediction performance, we used 10-fold leave-one-patient-out cross-validation and a cross?data set method. We evaluated MIMIC-IV and eICU-CRD across different cohort populations and subtypes of ICU within each database. Finally, external validation using the eICU-CRD and MIMIC-IV databases was conducted to assess the model?s generalization ability. The decision mask of the proposed method was used to capture the interpretability of the model. Results: The proposed method outperformed conventional approaches across different cohort populations in both MIMIC-IV and eICU-CRD. Additionally, it achieved higher accuracy than baseline models for various ICU subtypes within both databases. The interpretable prediction results can enhance clinicians? understanding of CA prediction by serving as a statistical comparison between non-CA and CA groups. Next, we tested the eICU-CRD and MIMIC-IV data sets using models trained on MIMIC-IV and eICU-CRD, respectively, to evaluate generalization ability. The results demonstrated superior performance compared with baseline models. Conclusions: Our novel framework for learning unique features provides stable predictive power across different ICU environments. Most of the interpretable global information reveals statistical differences between CA and non-CA groups, demonstrating its utility as an indicator for clinical decisions. Consequently, the proposed CA prediction system is a clinically validated algorithm that enables clinicians to intervene early based on CA prediction information and can be applied to clinical trials in digital health. UR - https://www.jmir.org/2024/1/e62890 UR - http://dx.doi.org/10.2196/62890 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/62890 ER - TY - JOUR AU - Mir, Hassan AU - Cullen, J. Katelyn AU - Mosleh, Karen AU - Setrak, Rafi AU - Jolly, Sanjit AU - Tsang, Michael AU - Rutledge, Gregory AU - Ibrahim, Quazi AU - Welsford, Michelle AU - Mercuri, Mathew AU - Schwalm, JD AU - Natarajan, K. Madhu PY - 2024/9/6 TI - Smartphone App for Prehospital ECG Transmission in ST-Elevation Myocardial Infarction Activation: Protocol for a Mixed Methods Study JO - JMIR Res Protoc SP - e55506 VL - 13 KW - ST-elevation myocardial infarction KW - m-health KW - cardiac systems of care KW - knowledge mobilization KW - digital health KW - smartphone technology KW - technology KW - STEMI KW - Canada KW - implementation KW - mobile phone N2 - Background: Timely diagnosis and treatment for ST-elevation myocardial infarction (STEMI) requires a coordinated response from multiple providers. Rapid intervention is key to reducing mortality and morbidity. Activation of the cardiac catheterization laboratory may occur through verbal communication and may also involve the secure sharing of electrocardiographic images between frontline health care providers and interventional cardiologists. To improve this response, we developed a quick, easy-to-use, privacy-compliant smartphone app, that is SMART AMI-ACS (Strategic Management of Acute Reperfusion and Therapies in Acute Myocardial Infarction Acute Coronary Syndromes), for real-time verbal communication and sharing of electrocardiographic images among health care providers in Ontario, Canada. The app further provides information about diagnosis, management, and risk calculators for patients presenting with acute coronary syndrome. Objective: This study aims to integrate the app into workflow processes to improve communication for STEMI activation, resulting in decreased treatment times, improved patient outcomes, and reduced unnecessary catheterization laboratory activation and transfer. Methods: Implementation of the app will be guided by the Reach, Effectiveness, Acceptability, Implementation, and Maintenance (RE-AIM) framework to measure impact. The study will use quantitative registry data already being collected through the SMART AMI project (STEMI registry), the use of the SMART AMI app, and quantitative and qualitative survey data from physicians. Survey questions will be based on the Consolidated Framework for Implementation Research. Descriptive quantitative analysis and thematic qualitative analysis of survey results will be conducted. Continuous variables will be described using either mean and SD or median and IQR values at pre- and postintervention periods by the study sites. Categorical variables, such as false activation, will be described as frequencies (percentages). For each outcome, an interrupted time series regression model will be fitted to evaluate the impact of the app. Results: The primary outcomes of this study include the usability, acceptability, and functionality of the app for physicians. This will be measured using electronic surveys to identify barriers and facilitators to app use. Other key outcomes will measure the implementation of the app by reviewing the timing-of-care intervals, false ?avoidable? catheterization laboratory activation rates, and uptake and use of the app by physicians. Prospective evaluation will be conducted between April 1, 2022, and March 31, 2023. However, for the timing- and accuracy-of-care outcomes, registry data will be compared from January 1, 2019, to March 31, 2023. Data analysis is expected to be completed in Fall 2024, with the completion of a paper for publication anticipated by the end of 2024. Conclusions: Smartphone technology is well integrated into clinical practice and widely accessible. The proposed solution being tested is secure and leverages the accessibility of smartphones. Emergency medicine physicians can use this app to quickly, securely, and accurately transmit information ensuring faster and more appropriate decision-making for STEMI activation. Trial Registration: ClinicalTrials.gov NCT05290389; https://clinicaltrials.gov/study/NCT05290389 International Registered Report Identifier (IRRID): DERR1-10.2196/55506 UR - https://www.researchprotocols.org/2024/1/e55506 UR - http://dx.doi.org/10.2196/55506 UR - http://www.ncbi.nlm.nih.gov/pubmed/39240681 ID - info:doi/10.2196/55506 ER - TY - JOUR AU - Yang, Jingang AU - Li, Yingxue AU - Li, Xiang AU - Tao, Shuiying AU - Zhang, Yuan AU - Chen, Tiange AU - Xie, Guotong AU - Xu, Haiyan AU - Gao, Xiaojin AU - Yang, Yuejin PY - 2024/7/30 TI - A Machine Learning Model for Predicting In-Hospital Mortality in Chinese Patients With ST-Segment Elevation Myocardial Infarction: Findings From the China Myocardial Infarction Registry JO - J Med Internet Res SP - e50067 VL - 26 KW - ST-elevation myocardial infarction KW - in-hospital mortality KW - risk prediction KW - explainable machine learning KW - machine learning KW - acute myocardial infarction KW - myocardial infarction KW - mortality KW - risk KW - predication model KW - china KW - clinical practice KW - validation KW - patient management KW - management N2 - Background: Machine learning (ML) risk prediction models, although much more accurate than traditional statistical methods, are inconvenient to use in clinical practice due to their nontransparency and requirement of a large number of input variables. Objective: We aimed to develop a precise, explainable, and flexible ML model to predict the risk of in-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI). Methods: This study recruited 18,744 patients enrolled in the 2013 China Acute Myocardial Infarction (CAMI) registry and 12,018 patients from the China Patient-Centered Evaluative Assessment of Cardiac Events (PEACE)-Retrospective Acute Myocardial Infarction Study. The Extreme Gradient Boosting (XGBoost) model was derived from 9616 patients in the CAMI registry (2014, 89 variables) with 5-fold cross-validation and validated on both the 9125 patients in the CAMI registry (89 variables) and the independent China PEACE cohort (10 variables). The Shapley Additive Explanations (SHAP) approach was employed to interpret the complex relationships embedded in the proposed model. Results: In the XGBoost model for predicting all-cause in-hospital mortality, the variables with the top 8 most important scores were age, left ventricular ejection fraction, Killip class, heart rate, creatinine, blood glucose, white blood cell count, and use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs). The area under the curve (AUC) on the CAMI validation set was 0.896 (95% CI 0.884-0.909), significantly higher than the previous models. The AUC for the Global Registry of Acute Coronary Events (GRACE) model was 0.809 (95% CI 0.790-0.828), and for the TIMI model, it was 0.782 (95% CI 0.763-0.800). Despite the China PEACE validation set only having 10 available variables, the AUC reached 0.840 (0.829-0.852), showing a substantial improvement to the GRACE (0.762, 95% CI 0.748-0.776) and TIMI (0.789, 95% CI 0.776-0.803) scores. Several novel and nonlinear relationships were discovered between patients? characteristics and in-hospital mortality, including a U-shape pattern of high-density lipoprotein cholesterol (HDL-C). Conclusions: The proposed ML risk prediction model was highly accurate in predicting in-hospital mortality. Its flexible and explainable characteristics make the model convenient to use in clinical practice and could help guide patient management. Trial Registration: ClinicalTrials.gov NCT01874691; https://clinicaltrials.gov/study/NCT01874691 UR - https://www.jmir.org/2024/1/e50067 UR - http://dx.doi.org/10.2196/50067 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/50067 ER - TY - JOUR AU - van den Beuken, F. Wisse M. AU - van Schuppen, Hans AU - Demirtas, Derya AU - van Halm, P. Vokko AU - van der Geest, Patrick AU - Loer, A. Stephan AU - Schwarte, A. Lothar AU - Schober, Patrick PY - 2024/7/25 TI - Investigating Users? Attitudes Toward Automated Smartwatch Cardiac Arrest Detection: Cross-Sectional Survey Study JO - JMIR Hum Factors SP - e57574 VL - 11 KW - out-of-hospital cardiac arrest KW - wearables KW - wearable KW - digital health KW - smartwatch KW - automated cardiac arrest detection KW - emergency medicine KW - emergency KW - cardiology KW - heart KW - cardiac KW - cross sectional KW - survey KW - surveys KW - questionnaire KW - questionnaires KW - experience KW - experiences KW - attitude KW - attitudes KW - opinion KW - perception KW - perceptions KW - perspective KW - perspectives KW - acceptance KW - adoption KW - willingness KW - intent KW - intention N2 - Background: Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality in the developed world. Timely detection of cardiac arrest and prompt activation of emergency medical services (EMS) are essential, yet challenging. Automated cardiac arrest detection using sensor signals from smartwatches has the potential to shorten the interval between cardiac arrest and activation of EMS, thereby increasing the likelihood of survival. Objective: This cross-sectional survey study aims to investigate users? perspectives on aspects of continuous monitoring such as privacy and data protection, as well as other implications, and to collect insights into their attitudes toward the technology. Methods: We conducted a cross-sectional web-based survey in the Netherlands among 2 groups of potential users of automated cardiac arrest technology: consumers who already own a smartwatch and patients at risk of cardiac arrest. Surveys primarily consisted of closed-ended questions with some additional open-ended questions to provide supplementary insight. The quantitative data were analyzed descriptively, and a content analysis of the open-ended questions was conducted. Results: In the consumer group (n=1005), 90.2% (n=906; 95% CI 88.1%-91.9%) of participants expressed an interest in the technology, and 89% (n=1196; 95% CI 87.3%-90.7%) of the patient group (n=1344) showed interest. More than 75% (consumer group: n= 756; patient group: n=1004) of the participants in both groups indicated they were willing to use the technology. The main concerns raised by participants regarding the technology included privacy, data protection, reliability, and accessibility. Conclusions: The vast majority of potential users expressed a strong interest in and positive attitude toward automated cardiac arrest detection using smartwatch technology. However, a number of concerns were identified, which should be addressed in the development and implementation process to optimize acceptance and effectiveness of the technology. UR - https://humanfactors.jmir.org/2024/1/e57574 UR - http://dx.doi.org/10.2196/57574 ID - info:doi/10.2196/57574 ER - TY - JOUR AU - Lee, Hsin-Ying AU - Kuo, Po-Chih AU - Qian, Frank AU - Li, Chien-Hung AU - Hu, Jiun-Ruey AU - Hsu, Wan-Ting AU - Jhou, Hong-Jie AU - Chen, Po-Huang AU - Lee, Cho-Hao AU - Su, Chin-Hua AU - Liao, Po-Chun AU - Wu, I-Ju AU - Lee, Chien-Chang PY - 2024/7/23 TI - Prediction of In-Hospital Cardiac Arrest in the Intensive Care Unit: Machine Learning?Based Multimodal Approach JO - JMIR Med Inform SP - e49142 VL - 12 KW - cardiac arrest KW - machine learning KW - intensive care KW - mortality KW - medical emergency team KW - early warning scores N2 - Background: Early identification of impending in-hospital cardiac arrest (IHCA) improves clinical outcomes but remains elusive for practicing clinicians. Objective: We aimed to develop a multimodal machine learning algorithm based on ensemble techniques to predict the occurrence of IHCA. Methods: Our model was developed by the Multiparameter Intelligent Monitoring of Intensive Care (MIMIC)?IV database and validated in the Electronic Intensive Care Unit Collaborative Research Database (eICU-CRD). Baseline features consisting of patient demographics, presenting illness, and comorbidities were collected to train a random forest model. Next, vital signs were extracted to train a long short-term memory model. A support vector machine algorithm then stacked the results to form the final prediction model. Results: Of 23,909 patients in the MIMIC-IV database and 10,049 patients in the eICU-CRD database, 452 and 85 patients, respectively, had IHCA. At 13 hours in advance of an IHCA event, our algorithm had already demonstrated an area under the receiver operating characteristic curve of 0.85 (95% CI 0.815?0.885) in the MIMIC-IV database. External validation with the eICU-CRD and National Taiwan University Hospital databases also presented satisfactory results, showing area under the receiver operating characteristic curve values of 0.81 (95% CI 0.763-0.851) and 0.945 (95% CI 0.934-0.956), respectively. Conclusions: Using only vital signs and information available in the electronic medical record, our model demonstrates it is possible to detect a trajectory of clinical deterioration up to 13 hours in advance. This predictive tool, which has undergone external validation, could forewarn and help clinicians identify patients in need of assessment to improve their overall prognosis. UR - https://medinform.jmir.org/2024/1/e49142 UR - http://dx.doi.org/10.2196/49142 ID - info:doi/10.2196/49142 ER - TY - JOUR AU - Razjouyan, Javad AU - Orkaby, R. Ariela AU - Horstman, J. Molly AU - Goyal, Parag AU - Intrator, Orna AU - Naik, D. Aanand PY - 2024/6/27 TI - The Frailty Trajectory?s Additional Edge Over the Frailty Index: Retrospective Cohort Study of Veterans With Heart Failure JO - JMIR Aging SP - e56345 VL - 7 KW - gerontology KW - geriatric KW - geriatrics KW - older adult KW - older adults KW - elder KW - elderly KW - older person KW - older people KW - ageing KW - aging KW - frailty KW - frailty index KW - frailty trajectory KW - frail KW - weak KW - weakness KW - heart failure KW - HF KW - cardiovascular disease KW - CVD KW - congestive heart failure KW - CHF KW - myocardial infarction KW - MI KW - unstable angina KW - angina KW - cardiac arrest KW - atherosclerosis KW - cardiology KW - cardiac KW - cardiologist KW - cardiologists UR - https://aging.jmir.org/2024/1/e56345 UR - http://dx.doi.org/10.2196/56345 ID - info:doi/10.2196/56345 ER - TY - JOUR AU - Lee, Heekyung AU - Oh, Jaehoon AU - Choi, Joong Hyuk AU - Shin, Hyungoo AU - Cho, Yongil AU - Lee, Juncheol PY - 2024/6/24 TI - The Incidence and Outcomes of Out-of-Hospital Cardiac Arrest During the COVID-19 Pandemic in South Korea: Multicenter Registry Study JO - JMIR Public Health Surveill SP - e52402 VL - 10 KW - heart arrest KW - cardiopulmonary resuscitation KW - SARS-CoV-2 KW - mortality KW - outpatient KW - cardiac arrest KW - multicenter registry study KW - out-of-hospital cardiac arrest KW - heart attack KW - observational study KW - adult KW - older adults KW - analysis KW - pandemic KW - prepandemic KW - endemic KW - defibrillator KW - COVID-19 N2 - Background: The COVID-19 pandemic has profoundly affected out-of-hospital cardiac arrest (OHCA) and disrupted the chain of survival. Even after the end of the pandemic, the risk of new variants and surges persists. Analyzing the characteristics of OHCA during the pandemic is important to prepare for the next pandemic and to avoid repeated negative outcomes. However, previous studies have yielded somewhat varied results, depending on the health care system or the specific characteristics of social structures. Objective: We aimed to investigate and compare the incidence, outcomes, and characteristics of OHCA during the prepandemic and pandemic periods using data from a nationwide multicenter OHCA registry. Methods: We conducted a multicenter, retrospective, observational study using data from the Korean Cardiac Arrest Resuscitation Consortium (KoCARC) registry. This study included adult patients with OHCA in South Korea across 3 distinct 1-year periods: the prepandemic period (from January to December 2019), early phase pandemic period (from July 2020 to June 2021), and late phase pandemic period (from July 2021 to June 2022). We extracted and contrasted the characteristics of patients with OHCA, prehospital time factors, and outcomes for the patients across these 3 periods. The primary outcomes were survival to hospital admission and survival to hospital discharge. The secondary outcome was good neurological outcome. Results: From the 3 designated periods, a total of 9031 adult patients with OHCA were eligible for analysis (prepandemic: n=2728; early pandemic: n=2954; and late pandemic: n=3349). Witnessed arrest (P<.001) and arrest at home or residence (P=.001) were significantly more frequent during the pandemic period than during the prepandemic period, and automated external defibrillator use by bystanders was lower in the early phase of the pandemic than during other periods. As the pandemic advanced, the rates of the first monitored shockable rhythm (P=.10) and prehospital endotracheal intubation (P<.001) decreased significantly. Time from cardiac arrest cognition to emergency department arrival increased sequentially (prepandemic: 33 min; early pandemic: 35 min; and late pandemic: 36 min; P<.001). Both survival and neurological outcomes worsened as the pandemic progressed, with survival to discharge showing the largest statistical difference (prepandemic: 385/2728, 14.1%; early pandemic: 355/2954, 12%; and late pandemic: 392/3349, 11.7%; P=.01). Additionally, none of the outcomes differed significantly between the early and late phase pandemic periods (all P>.05). Conclusions: During the pandemic, especially amid community COVID-19 surges, the incidence of OHCA increased while survival rates and good neurological outcome at discharge decreased. Prehospital OHCA factors, which are directly related to OHCA prognosis, were adversely affected by the pandemic. Ongoing discussions are needed to maintain the chain of survival in the event of a new pandemic. Trial Registration: ClinicalTrials.gov NCT03222999; https://classic.clinicaltrials.gov/ct2/show/NCT03222999 UR - https://publichealth.jmir.org/2024/1/e52402 UR - http://dx.doi.org/10.2196/52402 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/52402 ER - TY - JOUR AU - Li, ming Yi AU - Jia, Yuheng AU - Bai, Lin AU - Yang, Bosen AU - Chen, Mao AU - Peng, Yong PY - 2024/6/7 TI - U-Shaped Relationship Between Fibrinogen Level and 10-year Mortality in Patients With Acute Coronary Syndrome: Prospective Cohort Study JO - JMIR Public Health Surveill SP - e54485 VL - 10 KW - fibrinogen KW - acute coronary syndrome KW - 10-year mortality KW - risk factor KW - coronary artery disease KW - myocardial KW - heart disease KW - inflammatory factor KW - retrospective study KW - Kaplan-Meier analysis KW - mortality KW - all-cause mortality KW - cubic-spline curve KW - regression model UR - https://publichealth.jmir.org/2024/1/e54485 UR - http://dx.doi.org/10.2196/54485 UR - http://www.ncbi.nlm.nih.gov/pubmed/38848124 ID - info:doi/10.2196/54485 ER - TY - JOUR AU - Pelly, Louise Melissa AU - Fatehi, Farhad AU - Liew, Danny AU - Verdejo-Garcia, Antonio PY - 2023/10/30 TI - Digital Health Secondary Prevention Using Co-Design Procedures: Focus Group Study With Health Care Providers and Patients With Myocardial Infarction JO - JMIR Cardio SP - e49892 VL - 7 KW - co-design KW - digital health KW - myocardial infarction KW - qualitative KW - participatory KW - mobile health N2 - Background: Myocardial infarction (MI) is a debilitating condition and a leading cause of morbidity and mortality worldwide. Digital health is a promising approach for delivering secondary prevention to support patients with a history of MI and for reducing risk factors that can lead to a future event. However, its potential can only be fulfilled when the technology meets the needs of the end users who will be interacting with this secondary prevention. Objective: We aimed to gauge the opinions of patients with a history of MI and health professionals concerning the functions, features, and characteristics of a digital health solution to support post-MI care. Methods: Our approach aligned with the gold standard participatory co-design procedures enabling progressive refinement of feedback via exploratory, confirmatory, and prototype-assisted feedback from participants. Patients with a history of MI and health professionals from Australia attended focus groups over a videoconference system. We engaged with 38 participants across 3 rounds of focus groups using an iterative co-design approach. Round 1 included 8 participants (4 patients and 4 health professionals), round 2 included 24 participants (11 patients and 13 health professionals), and round 3 included 22 participants (14 patients and 8 health professionals). Results: Participants highlighted the potential of digital health in addressing the unmet needs of post-MI care. Both patients with a history of MI and health professionals agreed that mental health is a key concern in post-MI care that requires further support. Participants agreed that family members can be used to support postdischarge care and require support from the health care team. Participants agreed that incorporating simple games with a points system can increase long-term engagement. However, patients with a history of MI emphasized a lack of support from their health care team, family, and community more strongly than health professionals. They also expressed some openness to using artificial intelligence, whereas health professionals expressed that users should not be aware of artificial intelligence use. Conclusions: These results provide valuable insights into the development of digital health secondary preventions aimed at supporting patients with a history of MI. Future research can implement a pilot study in the population with MI to trial these recommendations in a real-world setting. UR - https://cardio.jmir.org/2023/1/e49892 UR - http://dx.doi.org/10.2196/49892 UR - http://www.ncbi.nlm.nih.gov/pubmed/37902821 ID - info:doi/10.2196/49892 ER - TY - JOUR AU - Hammami, Rania AU - Boudiche, Selim AU - Rami, Tlili AU - Ben Halima, Nejeh AU - Jamel, Ahmed AU - Rekik, Bassem AU - Gribaa, Rym AU - Imtinene, Mrad Ben AU - Charfeddine, Salma AU - Ellouze, Tarek AU - Bahloul, Amine AU - Hédi, Slima Ben AU - Langar, Jamel AU - Ben Ahmed, Habib AU - Ibn Elhadj, Zied AU - Hmam, Mohamed AU - Ben Abdessalem, Aymen Mohamed AU - Maaoui, Sabri AU - Fennira, Sana AU - Lobna, Laroussi AU - Hassine, Majed AU - Ouanes, Sami AU - Mohamed Faouzi, Drissi AU - Mallek, Souad AU - Mahdhaoui, Abdallah AU - Meriem, Dghim AU - Jomaa, Walid AU - Zayed, Sofien AU - Kateb, Tawfik AU - Bouchahda, Nidhal AU - Azaiez, Fares AU - Ben Salem, Helmi AU - Marouen, Morched AU - Noamen, Aymen AU - Abdesselem, Salem AU - Hichem, Denguir AU - Ibn Hadj Amor, Hassen AU - Abdeljelil, Farhati AU - Amara, Amine AU - Bejar, Karim AU - Khaldoun, Hamda Ben AU - Hamza, Chiheb AU - Ben Jamaa, Mohsen AU - Fourati, Sami AU - Elleuch, Faycal AU - Grati, Zeineb AU - Chtourou, Slim AU - Marouene, Sami AU - Sahnoun, Mohamed AU - Hadrich, Morched AU - Mohamed Abdelkader, Maalej AU - Bouraoui, Hatem AU - Kamoun, Kamel AU - Hadrich, Moufid AU - Ben Chedli, Tarek AU - Drissa, Akrem Mohamed AU - Charfeddine, Hanene AU - Saadaoui, Nizar AU - Achraf, Gargouri AU - Ahmed, Siala AU - Ayari, Mokdad AU - Nabil, Marsit AU - Mnif, Sabeur AU - Sahnoun, Maher AU - Kammoun, Helmi AU - Ben Jemaa, Khaled AU - Mostari, Gharbi AU - Hamrouni, Nebil AU - Yamen, Maazoun AU - Ellouz, Yassine AU - Smiri, Zahreddine AU - Hdiji, Amine AU - Bassem, Jerbi AU - Ayadi, Wacef AU - Zouari, Amir AU - Abbassi, Chedly AU - Fatma, Masmoudi Boujelben AU - Battikh, Kais AU - Kharrat, Elyes AU - Gtif, Imen AU - Sami, Milouchi AU - Bezdah, Leila AU - Kachboura, Salem AU - Maatouk, Faouzi Mohamed AU - Kraiem, Sondes AU - Jeridi, Gouider AU - Neffati, Elyes AU - Kammoun, Samir AU - Ben Ameur, Youssef AU - Fehri, Wafa AU - Gamra, Habib AU - Zakhama, Lilia AU - Addad, Faouzi AU - Mohamed Sami, Mourali AU - Abid, Leila PY - 2022/8/5 TI - Design and Rationale of the National Tunisian Registry of Percutaneous Coronary Intervention: Protocol for a Prospective Multicenter Observational Study JO - JMIR Res Protoc SP - e24595 VL - 11 IS - 8 KW - percutaneous coronary intervention KW - 1-year outcome KW - Tunisia KW - national KW - multicentric KW - registry KW - percutaneous KW - coronary KW - artery disease N2 - Background: Coronary artery diseases remain the leading cause of death in the world. The management of this condition has improved remarkably in the recent years owing to the development of new technical tools and multicentric registries. Objective: The aim of this study is to investigate the in-hospital and 1-year clinical outcomes of patients treated with percutaneous coronary intervention (PCI) in Tunisia. Methods: We will conduct a prospective multicentric observational study with patients older than 18 years who underwent PCI between January 31, 2020 and June 30, 2020. The primary end point is the occurrence of a major adverse cardiovascular event, defined as cardiovascular death, myocardial infarction, cerebrovascular accident, or target vessel revascularization with either repeat PCI or coronary artery bypass grafting (CABG). The secondary end points are procedural success rate, stent thrombosis, and the rate of redo PCI/CABG for in-stent restenosis. Results: In this study, the demographic profile and the general risk profile of Tunisian patients who underwent PCI and their end points will be analyzed. The complexity level of the procedures and the left main occlusion, bifurcation occlusion, and chronic total occlusion PCI will be analyzed, and immediate as well as long-term results will be determined. The National Tunisian Registry of PCI (NATURE-PCI) will be the first national multicentric registry of angioplasty in Africa. For this study, the institutional ethical committee approval was obtained (0223/2020). This trial consists of 97 cardiologists and 2498 patients who have undergone PCI with a 1-year follow-up period. Twenty-eight catheterization laboratories from both public (15 laboratories) and private (13 laboratories) sectors will enroll patients after receiving informed consent. Of the 2498 patients, 1897 (75.9%) are managed in the public sector and 601 (24.1%) are managed in the private sector. The COVID-19 pandemic started in Tunisia in March 2020; 719 patients (31.9%) were included before the COVID-19 pandemic and 1779 (60.1%) during the pandemic. The inclusion of patients has been finished, and we expect to publish the results by the end of 2022. Conclusions: This study would add data and provide a valuable opportunity for real-world clinical epidemiology and practice in the field of interventional cardiology in Tunisia with insights into the uptake of PCI in this limited-income region. Trial Registration: Clinicaltrials.gov NCT04219761; https://clinicaltrials.gov/ct2/show/NCT04219761 International Registered Report Identifier (IRRID): RR1-10.2196/24595 UR - https://www.researchprotocols.org/2022/8/e24595 UR - http://dx.doi.org/10.2196/24595 UR - http://www.ncbi.nlm.nih.gov/pubmed/35930353 ID - info:doi/10.2196/24595 ER - TY - JOUR AU - Huang, Yanqun AU - Zheng, Zhimin AU - Ma, Moxuan AU - Xin, Xin AU - Liu, Honglei AU - Fei, Xiaolu AU - Wei, Lan AU - Chen, Hui PY - 2022/8/3 TI - Improving the Performance of Outcome Prediction for Inpatients With Acute Myocardial Infarction Based on Embedding Representation Learned From Electronic Medical Records: Development and Validation Study JO - J Med Internet Res SP - e37486 VL - 24 IS - 8 KW - representation learning KW - skip-gram KW - feature association strengths KW - feature importance KW - mortality risk prediction KW - acute myocardial infarction N2 - Background: The widespread secondary use of electronic medical records (EMRs) promotes health care quality improvement. Representation learning that can automatically extract hidden information from EMR data has gained increasing attention. Objective: We aimed to propose a patient representation with more feature associations and task-specific feature importance to improve the outcome prediction performance for inpatients with acute myocardial infarction (AMI). Methods: Medical concepts, including patients? age, gender, disease diagnoses, laboratory tests, structured radiological features, procedures, and medications, were first embedded into real-value vectors using the improved skip-gram algorithm, where concepts in the context windows were selected by feature association strengths measured by association rule confidence. Then, each patient was represented as the sum of the feature embeddings weighted by the task-specific feature importance, which was applied to facilitate predictive model prediction from global and local perspectives. We finally applied the proposed patient representation into mortality risk prediction for 3010 and 1671 AMI inpatients from a public data set and a private data set, respectively, and compared it with several reference representation methods in terms of the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and F1-score. Results: Compared with the reference methods, the proposed embedding-based representation showed consistently superior predictive performance on the 2 data sets, achieving mean AUROCs of 0.878 and 0.973, AUPRCs of 0.220 and 0.505, and F1-scores of 0.376 and 0.674 for the public and private data sets, respectively, while the greatest AUROCs, AUPRCs, and F1-scores among the reference methods were 0.847 and 0.939, 0.196 and 0.283, and 0.344 and 0.361 for the public and private data sets, respectively. Feature importance integrated in patient representation reflected features that were also critical in prediction tasks and clinical practice. Conclusions: The introduction of feature associations and feature importance facilitated an effective patient representation and contributed to prediction performance improvement and model interpretation. UR - https://www.jmir.org/2022/8/e37486 UR - http://dx.doi.org/10.2196/37486 UR - http://www.ncbi.nlm.nih.gov/pubmed/35921141 ID - info:doi/10.2196/37486 ER - TY - JOUR AU - Arutyunov, P. Gregory AU - Arutyunov, G. Alexander AU - Ageev, T. Fail AU - Fofanova, V. Tatiana PY - 2022/7/25 TI - Use of Digital Technology Tools to Characterize Adherence to Prescription-Grade Omega-3 Polyunsaturated Fatty Acid Therapy in Postmyocardial or Hypertriglyceridemic Patients in the DIAPAsOn Study: Prospective Observational Study JO - JMIR Cardio SP - e37490 VL - 6 IS - 2 KW - primary care KW - research KW - myocardial infarction KW - cardiology KW - heart KW - cardiac KW - cardiac health KW - digital health KW - electronic patient engagement KW - eHealth KW - patient engagement KW - clinical report KW - treatment KW - treatment adherence N2 - Background: Maintaining sustained adherence to medication for optimal management of chronic noninfectious diseases, such as atherosclerotic vascular disease, is a well-documented therapeutic challenge. Objective: The DIAPAsOn study was a 6-month, multicenter prospective observational study in the Russian Federation that examined adherence to a preparation of highly purified omega-3 polyunsaturated fatty acids (Omacor) in 2167 adult patients with a history of recent myocardial infarction or endogenous hypertriglyceridemia. Methods: A feature of DIAPAsOn was the use of a bespoke electronic patient engagement and data collection system to monitor adherence. Adherence was also monitored by enquiry at clinic visits. A full description of the study?s aims and methods has appeared in JMIR Research Protocols. Results: The net average reduction from baseline in both total and low-density lipoprotein cholesterol was approximately 1 mmol/L and the net average increase in high-density lipoprotein cholesterol was 0.2 (SD 0.53) mmol/L (P<.001 for all outcomes vs baseline). The mean triglyceride level was 3.0 (SD 1.3) mmol/L at visit 1, 2.0 (SD 0.9) mmol/L at visit 2, and 1.7 (SD 0.7) mmol/L at visit 3 (P<.001 for later visits vs visit 1). The percentage of patients with a triglyceride level <1.7 mmol/L rose from 13.1% (282/2151) at baseline to 54% (1028/1905) at the end of the study. Digital reporting of adherence was registered by 8.3% (180/2167) of patients; average scores indicted poor adherence. However, a clinic-based enquiry suggested high levels of adherence. Data on health-related quality of life accrued from digitally engaged patients identified improvements among patients reporting high adherence to study treatment, but patient numbers were small. Conclusions: The lipid and lipoprotein findings indicate that Omacor had nominally favorable effects on the blood lipid profile. Less than 10% of patients enrolled in DIAPAsOn used the bespoke digital platform piloted in the study, and the level of self-reported adherence to medication by these patients was also low. Reasons for this low uptake and adherence are unclear. Better adherence was recorded in clinical reports. Trial Registration: ClinicalTrials.gov NCT03415152; https://clinicaltrials.gov/ct2/show/NCT03415152 UR - https://cardio.jmir.org/2022/2/e37490 UR - http://dx.doi.org/10.2196/37490 UR - http://www.ncbi.nlm.nih.gov/pubmed/35877173 ID - info:doi/10.2196/37490 ER - TY - JOUR AU - Treskes, Willem Roderick AU - van den Akker-van Marle, Elske M. AU - van Winden, Louise AU - van Keulen, Nicole AU - van der Velde, Tjeerd Enno AU - Beeres, Saskia AU - Atsma, Douwe AU - Schalij, Jan Martin PY - 2022/4/25 TI - The Box?eHealth in the Outpatient Clinic Follow-up of Patients With Acute Myocardial Infarction: Cost-Utility Analysis JO - J Med Internet Res SP - e30236 VL - 24 IS - 4 KW - smart technology KW - myocardial infarction KW - cost-utility KW - outpatients KW - cost-effectiveness KW - eHealth KW - remote monitoring KW - cost of care KW - quality of life N2 - Background: Smartphone compatible wearables have been released on the consumers market, enabling remote monitoring. Remote monitoring is often named as a tool to reduce the cost of care. Objective: The primary purpose of this paper is to describe a cost-utility analysis of an eHealth intervention compared to regular follow-up in patients with acute myocardial infarction (AMI). Methods: In this trial, of which clinical results have been published previously, patients with an AMI were randomized in a 1:1 fashion between an eHealth intervention and regular follow-up. The remote monitoring intervention consisted of a blood pressure monitor, weight scale, electrocardiogram device, and step counter. Furthermore, two in-office outpatient clinic visits were replaced by e-visits. The control group received regular care. The differences in mean costs and quality of life per patient between both groups during one-year follow-up were calculated. Results: Mean costs per patient were ?2417±2043 (US $2657±2246) for the intervention and ?2888±2961 (US $3175±3255) for the control group. This yielded a cost reduction of ?471 (US $518) per patient. This difference was not statistically significant (95% CI ??275 to ?1217; P=.22, US $?302 to $1338). The average quality-adjusted life years in the first year of follow-up was 0.74 for the intervention group and 0.69 for the control (difference ?0.05, 95% CI ?0.09 to ?0.01; P=.01). Conclusions: eHealth in the outpatient clinic setting for patients who suffered from AMI is likely to be cost-effective compared to regular follow-up. Further research should be done to corroborate these findings in other patient populations and different care settings. Trial Registration: ClinicalTrials.gov NCT02976376; https://clinicaltrials.gov/ct2/show/NCT02976376 International Registered Report Identifier (IRRID): RR2-10.2196/resprot.8038 UR - https://www.jmir.org/2022/4/e30236 UR - http://dx.doi.org/10.2196/30236 UR - http://www.ncbi.nlm.nih.gov/pubmed/35468091 ID - info:doi/10.2196/30236 ER - TY - JOUR AU - Ögmundsdóttir Michelsen, Halldóra AU - Sjölin, Ingela AU - Bäck, Maria AU - Gonzalez Garcia, Manuel AU - Olsson, Anneli AU - Sandberg, Camilla AU - Schiopu, Alexandru AU - Leósdóttir, Margrét PY - 2022/3/31 TI - Effect of a Lifestyle-Focused Web-Based Application on Risk Factor Management in Patients Who Have Had a Myocardial Infarction: Randomized Controlled Trial JO - J Med Internet Res SP - e25224 VL - 24 IS - 3 KW - eHealth KW - cardiac rehabilitation KW - cardiovascular KW - mobile device app KW - risk factors KW - web-based application KW - mobile phone N2 - Background: Cardiac rehabilitation is central in reducing mortality and morbidity after myocardial infarction. However, the fulfillment of guideline-recommended cardiac rehabilitation targets is unsatisfactory. eHealth offers new possibilities to improve clinical care. Objective: This study aims to assess the effect of a web-based application designed to support adherence to lifestyle advice and self-control of risk factors (intervention) in addition to center-based cardiac rehabilitation, compared with cardiac rehabilitation only (usual care). Methods: All 150 patients participated in cardiac rehabilitation. Patients randomized to the intervention group (n=101) received access to the application for 25 weeks where information about lifestyle (eg, diet and physical activity), risk factors (eg, weight and blood pressure [BP]), and symptoms could be registered. The software provided feedback and lifestyle advice. The primary outcome was a change in submaximal exercise capacity (Watts [W]) between follow-up visits. Secondary outcomes included changes in modifiable risk factors between baseline and follow-up visits and uptake and adherence to the application. Regression analysis was used, adjusting for relevant baseline variables. Results: There was a nonsignificant trend toward a larger change in exercise capacity in the intervention group (n=66) compared with the usual care group (n=40; +14.4, SD 19.0 W, vs +10.3, SD 16.1 W; P=.22). Patients in the intervention group achieved significantly larger BP reduction compared with usual care patients at 2 weeks (systolic ?27.7 vs ?16.4 mm Hg; P=.006) and at 6 to 10 weeks (systolic ?25.3 vs ?16.4 mm Hg; P=.02, and diastolic ?13.4 vs ?9.1 mm Hg; P=.05). A healthy diet index score improved significantly more between baseline and the 2-week follow-up in the intervention group (+2.3 vs +1.4 points; P=.05), mostly owing to an increase in the consumption of fish and fruit. At 6 to 10 weeks, 64% (14/22) versus 46% (5/11) of smokers in the intervention versus usual care groups had quit smoking, and at 12 to 14 months, the respective percentages were 55% (12/22) versus 36% (4/11). However, the number of smokers in the study was low (33/149, 21.9%), and the differences were nonsignificant. Attendance in cardiac rehabilitation was high, with 96% (96/100) of patients in the intervention group and 98% (48/49) of patients receiving usual care only attending 12- to 14-month follow-up. Uptake (logging data in the application at least once) was 86.1% (87/101). Adherence (logging data at least twice weekly) was 91% (79/87) in week 1 and 56% (49/87) in week 25. Conclusions: Complementing cardiac rehabilitation with a web-based application improved BP and dietary habits during the first months after myocardial infarction. A nonsignificant tendency toward better exercise capacity and higher smoking cessation rates was observed. Although the study group was small, these positive trends support further development of eHealth in cardiac rehabilitation. Trial Registration: ClinicalTrials.gov NCT03260582; https://clinicaltrials.gov/ct2/show/NCT03260582 International Registered Report Identifier (IRRID): RR2-10.1186/s13063-018-3118-1 UR - https://www.jmir.org/2022/3/e25224 UR - http://dx.doi.org/10.2196/25224 UR - http://www.ncbi.nlm.nih.gov/pubmed/35357316 ID - info:doi/10.2196/25224 ER - TY - JOUR AU - Chen, Jinying AU - Wijesundara, G. Jessica AU - Enyim, E. Gabrielle AU - Lombardini, M. Lisa AU - Gerber, S. Ben AU - Houston, K. Thomas AU - Sadasivam, S. Rajani PY - 2022/3/7 TI - Understanding Patients? Intention to Use Digital Health Apps That Support Postdischarge Symptom Monitoring by Providers Among Patients With Acute Coronary Syndrome: Survey Study JO - JMIR Hum Factors SP - e34452 VL - 9 IS - 1 KW - coronary KW - monitor KW - elder KW - health app KW - symptom KW - eHealth KW - mobile health KW - intention KW - barrier KW - facilitator N2 - Background: After hospital discharge, patients with acute coronary syndrome (ACS) often experience symptoms that prompt them to seek acute medical attention. Early evaluation of postdischarge symptoms by health care providers may reduce unnecessary acute care utilization. However, hospital-initiated follow-up encounters are insufficient for timely detection and assessment of symptoms. While digital health tools can help address this issue, little is known about the intention to use such tools in ACS patients. Objective: This study aimed to assess ACS patients? intention to use digital health apps that support postdischarge symptom monitoring by health care providers and identify patient-perceived facilitators and barriers to app use. Methods: Using email invitations or phone calls, we recruited ACS patients discharged from a central Massachusetts health care system between December 2020 and April 2021, to participate in the study. Surveys were delivered online or via phone to individual participants. Demographics and access to technology were assessed. The intention to use a symptom monitoring app was assessed using 5-point Likert-type (from strongly agree to strongly disagree) items, such as ?If this app were available to me, I would use it.? Responses were compared across demographic subgroups and survey delivery methods. Two open-ended questions assessed perceived facilitators and barriers to app use, with responses analyzed using qualitative content analysis. Results: Among 100 respondents (response rate 8.1%), 45 (45%) completed the survey by phone. The respondents were on average 68 years old (SD 13 years), with 90% (90/100) White, 39% (39/100) women, and 88% (88/100) having access to the internet or a mobile phone. Most participants (65/100, 65%) agreed or strongly agreed that they would use the app, among which 53 (82%) would use the app as often as possible. The percentage of participants with the intention to use the app was 75% among those aged 65-74 years and dropped to 44% among those older than 75 years. The intention to use was higher in online survey respondents (vs phone survey respondents; odds ratio 3.07, 95% CI 1.20-7.88) after adjusting for age and access to technology. The analysis of open-ended questions identified the following 4 main facilitators (motivations): (1) easily reaching providers, (2) accessing or providing information, (3) quickly reaching providers, and (4) consulting providers for symptoms, and the following 4 main barriers: (1) privacy/security concerns, (2) uncomfortable using technology, (3) user-unfriendly app interface, and (4) preference for in-person/phone care. Conclusions: There was a strong intention to use a symptom monitoring app postdischarge among ACS patients. However, this intent decreased in patients older than 75 years. The survey identified barriers related to technology use, privacy/security, and the care delivery mode. Further research is warranted to determine if such intent translates into app use, and better symptom management and health care quality. UR - https://humanfactors.jmir.org/2022/1/e34452 UR - http://dx.doi.org/10.2196/34452 UR - http://www.ncbi.nlm.nih.gov/pubmed/35254269 ID - info:doi/10.2196/34452 ER - TY - JOUR AU - Chowdhury, Rajiv AU - Noh, Md Mohd Fairulnizal AU - Ismail, Rasheeqa Sophia AU - van Daalen, Robin Kim AU - Kamaruddin, Megat Puteri Sofia Nadira AU - Zulkiply, Hafizah Siti AU - Azizul, Hayati Nur AU - Khalid, Mustafa Norhayati AU - Ali, Azizan AU - Idris, Mohd Izyan AU - Mei, Shih Yong AU - Abdullah, Rifham Shazana AU - Faridus, Norfashihah AU - Yusof, Md Nur Azirah AU - Yusoff, M. Nur Najwa Farahin AU - Jamal, Rahman AU - Rahim, Abdul Aizai Azan AU - Ghapar, Abdul Abdul Kahar AU - Radhakrishnan, Kutty Ammu AU - Fong, Yip Alan Yean AU - Ismail, Omar AU - Krishinan, Saravanan AU - Lee, Yan Chuey AU - Bang, Houng Liew AU - Mageswaren, Eashwary AU - Mahendran, Kauthaman AU - Amin, Mohd Nor Hanim AU - Muthusamy, Gunavathy AU - Jin, Hean Aaron Ong AU - Ramli, Wazi Ahmad AU - Ross, Thomas Noel AU - Ruhani, Irawan Anwar AU - Yahya, Mansor AU - Yusoff, Yusniza AU - Abidin, Zainal Siti Khairani AU - Amado, Laryssa AU - Bolton, Thomas AU - Weston, Sophie AU - Crawte, Jason AU - Ovenden, Niko AU - Michielsen, Ank AU - Monower, Mostafa Md AU - Mahiyuddin, Wan Wan Rozita AU - Wood, Angela AU - Di Angelantonio, Emanuele AU - Sulaiman, Suffia Nur AU - Danesh, John AU - Butterworth, S. Adam PY - 2022/2/10 TI - Investigating Genetic and Other Determinants of First-Onset Myocardial Infarction in Malaysia: Protocol for the Malaysian Acute Vascular Events Risk Study JO - JMIR Res Protoc SP - e31885 VL - 11 IS - 2 KW - myocardial infarction KW - cardiovascular disease KW - case-control study KW - Malaysia N2 - Background: Although the burden of premature myocardial infarction (MI) is high in Malaysia, direct evidence on the determinants of MI in this multi-ethnic population remains sparse. Objective: The Malaysian Acute Vascular Events Risk (MAVERIK) study is a retrospective case-control study established to investigate the genomic, lipid-related, and other determinants of acute MI in Malaysia. In this paper, we report the study protocol and early results. Methods: By June 2019, we had enrolled approximately 2500 patients with their first MI and 2500 controls without cardiovascular disease, who were frequency-matched by age, sex, and ethnicity, from 17 hospitals in Malaysia. For each participant, serum and whole blood have been collected and stored. Clinical, demographic, and behavioral information has been obtained using a 200-item questionnaire. Results: Tobacco consumption, a history of diabetes, hypertension, markers of visceral adiposity, indicators of lower socioeconomic status, and a family history of coronary disease were more prevalent in cases than in controls. Adjusted (age and sex) logistic regression models for traditional risk factors indicated that current smoking (odds ratio [OR] 4.11, 95% CI 3.56-4.75; P<.001), previous smoking (OR 1.34, 95% CI 1.12-1.60; P=.001), a history of high blood pressure (OR 2.13, 95% CI 1.86-2.44; P<.001), a history of diabetes mellitus (OR 2.72, 95% CI 2.34-3.17; P<.001), a family history of coronary heart disease (OR 1.28, 95% CI 1.07-1.55; P=.009), and obesity (BMI >30 kg/m2; OR 1.19, 95% CI 1.05-1.34; P=.009) were associated with MI in age- and sex-adjusted models. Conclusions: The MAVERIK study can serve as a useful platform to investigate genetic and other risk factors for MI in an understudied Southeast Asian population. It should help to hasten the discovery of disease-causing pathways and inform regionally appropriate strategies that optimize public health action. International Registered Report Identifier (IRRID): RR1-10.2196/31885 UR - https://www.researchprotocols.org/2022/2/e31885 UR - http://dx.doi.org/10.2196/31885 UR - http://www.ncbi.nlm.nih.gov/pubmed/35142634 ID - info:doi/10.2196/31885 ER - TY - JOUR AU - Alamgir, Asma AU - Mousa, Osama AU - Shah, Zubair PY - 2021/12/17 TI - Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review JO - JMIR Med Inform SP - e30798 VL - 9 IS - 12 KW - artificial intelligence KW - machine learning KW - deep learning KW - cardiac arrest KW - predict N2 - Background: Cardiac arrest is a life-threatening cessation of activity in the heart. Early prediction of cardiac arrest is important, as it allows for the necessary measures to be taken to prevent or intervene during the onset. Artificial intelligence (AI) technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. Objective: This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. Methods: A scoping review was conducted in line with the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. Scopus, ScienceDirect, Embase, the Institute of Electrical and Electronics Engineers, and Google Scholar were searched to identify relevant studies. Backward reference list checks of the included studies were also conducted. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. Results: Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. Of the 47 studies, we were able to classify the approaches taken by the studies into 3 different categories: 26 (55%) studies predicted cardiac arrest by analyzing specific parameters or variables of the patients, whereas 16 (34%) studies developed an AI-based warning system. The remaining 11% (5/47) of studies focused on distinguishing patients at high risk of cardiac arrest from patients who were not at risk. Two studies focused on the pediatric population, and the rest focused on adults (45/47, 96%). Most of the studies used data sets with a size of <10,000 samples (32/47, 68%). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (38/47, 81%), and the most used algorithm was the neural network (23/47, 49%). K-fold cross-validation was the most used algorithm evaluation tool reported in the studies (24/47, 51%). Conclusions: AI is extensively used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in improving cardiac medicine. There is a need for more reviews to learn the obstacles to the implementation of AI technologies in clinical settings. Moreover, research focusing on how to best provide clinicians with support to understand, adapt, and implement this technology in their practice is also necessary. UR - https://medinform.jmir.org/2021/12/e30798 UR - http://dx.doi.org/10.2196/30798 UR - http://www.ncbi.nlm.nih.gov/pubmed/34927595 ID - info:doi/10.2196/30798 ER - TY - JOUR AU - Biersteker, Tom AU - Hilt, Alexander AU - van der Velde, Enno AU - Schalij, Jan Martin AU - Treskes, Willem Roderick PY - 2021/12/16 TI - Real-World Experience of mHealth Implementation in Clinical Practice (the Box): Design and Usability Study JO - JMIR Cardio SP - e26072 VL - 5 IS - 2 KW - eHealth KW - mHealth KW - remote patient monitoring KW - cardiology KW - patient satisfaction KW - patient empowerment KW - mobile phone N2 - Background: Mobile health (mHealth) is an emerging field of scientific interest worldwide. Potential benefits include increased patient engagement, improved clinical outcomes, and reduced health care costs. However, mHealth is often studied in projects or trials, and structural implantation in clinical practice is less common. Objective: The purpose of this paper is to outline the design of the Box and its implementation and use in an outpatient clinic setting. The impact on logistical outcomes and patient and provider satisfaction is discussed. Methods: In 2016, an mHealth care track including smartphone-compatible devices, named the Box, was implemented in the cardiology department of a tertiary medical center in the Netherlands. Patients with myocardial infarction, rhythm disorders, cardiac surgery, heart failure, and congenital heart disease received devices to measure daily weight, blood pressure, heart rate, temperature, and oxygen saturation. In addition, professional and patient user comments on the experience with the care track were obtained via structured interviews. Results: From 2016 to April 2020, a total of 1140 patients were connected to the mHealth care track. On average, a Box cost ?350 (US $375), not including extra staff costs. The median patient age was 60.8 (IQR 52.9-69.3) years, and 73.59% (839/1140) were male. A median of 260 (IQR 105-641) measurements was taken on a median of 189 (IQR 98-372) days. Patients praised the ease of use of the devices and felt more involved with their illness and care. Professionals reported more productive outpatient consultations as well as improved insight into health parameters such as blood pressure and weight. A feedback loop from the hospital to patient to focus on measurements was commented as an important improvement by both patients and professionals. Conclusions: In this study, the design and implementation of an mHealth care track for outpatient follow-up of patients with various cardiovascular diseases is described. Data from these 4 years indicate that mHealth is feasible to incorporate in outpatient management and is generally well-accepted by patients and providers. Limitations include the need for manual measurement data checks and the risk of data overload. Moreover, the tertiary care setting in which the Box was introduced may limit the external validity of logistical and financial end points to other medical centers. More evidence is needed to show the effects of mHealth on clinical outcomes and on cost-effectiveness. UR - https://cardio.jmir.org/2021/2/e26072 UR - http://dx.doi.org/10.2196/26072 UR - http://www.ncbi.nlm.nih.gov/pubmed/34642159 ID - info:doi/10.2196/26072 ER - TY - JOUR AU - Abid, Leila AU - Kammoun, Ikram AU - Ben Halima, Manel AU - Charfeddine, Salma AU - Ben Slima, Hedi AU - Drissa, Meriem AU - Mzoughi, Khadija AU - Mbarek, Dorra AU - Riahi, Leila AU - Antit, Saoussen AU - Ben Halima, Afef AU - Ouechtati, Wejdene AU - Allouche, Emna AU - Mechri, Mehdi AU - Yousfi, Chedi AU - Khorchani, Ali AU - Abid, Omar AU - Sammoud, Kais AU - Ezzaouia, Khaled AU - Gtif, Imen AU - Ouali, Sana AU - Triki, Feten AU - Hamdi, Sonia AU - Boudiche, Selim AU - Chebbi, Marwa AU - Hentati, Mouna AU - Farah, Amani AU - Triki, Habib AU - Ghardallou, Houda AU - Raddaoui, Haythem AU - Zayed, Sofien AU - Azaiez, Fares AU - Omri, Fadwa AU - Zouari, Akram AU - Ben Ali, Zine AU - Najjar, Aymen AU - Thabet, Houssem AU - Chaker, Mouna AU - Mohamed, Samar AU - Chouaieb, Marwa AU - Ben Jemaa, Abdelhamid AU - Tangour, Haythem AU - Kammoun, Yassmine AU - Bouhlel, Mahmoud AU - Azaiez, Seifeddine AU - Letaief, Rim AU - Maskhi, Salah AU - Amri, Aymen AU - Naanaa, Hela AU - Othmani, Raoudha AU - Chahbani, Iheb AU - Zargouni, Houcine AU - Abid, Syrine AU - Ayari, Mokdad AU - ben Ameur, Ines AU - Gasmi, Ali AU - ben Halima, Nejeh AU - Haouala, Habib AU - Boughzela, Essia AU - Zakhama, Lilia AU - ben Youssef, Soraya AU - Nasraoui, Wided AU - Boujnah, Rachid Mohamed AU - Barakett, Nadia AU - Kraiem, Sondes AU - Drissa, Habiba AU - Ben Khalfallah, Ali AU - Gamra, Habib AU - Kachboura, Salem AU - Bezdah, Leila AU - Baccar, Hedi AU - Milouchi, Sami AU - Sdiri, Wissem AU - Ben Omrane, Skander AU - Abdesselem, Salem AU - Kanoun, Alifa AU - Hezbri, Karima AU - Zannad, Faiez AU - Mebazaa, Alexandre AU - Kammoun, Samir AU - Mourali, Sami Mohamed AU - Addad, Faouzi PY - 2021/10/27 TI - Design and Rationale of the National Tunisian Registry of Heart Failure (NATURE-HF): Protocol for a Multicenter Registry Study JO - JMIR Res Protoc SP - e12262 VL - 10 IS - 10 KW - heart failure KW - acute heart failure KW - chronic heart failure KW - diagnosis KW - prognosis KW - treatment N2 - Background: The frequency of heart failure (HF) in Tunisia is on the rise and has now become a public health concern. This is mainly due to an aging Tunisian population (Tunisia has one of the oldest populations in Africa as well as the highest life expectancy in the continent) and an increase in coronary artery disease and hypertension. However, no extensive data are available on demographic characteristics, prognosis, and quality of care of patients with HF in Tunisia (nor in North Africa). Objective: The aim of this study was to analyze, follow, and evaluate patients with HF in a large nation-wide multicenter trial. Methods: A total of 1700 patients with HF diagnosed by the investigator will be included in the National Tunisian Registry of Heart Failure study (NATURE-HF). Patients must visit the cardiology clinic 1, 3, and 12 months after study inclusion. This follow-up is provided by the investigator. All data are collected via the DACIMA Clinical Suite web interface. Results: At the end of the study, we will note the occurrence of cardiovascular death (sudden death, coronary artery disease, refractory HF, stroke), death from any cause (cardiovascular and noncardiovascular), and the occurrence of a rehospitalization episode for an HF relapse during the follow-up period. Based on these data, we will evaluate the demographic characteristics of the study patients, the characteristics of pathological antecedents, and symptomatic and clinical features of HF. In addition, we will report the paraclinical examination findings such as the laboratory standard parameters and brain natriuretic peptides, electrocardiogram or 24-hour Holter monitoring, echocardiography, and coronarography. We will also provide a description of the therapeutic environment and therapeutic changes that occur during the 1-year follow-up of patients, adverse events following medical treatment and intervention during the 3- and 12-month follow-up, the evaluation of left ventricular ejection fraction during the 3- and 12-month follow-up, the overall rate of rehospitalization over the 1-year follow-up for an HF relapse, and the rate of rehospitalization during the first 3 months after inclusion into the study. Conclusions: The NATURE-HF study will fill a significant gap in the dynamic landscape of HF care and research. It will provide unique and necessary data on the management and outcomes of patients with HF. This study will yield the largest contemporary longitudinal cohort of patients with HF in Tunisia. Trial Registration: ClinicalTrials.gov NCT03262675; https://clinicaltrials.gov/ct2/show/NCT03262675 International Registered Report Identifier (IRRID): DERR1-10.2196/12262 UR - https://www.researchprotocols.org/2021/10/e12262 UR - http://dx.doi.org/10.2196/12262 UR - http://www.ncbi.nlm.nih.gov/pubmed/34704958 ID - info:doi/10.2196/12262 ER - TY - JOUR AU - Shin, Jeong Seo AU - Park, Jungchan AU - Lee, Seung-Hwa AU - Yang, Kwangmo AU - Park, Woong Rae PY - 2021/10/14 TI - Predictability of Mortality in Patients With Myocardial Injury After Noncardiac Surgery Based on Perioperative Factors via Machine Learning: Retrospective Study JO - JMIR Med Inform SP - e32771 VL - 9 IS - 10 KW - myocardial injury after noncardiac surgery KW - high-sensitivity cardiac troponin KW - machine learning KW - extreme gradient boosting N2 - Background: Myocardial injury after noncardiac surgery (MINS) is associated with increased postoperative mortality, but the relevant perioperative factors that contribute to the mortality of patients with MINS have not been fully evaluated. Objective: To establish a comprehensive body of knowledge relating to patients with MINS, we researched the best performing predictive model based on machine learning algorithms. Methods: Using clinical data from 7629 patients with MINS from the clinical data warehouse, we evaluated 8 machine learning algorithms for accuracy, precision, recall, F1 score, area under the receiver operating characteristic (AUROC) curve, and area under the precision-recall curve to investigate the best model for predicting mortality. Feature importance and Shapley Additive Explanations values were analyzed to explain the role of each clinical factor in patients with MINS. Results: Extreme gradient boosting outperformed the other models. The model showed an AUROC of 0.923 (95% CI 0.916-0.930). The AUROC of the model did not decrease in the test data set (0.894, 95% CI 0.86-0.922; P=.06). Antiplatelet drugs prescription, elevated C-reactive protein level, and beta blocker prescription were associated with reduced 30-day mortality. Conclusions: Predicting the mortality of patients with MINS was shown to be feasible using machine learning. By analyzing the impact of predictors, markers that should be cautiously monitored by clinicians may be identified. UR - https://medinform.jmir.org/2021/10/e32771 UR - http://dx.doi.org/10.2196/32771 UR - http://www.ncbi.nlm.nih.gov/pubmed/34647900 ID - info:doi/10.2196/32771 ER - TY - JOUR AU - Chi, Chien-Yu AU - Ao, Shuang AU - Winkler, Adrian AU - Fu, Kuan-Chun AU - Xu, Jie AU - Ho, Yi-Lwun AU - Huang, Chien-Hua AU - Soltani, Rohollah PY - 2021/9/13 TI - Predicting the Mortality and Readmission of In-Hospital Cardiac Arrest Patients With Electronic Health Records: A Machine Learning Approach JO - J Med Internet Res SP - e27798 VL - 23 IS - 9 KW - in-hospital cardiac arrest KW - 30-day mortality KW - 30-day readmission KW - machine learning KW - imbalanced dataset N2 - Background: In-hospital cardiac arrest (IHCA) is associated with high mortality and health care costs in the recovery phase. Predicting adverse outcome events, including readmission, improves the chance for appropriate interventions and reduces health care costs. However, studies related to the early prediction of adverse events of IHCA survivors are rare. Therefore, we used a deep learning model for prediction in this study. Objective: This study aimed to demonstrate that with the proper data set and learning strategies, we can predict the 30-day mortality and readmission of IHCA survivors based on their historical claims. Methods: National Health Insurance Research Database claims data, including 168,693 patients who had experienced IHCA at least once and 1,569,478 clinical records, were obtained to generate a data set for outcome prediction. We predicted the 30-day mortality/readmission after each current record (ALL-mortality/ALL-readmission) and 30-day mortality/readmission after IHCA (cardiac arrest [CA]-mortality/CA-readmission). We developed a hierarchical vectorizer (HVec) deep learning model to extract patients? information and predict mortality and readmission. To embed the textual medical concepts of the clinical records into our deep learning model, we used Text2Node to compute the distributed representations of all medical concept codes as a 128-dimensional vector. Along with the patient?s demographic information, our novel HVec model generated embedding vectors to hierarchically describe the health status at the record-level and patient-level. Multitask learning involving two main tasks and auxiliary tasks was proposed. As CA-mortality and CA-readmission were rare, person upsampling of patients with CA and weighting of CA records were used to improve prediction performance. Results: With the multitask learning setting in the model learning process, we achieved an area under the receiver operating characteristic of 0.752 for CA-mortality, 0.711 for ALL-mortality, 0.852 for CA-readmission, and 0.889 for ALL-readmission. The area under the receiver operating characteristic was improved to 0.808 for CA-mortality and 0.862 for CA-readmission after solving the extremely imbalanced issue for CA-mortality/CA-readmission by upsampling and weighting. Conclusions: This study demonstrated the potential of predicting future outcomes for IHCA survivors by machine learning. The results showed that our proposed approach could effectively alleviate data imbalance problems and train a better model for outcome prediction. UR - https://www.jmir.org/2021/9/e27798 UR - http://dx.doi.org/10.2196/27798 UR - http://www.ncbi.nlm.nih.gov/pubmed/34515639 ID - info:doi/10.2196/27798 ER - TY - JOUR AU - Han, Changho AU - Song, Youngjae AU - Lim, Hong-Seok AU - Tae, Yunwon AU - Jang, Jong-Hwan AU - Lee, Tak Byeong AU - Lee, Yeha AU - Bae, Woong AU - Yoon, Dukyong PY - 2021/9/10 TI - Automated Detection of Acute Myocardial Infarction Using Asynchronous Electrocardiogram Signals?Preview of Implementing Artificial Intelligence With Multichannel Electrocardiographs Obtained From Smartwatches: Retrospective Study JO - J Med Internet Res SP - e31129 VL - 23 IS - 9 KW - wearables KW - smartwatches KW - asynchronous electrocardiogram KW - artificial intelligence KW - deep learning KW - automatic diagnosis KW - myocardial infarction KW - timely diagnosis KW - machine learning KW - digital health KW - cardiac health KW - cardiology N2 - Background: When using a smartwatch to obtain electrocardiogram (ECG) signals from multiple leads, the device has to be placed on different parts of the body sequentially. The ECG signals measured from different leads are asynchronous. Artificial intelligence (AI) models for asynchronous ECG signals have barely been explored. Objective: We aimed to develop an AI model for detecting acute myocardial infarction using asynchronous ECGs and compare its performance with that of the automatic ECG interpretations provided by a commercial ECG analysis software. We sought to evaluate the feasibility of implementing multiple lead?based AI-enabled ECG algorithms on smartwatches. Moreover, we aimed to determine the optimal number of leads for sufficient diagnostic power. Methods: We extracted ECGs recorded within 24 hours from each visit to the emergency room of Ajou University Medical Center between June 1994 and January 2018 from patients aged 20 years or older. The ECGs were labeled on the basis of whether a diagnostic code corresponding to acute myocardial infarction was entered. We derived asynchronous ECG lead sets from standard 12-lead ECG reports and simulated a situation similar to the sequential recording of ECG leads via smartwatches. We constructed an AI model based on residual networks and self-attention mechanisms by randomly masking each lead channel during the training phase and then testing the model using various targeting lead sets with the remaining lead channels masked. Results: The performance of lead sets with 3 or more leads compared favorably with that of the automatic ECG interpretations provided by a commercial ECG analysis software, with 8.1%-13.9% gain in sensitivity when the specificity was matched. Our results indicate that multiple lead-based AI-enabled ECG algorithms can be implemented on smartwatches. Model performance generally increased as the number of leads increased (12-lead sets: area under the receiver operating characteristic curve [AUROC] 0.880; 4-lead sets: AUROC 0.858, SD 0.008; 3-lead sets: AUROC 0.845, SD 0.011; 2-lead sets: AUROC 0.813, SD 0.018; single-lead sets: AUROC 0.768, SD 0.001). Considering the short amount of time needed to measure additional leads, measuring at least 3 leads?ideally more than 4 leads?is necessary for minimizing the risk of failing to detect acute myocardial infarction occurring in a certain spatial location or direction. Conclusions: By developing an AI model for detecting acute myocardial infarction with asynchronous ECG lead sets, we demonstrated the feasibility of multiple lead-based AI-enabled ECG algorithms on smartwatches for automated diagnosis of cardiac disorders. We also demonstrated the necessity of measuring at least 3 leads for accurate detection. Our results can be used as reference for the development of other AI models using sequentially measured asynchronous ECG leads via smartwatches for detecting various cardiac disorders. UR - https://www.jmir.org/2021/9/e31129 UR - http://dx.doi.org/10.2196/31129 UR - http://www.ncbi.nlm.nih.gov/pubmed/34505839 ID - info:doi/10.2196/31129 ER - TY - JOUR AU - Bruggmann, Christel AU - Adjedj, Julien AU - Sardy, Sylvain AU - Muller, Olivier AU - Voirol, Pierre AU - Sadeghipour, Farshid PY - 2021/8/30 TI - Effects of the Interactive Web-Based Video ?Mon Coeur, Mon BASIC? on Drug Adherence of Patients With Myocardial Infarction: Randomized Controlled Trial JO - J Med Internet Res SP - e21938 VL - 23 IS - 8 KW - acute coronary syndrome KW - eHealth KW - drug adherence KW - mHealth KW - mobile phone N2 - Background: Secondary prevention strategies after acute coronary syndrome (ACS) presentation with the use of drug combinations are essential to reduce the recurrence of cardiovascular events. However, lack of drug adherence is known to be common in this population and to be related to treatment failure. To improve drug adherence, we developed the ?Mon Coeur, Mon BASIC? video. This online video has been specifically designed to inform patients about their disease and their current medications. Interactivity has been used to increase patient attention, and the video can also be viewed on smartphones and tablets. Objective: The objective of this study was to assess the long-term impact of an informative web-based video on drug adherence in patients admitted for an ACS. Methods: This randomized study was conducted with consecutive patients admitted to University Hospital of Lausanne for ACS. We randomized patients to an intervention group, which had access to the web-based video and a short interview with the pharmacist, and a control group receiving usual care. The primary outcome was the difference in drug adherence, assessed with the Adherence to Refills and Medication Scale (ARMS; 9 multiple-choice questions, scores ranging from 12 for perfect adherence to 48 for lack of adherence), between groups at 1, 3, and 6 months. We assessed the difference in ARMS score between both groups with the Wilcoxon rank sum test. Secondary outcomes were differences in knowledge, readmissions, and emergency room visits between groups and patients? satisfaction with the video. Results: Sixty patients were included at baseline. The median age of the participants was 59 years (IQR 49-69), and 85% (51/60) were male. At 1 month, 51 patients participated in the follow-up, 50 patients participated at 3 months, and 47 patients participated at 6 months. The mean ARMS scores at 1 and 6 months did not differ between the intervention and control groups (13.24 vs 13.15, 13.52 vs 13.68, respectively). At 3 months, this score was significantly lower in the intervention group than in the control group (12.54 vs 13.75; P=.03). We observed significant increases in knowledge from baseline to 1 and 3 months, but not to 6 months, in the intervention group. Readmissions and emergency room visits have been very rare, and the proportion was not different among groups. Patients in the intervention group were highly satisfied with the video. Conclusions: Despite a lower sample size than we expected to reach, we observed that the ?Mon Coeur, Mon BASIC? web-based interactive video improved patients? knowledge and seemed to have an impact on drug adherence. These results are encouraging, and the video will be offered to all patients admitted to our hospital with ACS. Trial Registration: ClinicalTrials.gov NCT03949608; https://clinicaltrials.gov/ct2/show/NCT03949608 UR - https://www.jmir.org/2021/8/e21938 UR - http://dx.doi.org/10.2196/21938 UR - http://www.ncbi.nlm.nih.gov/pubmed/34459744 ID - info:doi/10.2196/21938 ER - TY - JOUR AU - Arutyunov, P. Gregory AU - Arutyunov, G. Alexander AU - Ageev, T. Fail AU - Fofanova, V. Tatiana PY - 2021/8/30 TI - Digital Technology Tools to Examine Patient Adherence to a Prescription-Only Omega-3 Polyunsaturated Fatty Acid Therapy To Mitigate Cardiovascular Risk: Protocol for a Prospective Observational Study and Preliminary Demographic Analysis JO - JMIR Res Protoc SP - e29061 VL - 10 IS - 8 KW - omega-3-acid ethyl esters KW - myocardial infarction KW - hypertriglyceridemia KW - adherence KW - compliance KW - persistence KW - mHealth KW - eHealth KW - patient-reported outcomes N2 - Background: Sustained adherence and persistence with prescription medications is considered essential to achieve maximal treatment benefit for patients with major chronic, noncommunicable diseases such as hyperlipidemia and lipid-associated cardiovascular disease. It is widely documented, however, that many patients with these conditions have poor long-term adherence to their treatments. The population of Russia is affected by poor adherence in the same ways as populations elsewhere and continues to have high rates of cardiovascular disease. Objective: The purpose of this study was to examine patient adherence to a prescription-only preparation of highly purified omega-3 polyunsaturated fatty acids (1.2 to 1 eicosapentaenoic acid to docosahexaenoic ratio, 90% purity) in a large sample of patients at risk for cardiovascular diseases using digital technology to monitor patient behavior and as an outreach facility for patient education and engagement. Methods: We conducted a 6-month prospective observational study (DIAPAsOn) at >100 centers in the Russian Federation. A bespoke electronic data capture and patient engagement system were developed with a well-established Russian technology supplier that enables information obtained during clinic visits to be supplemented by remote patient self-reporting. Other aspects of the program included raising patients' awareness about their condition via educational materials available in personal patient accounts in the electronic system. Results: From an initial cohort of 3000 patients, a safety population of 2572 patients (age: mean 60 years) with an equal proportion of men and women has been characterized. There was widespread concomitant cardiovascular pathology and commensurate use of multiple classes of cardiovascular medication, notably lipid-modifying and antihypertensive drugs. The program was completed by 1975 patients, of whom 780 were prescribed highly purified omega-3 polyunsaturated fatty acid supplements for secondary prevention after myocardial infarction and 1195 were prescribed highly purified omega-3 polyunsaturated fatty acid supplements for hypertriglyceridemia. Data collection and analysis have been completed. Conclusions: DIAPAsOn will provide insights into patient adherence with prescription-grade omega-3 polyunsaturated fatty acid therapy and perspectives on the role of mobile technology in monitoring and encouraging adherence to therapy. UR - https://www.researchprotocols.org/2021/8/e29061 UR - http://dx.doi.org/10.2196/29061 UR - http://www.ncbi.nlm.nih.gov/pubmed/34459746 ID - info:doi/10.2196/29061 ER - TY - JOUR AU - Humphries, Monica Sophia AU - Wallert, John AU - Norlund, Fredrika AU - Wallin, Emma AU - Burell, Gunilla AU - von Essen, Louise AU - Held, Claes AU - Olsson, Gustaf Erik Martin PY - 2021/5/24 TI - Internet-Based Cognitive Behavioral Therapy for Patients Reporting Symptoms of Anxiety and Depression After Myocardial Infarction: U-CARE Heart Randomized Controlled Trial Twelve-Month Follow-up JO - J Med Internet Res SP - e25465 VL - 23 IS - 5 KW - myocardial infarction KW - iCBT KW - psychological treatment KW - cardiovascular health KW - cognitive behavior therapy KW - internet KW - cardiovascular KW - infarction KW - treatment KW - anxiety KW - depression N2 - Background: The U-CARE Heart trial was one of the first randomized controlled trials to evaluate the effect of internet-based cognitive behavioral therapy on self-reported symptoms of anxiety or depression for patients with a recent myocardial infarction. While the effects of internet-based cognitive behavioral therapy on Hospital Anxiety and Depression Scale (HADS) scores at 14 weeks postbaseline were not significant, in this study, we investigated possible long-term effects of treatment. Objective: The aim of this study was to evaluate the long-term effectiveness of internet-based cognitive behavioral therapy on self-reported symptoms of anxiety and depression in patients 12 months after a myocardial infarction and to explore subsequent occurrences of cardiovascular disease events. Methods: Shortly after acute myocardial infarction, 239 patients (33% female, mean age 59.6 years) reporting mild-to-moderate symptoms of anxiety or depression were randomized to 14 weeks of therapist-guided internet-based cognitive behavioral therapy (n=117) or treatment as usual (n=122). Data from national registries were used to explore group differences in clinical outcomes such as cardiovascular disease and cardiovascular-related mortality for a follow-up period of up to 5 years: group differences in HADS total score 1 year post?myocardial infarction, the primary outcome, was analyzed using multiple linear regression. Secondary outcomes, such as HADS anxiety and depression subscales and the Cardiac Anxiety Questionnaire total score (CAQ), which measures heart-focused anxiety, were analyzed in the same way. Multiple imputation was used to account for missing data, and a pooled treatment effect was estimated. Adjusted Cox proportional hazards models were used to estimate hazard ratios (HRs) for data pertaining to registry outcomes. Results: Both groups reported lower HADS total scores 1 year after myocardial infarction than those at baseline. HADS total scores were not significantly different between the treatment and control groups 1 year after myocardial infarction (?=?1.14, 95% CI ?2.73 to 0.45, P=.16). CAQ was the only measure improved significantly by internet-based cognitive behavioral therapy when compared with treatment as usual (?=?2.58, 95% CI ?4.75 to ?0.42, P=.02) before adjusting for multiple comparisons. The composite outcome of nonfatal cardiovascular events and cardiovascular-related mortality did not differ between groups but was numerically higher in the internet-based cognitive behavioral therapy group, who were at slightly greater risk (HR 1.8, 95% CI 0.96 to 3.4, P=.07). Adjusting for previous myocardial infarction and diabetes attenuated this estimate (HR 1.5, 95% CI 0.8 to 2.8, P=.25). Conclusions: Internet-based cognitive behavioral therapy was not superior in reducing self-reported symptoms of depression or anxiety compared to treatment as usual at the 1-year follow-up after myocardial infarction. A reduction in cardiac-related anxiety was observed but was not significant after adjusting for multiple comparisons. There was no difference in risk of cardiovascular events between the treatment groups. Low treatment adherence, which might have affected treatment engagement and outcomes, should be considered when interpreting these results. Trial Registration: ClinicalTrials.gov NCT01504191; https://clinicaltrials.gov/ct2/show/NCT01504191 International Registered Report Identifier (IRRID): RR2-10.1186/s13063-015-0689-y UR - https://www.jmir.org/2021/5/e25465 UR - http://dx.doi.org/10.2196/25465 UR - http://www.ncbi.nlm.nih.gov/pubmed/34028358 ID - info:doi/10.2196/25465 ER - TY - JOUR AU - Fraticelli, Laurie AU - Freyssenge, Julie AU - Claustre, Clément AU - Martinez, Mikaël AU - Redjaline, Abdesslam AU - Serre, Patrice AU - Bochaton, Thomas AU - El Khoury, Carlos PY - 2021/4/27 TI - Estimating the Proportion of COVID-19 Contacts Among Households Based on Individuals With Myocardial Infarction History: Cross-sectional Telephone Survey JO - JMIR Form Res SP - e26955 VL - 5 IS - 4 KW - COVID-19 KW - survey KW - myocardial infarction KW - cases KW - contacts KW - household KW - estimate KW - cross-sectional KW - cardiovascular KW - risk KW - symptom N2 - Background: Adults with cardiovascular diseases were disproportionately associated with an increased risk of a severe form of COVID-19 and all-cause mortality. Objective: The aims of this study are to report the associated symptoms for COVID-19 cases, to estimate the proportion of contacts, and to describe the clinical signs and behaviors among individuals with and without myocardial infarction history among cases and contacts. Methods: A 2-week cross-sectional telephone survey was conducted during the first lockdown period in France, from May 4 to 15, 2020. A total of 668 households participated, representing 703 individuals with pre-existing cardiovascular disease in the past 2 years and 849 individuals without myocardial infarction history. Results: High rates of compliance with health measures were self-reported, regardless of age or risk factors. There were 4 confirmed COVID-19 cases that were registered from 4 different households. Based on deductive assumptions of the 1552 individuals, 9.73% (n=151) were identified as contacts, of whom 71.52% (108/151) were asymptomatic. Among individuals with a myocardial infarction history, 2 were COVID-19 cases, and the estimated proportion of contacts was 8.68% (61/703), of whom 68.85% (42/61) were asymptomatic. The cases and contacts presented different symptoms, with more respiratory signs in those with a myocardial infarction history. Conclusions: The telephone survey could be a relevant tool for reporting the number of contacts during a limited period and in a limited territory based on the presence of associated symptoms and COVID-19 cases in the households. This study advanced our knowledge to better prepare for future crises. UR - https://formative.jmir.org/2021/4/e26955 UR - http://dx.doi.org/10.2196/26955 UR - http://www.ncbi.nlm.nih.gov/pubmed/33855968 ID - info:doi/10.2196/26955 ER - TY - JOUR AU - Iftikhar, Aleeha AU - Bond, Raymond AU - Mcgilligan, Victoria AU - Leslie, J. Stephen AU - Knoery, Charles AU - Shand, James AU - Ramsewak, Adesh AU - Sharma, Divyesh AU - McShane, Anne AU - Rjoob, Khaled AU - Peace, Aaron PY - 2021/3/2 TI - Human?Computer Agreement of Electrocardiogram Interpretation for Patients Referred to and Declined for Primary Percutaneous Coronary Intervention: Retrospective Data Analysis Study JO - JMIR Med Inform SP - e24188 VL - 9 IS - 3 KW - ECG interpretation KW - agreement between human and computer KW - primary percutaneous coronary intervention service KW - acute myocardial infarction KW - scan KW - electrocardiogram KW - heart KW - intervention KW - infarction KW - human-computer KW - diagnostic N2 - Background: When a patient is suspected of having an acute myocardial infarction, they are accepted or declined for primary percutaneous coronary intervention partly based on clinical assessment of their 12-lead electrocardiogram (ECG) and ST-elevation myocardial infarction criteria. Objective: We retrospectively determined the agreement rate between human (specialists called activator nurses) and computer interpretations of ECGs of patients who were declined for primary percutaneous coronary intervention. Methods: Various features of patients who were referred for primary percutaneous coronary intervention were analyzed. Both the human and computer ECG interpretations were simplified to either ?suggesting? or ?not suggesting? acute myocardial infarction to avoid analysis of complex heterogeneous and synonymous diagnostic terms. Analyses, to measure agreement, and logistic regression, to determine if these ECG interpretations (and other variables such as patient age, chest pain) could predict patient mortality, were carried out. Results: Of a total of 1464 patients referred to and declined for primary percutaneous coronary intervention, 722 (49.3%) computer diagnoses suggested acute myocardial infarction, whereas 634 (43.3%) of the human interpretations suggested acute myocardial infarction (P<.001). The human and computer agreed that there was a possible acute myocardial infarction for 342 out of 1464 (23.3%) patients. However, there was a higher rate of human?computer agreement for patients not having acute myocardial infarctions (450/1464, 30.7%). The overall agreement rate was 54.1% (792/1464). Cohen ? showed poor agreement (?=0.08, P=.001). Only the age (odds ratio [OR] 1.07, 95% CI 1.05-1.09) and chest pain (OR 0.59, 95% CI 0.39-0.89) independent variables were statistically significant (P=.008) in predicting mortality after 30 days and 1 year. The odds for mortality within 1 year of referral were lower in patients with chest pain compared to those patients without chest pain. A referral being out of hours was a trending variable (OR 1.41, 95% CI 0.95-2.11, P=.09) for predicting the odds of 1-year mortality. Conclusions: Mortality in patients who were declined for primary percutaneous coronary intervention was higher than the reported mortality for ST-elevation myocardial infarction patients at 1 year. Agreement between computerized and human ECG interpretation is poor, perhaps leading to a high rate of inappropriate referrals. Work is needed to improve computer and human decision making when reading ECGs to ensure that patients are referred to the correct treatment facility for time-critical therapy. UR - https://medinform.jmir.org/2021/3/e24188 UR - http://dx.doi.org/10.2196/24188 UR - http://www.ncbi.nlm.nih.gov/pubmed/33650984 ID - info:doi/10.2196/24188 ER - TY - JOUR AU - Bhalodiya, Maganbhai Jayendra AU - Palit, Arnab AU - Giblin, Gerard AU - Tiwari, Kumar Manoj AU - Prasad, K. Sanjay AU - Bhudia, K. Sunil AU - Arvanitis, N. Theodoros AU - Williams, A. Mark PY - 2021/2/10 TI - Identifying Myocardial Infarction Using Hierarchical Template Matching?Based Myocardial Strain: Algorithm Development and Usability Study JO - JMIR Med Inform SP - e22164 VL - 9 IS - 2 KW - left ventricle KW - myocardial infarction KW - myocardium KW - strain N2 - Background: Myocardial infarction (MI; location and extent of infarction) can be determined by late enhancement cardiac magnetic resonance (CMR) imaging, which requires the injection of a potentially harmful gadolinium-based contrast agent (GBCA). Alternatively, emerging research in the area of myocardial strain has shown potential to identify MI using strain values. Objective: This study aims to identify the location of MI by developing an applied algorithmic method of circumferential strain (CS) values, which are derived through a novel hierarchical template matching (HTM) method. Methods: HTM-based CS H-spread from end-diastole to end-systole was used to develop an applied method. Grid-tagging magnetic resonance imaging was used to calculate strain values in the left ventricular (LV) myocardium, followed by the 16-segment American Heart Association model. The data set was used with k-fold cross-validation to estimate the percentage reduction of H-spread among infarcted and noninfarcted LV segments. A total of 43 participants (38 MI and 5 healthy) who underwent CMR imaging were retrospectively selected. Infarcted segments detected by using this method were validated by comparison with late enhancement CMR, and the diagnostic performance of the applied algorithmic method was evaluated with a receiver operating characteristic curve test. Results: The H-spread of the CS was reduced in infarcted segments compared with noninfarcted segments of the LV. The reductions were 30% in basal segments, 30% in midventricular segments, and 20% in apical LV segments. The diagnostic accuracy of detection, using the reported method, was represented by area under the curve values, which were 0.85, 0.82, and 0.87 for basal, midventricular, and apical slices, respectively, demonstrating good agreement with the late-gadolinium enhancement?based detections. Conclusions: The proposed applied algorithmic method has the potential to accurately identify the location of infarcted LV segments without the administration of late-gadolinium enhancement. Such an approach adds the potential to safely identify MI, potentially reduce patient scanning time, and extend the utility of CMR in patients who are contraindicated for the use of GBCA. UR - https://medinform.jmir.org/2021/2/e22164 UR - http://dx.doi.org/10.2196/22164 UR - http://www.ncbi.nlm.nih.gov/pubmed/33565992 ID - info:doi/10.2196/22164 ER - TY - JOUR AU - Nozari, Younes AU - Geraiely, Babak AU - Alipasandi, Kian AU - Mortazavi, Hamideh Seyedeh AU - Omidi, Negar AU - Aghajani, Hassan AU - Amirzadegan, Alireza AU - Pourhoseini, Hamidreza AU - Salarifar, Mojtaba AU - Alidoosti, Mohammad AU - Haji-Zeinali, Ali-Mohammad AU - Nematipour, Ebrahim AU - Nomali, Mahin PY - 2020/12/16 TI - Time to Treatment and In-Hospital Major Adverse Cardiac Events Among Patients With ST-Segment Elevation Myocardial Infarction Who Underwent Primary Percutaneous Coronary Intervention (PCI) According to the 24/7 Primary PCI Service Registry in Iran: Cross-Sectional Study JO - Interact J Med Res SP - e20352 VL - 9 IS - 4 KW - ST-segment elevation myocardial infarction KW - time to treatment KW - percutaneous coronary intervention KW - registries KW - Iran N2 - Background: Performing primary percutaneous coronary intervention (PCI) as a preferred reperfusion strategy for patients with ST-segment elevation myocardial infarction (STEMI) may be associated with major adverse cardiocerebrovascular events (MACCEs). Thus, timely primary PCI has been emphasized in order to improve outcomes. Despite guideline recommendations on trying to reduce the door-to-balloon time to <90 minutes in order to reduce mortality, less attention has been paid to other components of time to treatment, such as the symptom-to-balloon time, as an indicator of the total ischemic time, which includes the symptom-to-door time and door-to-balloon time, in terms of clinical outcomes of patients with STEMI undergoing primary PCI. Objective: We aimed to determine the association between each component of time to treatment (ie, symptom-to-door time, door-to-balloon time, and symptom-to-balloon time) and in-hospital MACCEs among patients with STEMI who underwent primary PCI. Methods: In this observational study, according to a prospective primary PCI 24/7 service registry, adult patients with STEMI who underwent primary PCI in one of six catheterization laboratories of Tehran Heart Center from November 2015 to August 2019, were studied. The primary outcome was in-hospital MACCEs, which was a composite index consisting of cardiac death, revascularization (ie, target vessel revascularization/target lesion revascularization), myocardial infarction, and stroke. It was compared at different levels of time to treatment (ie, symptom-to-door and door-to-balloon time <90 and ?90 minutes, and symptom-to-balloon time <180 and ?180 minutes). Data were analyzed using SPSS software version 24 (IBM Corp), with descriptive statistics, such as frequency, percentage, mean, and standard deviation, and statistical tests, such as chi-square test, t test, and univariate and multivariate logistic regression analyses, and with a significance level of <.05 and 95% CIs for odds ratios (ORs). Results: Data from 2823 out of 3204 patients were analyzed (mean age of 59.6 years, SD 11.6 years; 79.5% male [n=2243]; completion rate: 88.1%). Low proportions of symptom-to-door time ?90 minutes and symptom-to-balloon time ?180 minutes were observed among the study patients (579/2823, 20.5% and 691/2823, 24.5%, respectively). Overall, 2.4% (69/2823) of the patients experienced in-hospital MACCEs, and cardiac death (45/2823, 1.6%) was the most common cardiac outcome. In the univariate analysis, the symptom-to-balloon time predicted in-hospital MACCEs (OR 2.2, 95% CI 1.1-4.4; P=.03), while the symptom-to-door time (OR 1.4, 95% CI 0.7-2.6; P=.34) and door-to-balloon time (OR 1.1, 95% CI 0.6-1.8, P=.77) were not associated with in-hospital MACCEs. In the multivariate analysis, only symptom-to-balloon time ?180 minutes was associated with in-hospital MACCEs and was a predictor of in-hospital MACCEs (OR 2.3, 95% CI 1.1-5.2; P=.04). Conclusions: A longer symptom-to-balloon time was the only component associated with higher in-hospital MACCEs in the present study. Efforts should be made to shorten the symptom-to-balloon time in order to improve in-hospital MACCEs. International Registered Report Identifier (IRRID): RR2-10.2196/13161 UR - http://www.i-jmr.org/2020/4/e20352/ UR - http://dx.doi.org/10.2196/20352 UR - http://www.ncbi.nlm.nih.gov/pubmed/33325826 ID - info:doi/10.2196/20352 ER - TY - JOUR AU - Schwalm, D. J. AU - Ivers, M. Noah AU - Bouck, Zachary AU - Taljaard, Monica AU - Natarajan, K. Madhu AU - Dolovich, Lisa AU - Thavorn, Kednapa AU - McCready, Tara AU - O'Brien, Erin AU - Grimshaw, M. Jeremy PY - 2020/11/4 TI - Length of Initial Prescription at Hospital Discharge and Long-Term Medication Adherence for Elderly, Post-Myocardial Infarction Patients: Protocol for an Interrupted Time Series Study JO - JMIR Res Protoc SP - e18981 VL - 9 IS - 11 KW - post-myocardial infarction KW - adherence KW - standardized discharge prescription form KW - secondary prevention KW - policy change KW - medication KW - elderly KW - intervention KW - prescription KW - discharge KW - prevention KW - cardiology KW - heart N2 - Background: Based on high-quality evidence, guidelines recommend the long-term use of secondary prevention medications post-myocardial infarction (MI) to avoid recurrent cardiovascular events and death. Unfortunately, discontinuation of recommended medications post-MI is common. Observational evidence suggests that prescriptions covering a longer duration at discharge from hospital are associated with greater long-term medication adherence. The following is a proposal for the first interventional study to evaluate the impact of longer prescription duration at discharge post-MI on long-term medication adherence. Objective: The overarching goal of this study is to reduce morbidity and mortality among post-MI patients through improved long-term cardiac medication adherence. The specific objectives include the following. First, we will assess whether long-term cardiac medication adherence improves among elderly, post-MI patients following the implementation of (1) standardized discharge prescription forms with 90-day prescriptions and 3 repeats for recommended cardiac medication classes, in combination with education and (2) education alone compared to (3) usual care. Second, we will assess the cost implications of prolonged initial discharge prescriptions compared with usual care. Third, we will compare clinical outcomes between longer (>60 days) versus shorter prescription durations. Fourth, we will collect baseline information to inform a multicenter interventional study. Methods: We will conduct a quasiexperimental, interrupted time series design to evaluate the impact of a multifaceted intervention to implement longer duration prescriptions versus usual care on long-term cardiac medication adherence among post-MI patients. Intervention groups and their corresponding settings include: (1) intervention group 1: 1 cardiac center and 1 noncardiac hospital allocated to receive standardized discharge prescription forms supporting the dispensation of 90 days? worth of cardiac medications with 3 repeats, coupled with education; (2) intervention group 2: 4 sites (including 1 cardiac center) allocated to receive education only; and (3) control group: all remaining hospitals within the province that did not receive an intervention (ie, usual care). Administrative databases will be used to measure all outcomes. Adherence to 4 classes of cardiac medications ? statins, beta blockers, angiotensin system inhibitors, and secondary antiplatelets (ie, prasugrel, clopidogrel, or ticagrelor) ? will be assessed. Results: Enrollment began in September 2017, and results are expected to be analyzed in late 2020. Conclusions: The results have the potential to redefine best practices regarding discharge prescribing policies for patients post-MI. A policy of standardized maximum-duration prescriptions at the time of discharge post-MI is a simple intervention that has the potential to significantly improve long-term medication adherence, thus decreasing cardiac morbidity and mortality. If effective, this low-cost intervention to implement longer duration prescriptions post-MI could be easily scaled. Trial Registration: ClinicalTrials.gov NCT03257579; https://clinicaltrials.gov/ct2/show/NCT03257579 International Registered Report Identifier (IRRID): DERR1-10.2196/18981 UR - https://www.researchprotocols.org/2020/11/e18981 UR - http://dx.doi.org/10.2196/18981 UR - http://www.ncbi.nlm.nih.gov/pubmed/33146624 ID - info:doi/10.2196/18981 ER - TY - JOUR AU - Humphries, Monica Sophia AU - Rondung, Elisabet AU - Norlund, Fredrika AU - Sundin, Örjan AU - Tornvall, Per AU - Held, Claes AU - Spaak, Jonas AU - Lyngå, Patrik AU - Olsson, G. Erik M. PY - 2020/9/17 TI - Designing a Web-Based Psychological Intervention for Patients With Myocardial Infarction With Nonobstructive Coronary Arteries: User-Centered Design Approach JO - J Med Internet Res SP - e19066 VL - 22 IS - 9 KW - web-based intervention KW - iCBT KW - myocardial infarction KW - nonobstructive coronary arteries KW - patient involvement KW - psychological treatment KW - MINOCA KW - takotsubo cardiomyopathy N2 - Background: The involvement of patient research partners (PRPs) in research aims to safeguard the needs of patient groups and produce new interventions that are developed based on patient input. Myocardial infarction with nonobstructive coronary arteries (MINOCA), unlike acute myocardial infarction (MI) with obstructive coronary arteries, is presented with no significant obstructive coronary artery disease. Patients with this diagnosis are a subset of those diagnosed with traditional MI and often need more psychological support, something that is presently not established in the current treatment scheme in Swedish health care or elsewhere, to our knowledge. An internet-delivered intervention might offer patients with MINOCA the opportunity to access a psychological treatment that is tailored to their specific needs after MINOCA and could therefore supplement the existing medical care in an easily accessible format. Objective: This paper aims to describe the development of a therapist-guided, internet-delivered psychological intervention designed specifically for patients with MINOCA. Methods: The study used a participatory design that involved 7 PRPs diagnosed with MINOCA who collaborated with a team consisting of researchers, cardiologists, and psychologists. Intervention content was developed iteratively and presented to the PRPs across several prototypes, each continually adjusted and redesigned according to the feedback received. The intervention and experience of it were discussed by PRPs in a final meeting and then presented to a panel of 2 clinical psychologists and a cardiologist for further input. Results: The outcome of the collaboration between PRPs and the research group produced a web-based psychological 9-step program focusing on stress, worry, and valued action. The input from PRPs contributed substantially to the therapy content, homework tasks, interactive activities, multimedia, and design presentation. Conclusions: Working with PRPs to develop an intervention for people with MINOCA produced a web-based intervention that can be further evaluated with the goal of offering a new psychological treatment option to a patient group currently without one. Direct contribution from PRPs enabled us to obtain relevant, insightful, and valuable feedback that was put towards the overall design and content of the intervention. UR - http://www.jmir.org/2020/9/e19066/ UR - http://dx.doi.org/10.2196/19066 UR - http://www.ncbi.nlm.nih.gov/pubmed/32940615 ID - info:doi/10.2196/19066 ER - TY - JOUR AU - Hong, Sungjun AU - Lee, Sungjoo AU - Lee, Jeonghoon AU - Cha, Chul Won AU - Kim, Kyunga PY - 2020/8/4 TI - Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study JO - JMIR Med Inform SP - e15932 VL - 8 IS - 8 KW - machine learning KW - cardiac arrest prediction KW - emergency department KW - sequential characteristics KW - clinical validity N2 - Background: The development and application of clinical prediction models using machine learning in clinical decision support systems is attracting increasing attention. Objective: The aims of this study were to develop a prediction model for cardiac arrest in the emergency department (ED) using machine learning and sequential characteristics and to validate its clinical usefulness. Methods: This retrospective study was conducted with ED patients at a tertiary academic hospital who suffered cardiac arrest. To resolve the class imbalance problem, sampling was performed using propensity score matching. The data set was chronologically allocated to a development cohort (years 2013 to 2016) and a validation cohort (year 2017). We trained three machine learning algorithms with repeated 10-fold cross-validation. Results: The main performance parameters were the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). The random forest algorithm (AUROC 0.97; AUPRC 0.86) outperformed the recurrent neural network (AUROC 0.95; AUPRC 0.82) and the logistic regression algorithm (AUROC 0.92; AUPRC=0.72). The performance of the model was maintained over time, with the AUROC remaining at least 80% across the monitored time points during the 24 hours before event occurrence. Conclusions: We developed a prediction model of cardiac arrest in the ED using machine learning and sequential characteristics. The model was validated for clinical usefulness by chronological visualization focused on clinical usability. UR - http://medinform.jmir.org/2020/8/e15932/ UR - http://dx.doi.org/10.2196/15932 UR - http://www.ncbi.nlm.nih.gov/pubmed/32749227 ID - info:doi/10.2196/15932 ER - TY - JOUR AU - Kim, Junetae AU - Park, Rang Yu AU - Lee, Hoon Jeong AU - Lee, Jae-Ho AU - Kim, Young-Hak AU - Huh, Won Jin PY - 2020/3/18 TI - Development of a Real-Time Risk Prediction Model for In-Hospital Cardiac Arrest in Critically Ill Patients Using Deep Learning: Retrospective Study JO - JMIR Med Inform SP - e16349 VL - 8 IS - 3 KW - deep learning KW - cardiac arrest KW - Weibull distribution KW - forecasting KW - intensive care units KW - gated recurrent unit N2 - Background: Cardiac arrest is the most serious death-related event in intensive care units (ICUs), but it is not easily predicted because of the complex and time-dependent data characteristics of intensive care patients. Given the complexity and time dependence of ICU data, deep learning?based methods are expected to provide a good foundation for developing risk prediction models based on large clinical records. Objective: This study aimed to implement a deep learning model that estimates the distribution of cardiac arrest risk probability over time based on clinical data and assesses its potential. Methods: A retrospective study of 759 ICU patients was conducted between January 2013 and July 2015. A character-level gated recurrent unit with a Weibull distribution algorithm was used to develop a real-time prediction model. Fivefold cross-validation testing (training set: 80% and validation set: 20%) determined the consistency of model accuracy. The time-dependent area under the curve (TAUC) was analyzed based on the aggregation of 5 validation sets. Results: The TAUCs of the implemented model were 0.963, 0.942, 0.917, 0.875, 0.850, 0.842, and 0.761 before cardiac arrest at 1, 8, 16, 24, 32, 40, and 48 hours, respectively. The sensitivity was between 0.846 and 0.909, and specificity was between 0.923 and 0.946. The distribution of risk between the cardiac arrest group and the non?cardiac arrest group was generally different, and the difference rapidly increased as the time left until cardiac arrest reduced. Conclusions: A deep learning model for forecasting cardiac arrest was implemented and tested by considering the cumulative and fluctuating effects of time-dependent clinical data gathered from a large medical center. This real-time prediction model is expected to improve patient?s care by allowing early intervention in patients at high risk of unexpected cardiac arrests. UR - http://medinform.jmir.org/2020/3/e16349/ UR - http://dx.doi.org/10.2196/16349 UR - http://www.ncbi.nlm.nih.gov/pubmed/32186517 ID - info:doi/10.2196/16349 ER - TY - JOUR AU - Jiang, Xinchan AU - Ming, Wai-Kit AU - You, HS Joyce PY - 2019/06/17 TI - The Cost-Effectiveness of Digital Health Interventions on the Management of Cardiovascular Diseases: Systematic Review JO - J Med Internet Res SP - e13166 VL - 21 IS - 6 KW - telemedicine KW - cardiovascular diseases KW - stroke KW - heart failure KW - myocardial infarction KW - heart attack KW - cost-effectiveness KW - medical economics KW - decision modeling KW - systematic review N2 - Background: With the advancement in information technology and mobile internet, digital health interventions (DHIs) are improving the care of cardiovascular diseases (CVDs). The impact of DHIs on cost-effective management of CVDs has been examined using the decision analytic model?based health technology assessment approach. Objective: The aim of this study was to perform a systematic review of the decision analytic model?based studies evaluating the cost-effectiveness of DHIs on the management of CVDs. Methods: A literature review was conducted in Medline, Embase, Cumulative Index to Nursing and Allied Health Literature Complete, PsycINFO, Scopus, Web of Science, Center for Review and Dissemination, and Institute for IEEE Xplore between 2001 and 2018. Studies were included if the following criteria were met: (1) English articles, (2) DHIs that promoted or delivered clinical interventions and had an impact on patients? cardiovascular conditions, (3) studies that were modeling works with health economic outcomes of DHIs for CVDs, (4) studies that had a comparative group for assessment, and (5) full economic evaluations including a cost-effectiveness analysis, cost-utility analysis, cost-benefit analysis, and cost-consequence analysis. The primary outcome collected was the cost-effectiveness of the DHIs, presented by incremental cost per additional quality-adjusted life year (QALY). The quality of each included study was evaluated using the Consolidated Health Economic Evaluation Reporting Standards. Results: A total of 14 studies met the defined criteria and were included in the review. Among the included studies, heart failure (7/14, 50%) and stroke (4/14, 29%) were two of the most frequent CVDs that were managed by DHIs. A total of 9 (64%) studies were published between 2015 and 2018 and 5 (36%) published between 2011 and 2014. The time horizon was ?1 year in 3 studies (21%), >1 year in 10 studies (71%), and 1 study (7%) did not declare the time frame. The types of devices or technologies used to deliver the health interventions were short message service (1/14, 7%), telephone support (1/14, 7%), mobile app (1/14, 7%), video conferencing system (5/14, 36%), digital transmission of physiologic data (telemonitoring; 5/14, 36%), and wearable medical device (1/14, 7%). The DHIs gained higher QALYs with cost saving in 43% (6/14) of studies and gained QALYs at a higher cost at acceptable incremental cost-effectiveness ratio (ICER) in 57% (8/14) of studies. The studies were classified as excellent (0/14, 0%), good (9/14, 64%), moderate (4/14, 29%), and low (1/14, 7%) quality. Conclusions: This study is the first systematic review of decision analytic model?based cost-effectiveness analyses of DHIs in the management of CVDs. Most of the identified studies were published recently, and the majority of the studies were good quality cost-effectiveness analyses with an adequate duration of time frame. All the included studies found the DHIs to be cost-effective. UR - http://www.jmir.org/2019/6/e13166/ UR - http://dx.doi.org/10.2196/13166 UR - http://www.ncbi.nlm.nih.gov/pubmed/31210136 ID - info:doi/10.2196/13166 ER - TY - JOUR AU - Subki, Hussein Ahmed AU - Mortada, Hisham Hatan AU - Alsallum, Saad Mohammed AU - Alattas, Taleb Ali AU - Almalki, Ali Mohammed AU - Hindi, Mohammed Muhab AU - Subki, Hussein Siham AU - Alhejily, Awad Wesam PY - 2018/11/28 TI - Basic Life Support Knowledge Among a Nonmedical Population in Jeddah, Saudi Arabia: Cross-Sectional Study JO - Interact J Med Res SP - e10428 VL - 7 IS - 2 KW - basic life support KW - BLS KW - cardiopulmonary resuscitation KW - CPR KW - awareness KW - public KW - knowledge KW - Jeddah KW - Saudi Arabia N2 - Background: Providing basic life support (BLS) at the site of an accident is crucial to increase the survival rates of the injured people. It is especially relevant when health care is far away. Objective: The aim of our study is to assess the BLS knowledge level of the Saudi Arabian population and identify influencing factors associated with level of knowledge about BLS. Methods: Our study is a cross-sectional descriptive study, which was conducted using a self-administered online questionnaire derived from the BLS practice test. The Saudi population was the target population. The questionnaire was divided into two parts: one contained demographic data and the second part contained questions to test the population?s perception about how to perform BLS techniques properly. The data were collected between July and August 2017. Statistically significant differences were defined as those with a P value <.05, and a score of five or more was considered a passing score on the second part. We used SPSS version 21 for data analysis. Results: Our study included 301 participants. Our participants? BLS online exam scores ranged from 0 to 10, with a mean of 4.1 (SD 1.7). Only 39.2% (118/301) of the participants passed the test. The percentage of bachelor?s degree or higher holders constituted 60.1% (181/301) of the study population. In addition, higher income was significantly associated with higher scores on the test (P=.04). Conclusions: This study demonstrated that the theoretical knowledge level of BLS among the general population in Jeddah was below average. There is a critical need to increase the public?s exposure to BLS education through raising awareness campaigns and government-funded training programs that aim to curb the incidence of out-of-hospital cardiac arrest mortalities in the Saudi community. UR - http://www.i-jmr.org/2018/2/e10428/ UR - http://dx.doi.org/10.2196/10428 UR - http://www.ncbi.nlm.nih.gov/pubmed/30487122 ID - info:doi/10.2196/10428 ER - TY - JOUR AU - Wallert, John AU - Gustafson, Emelie AU - Held, Claes AU - Madison, Guy AU - Norlund, Fredrika AU - von Essen, Louise AU - Olsson, Gustaf Erik Martin PY - 2018/10/10 TI - Predicting Adherence to Internet-Delivered Psychotherapy for Symptoms of Depression and Anxiety After Myocardial Infarction: Machine Learning Insights From the U-CARE Heart Randomized Controlled Trial JO - J Med Internet Res SP - e10754 VL - 20 IS - 10 KW - applied predictive modeling KW - cardiac rehabilitation KW - linguistics KW - supervised machine learning KW - recursive feature elimination KW - treatment adherence and compliance KW - Web-based interventions N2 - Background: Low adherence to recommended treatments is a multifactorial problem for patients in rehabilitation after myocardial infarction (MI). In a nationwide trial of internet-delivered cognitive behavior therapy (iCBT) for the high-risk subgroup of patients with MI also reporting symptoms of anxiety, depression, or both (MI-ANXDEP), adherence was low. Since low adherence to psychotherapy leads to a waste of therapeutic resources and risky treatment abortion in MI-ANXDEP patients, identifying early predictors for adherence is potentially valuable for effective targeted care. Objectives: The goal of the research was to use supervised machine learning to investigate both established and novel predictors for iCBT adherence in MI-ANXDEP patients. Methods: Data were from 90 MI-ANXDEP patients recruited from 25 hospitals in Sweden and randomized to treatment in the iCBT trial Uppsala University Psychosocial Care Programme (U-CARE) Heart study. Time point of prediction was at completion of the first homework assignment. Adherence was defined as having completed more than 2 homework assignments within the 14-week treatment period. A supervised machine learning procedure was applied to identify the most potent predictors for adherence available at the first treatment session from a range of demographic, clinical, psychometric, and linguistic predictors. The internal binary classifier was a random forest model within a 3×10?fold cross-validated recursive feature elimination (RFE) resampling which selected the final predictor subset that best differentiated adherers versus nonadherers. Results: Patient mean age was 58.4 years (SD 9.4), 62% (56/90) were men, and 48% (43/90) were adherent. Out of the 34 potential predictors for adherence, RFE selected an optimal subset of 56% (19/34; Accuracy 0.64, 95% CI 0.61-0.68, P<.001). The strongest predictors for adherence were, in order of importance, (1) self-assessed cardiac-related fear, (2) sex, and (3) the number of words the patient used to answer the first homework assignment. Conclusions: For developing and testing effective iCBT interventions, investigating factors that predict adherence is important. Adherence to iCBT for MI-ANXDEP patients in the U-CARE Heart trial was best predicted by cardiac-related fear and sex, consistent with previous research, but also by novel linguistic predictors from written patient behavior which conceivably indicate verbal ability or therapeutic alliance. Future research should investigate potential causal mechanisms and seek to determine what underlying constructs the linguistic predictors tap into. Whether these findings replicate for other interventions outside of Sweden, in larger samples, and for patients with other conditions who are offered iCBT should also be investigated. Trial registration: ClinicalTrials.gov NCT01504191; https://clinicaltrials.gov/ct2/show/NCT01504191 (Archived at Webcite at http://www.webcitation.org/6xWWSEQ22) UR - http://www.jmir.org/2018/10/e10754/ UR - http://dx.doi.org/10.2196/10754 UR - http://www.ncbi.nlm.nih.gov/pubmed/30305255 ID - info:doi/10.2196/10754 ER - TY - JOUR AU - Wallin, Emma AU - Norlund, Fredrika AU - Olsson, Gustaf Erik Martin AU - Burell, Gunilla AU - Held, Claes AU - Carlsson, Tommy PY - 2018/03/16 TI - Treatment Activity, User Satisfaction, and Experienced Usability of Internet-Based Cognitive Behavioral Therapy for Adults With Depression and Anxiety After a Myocardial Infarction: Mixed-Methods Study JO - J Med Internet Res SP - e87 VL - 20 IS - 3 KW - mental health KW - internet KW - cognitive behavioral therapy KW - computer-assisted therapy KW - myocardial infarction KW - attrition KW - adherence N2 - Background: Knowledge about user experiences may lead to insights about how to improve treatment activity in Internet-based cognitive behavioral therapy (iCBT) to reduce symptoms of depression and anxiety among people with a somatic disease. There is a need for studies conducted alongside randomized trials, to explore treatment activity and user experiences related to such interventions, especially among people with older age who are recruited in routine care. Objective: The aim of the study was to explore treatment activity, user satisfaction, and usability experiences among patients allocated to treatment in the U-CARE Heart study, a randomized clinical trial of an iCBT intervention for treatment of depression and anxiety following a recent myocardial infarction. Methods: This was a mixed methods study where quantitative and qualitative approaches were used. Patients were recruited consecutively from 25 cardiac clinics in Sweden. The study included 117 patients allocated to 14 weeks of an iCBT intervention in the U-CARE Heart study. Quantitative data about treatment activity and therapist communication were collected through logged user patterns, which were analyzed with descriptive statistics. Qualitative data with regard to positive and negative experiences, and suggestions for improvements concerning the intervention, were collected through semistructured interviews with 21 patients in the treatment arm after follow-up. The interviews were analyzed with qualitative manifest content analysis. Results: Treatment activity was low with regard to number of completed modules (mean 0.76, SD 0.93, range 0-5) and completed assignments (mean 3.09, SD 4.05, range 0-29). Most of the participants initiated the introduction module (113/117, 96.6%), and about half (63/117, 53.9%) of all participants completed the introductory module, but only 18 (15.4%, 18/117) continued to work with any of the remaining 10 modules, and each of the remaining modules was completed by 7 or less of the participants. On average, patients sent less than 2 internal messages to their therapist during the intervention (mean 1.42, SD 2.56, range 0-16). Interviews revealed different preferences with regard to the internet-based portal, the content of the treatment program, and the therapist communication. Aspects related to the personal situation and required skills included unpleasant emotions evoked by the intervention, lack of time, and technical difficulties. Conclusions: Patients with a recent myocardial infarction and symptoms of depression and anxiety showed low treatment activity in this guided iCBT intervention with regard to completed modules, completed assignments, and internal messages sent to their therapist. The findings call attention to the need for researchers to carefully consider the preferences, personal situation, and technical skills of the end users during the development of these interventions. The study indicates several challenges that need to be addressed to improve treatment activity, user satisfaction, and usability in internet-based interventions in this population. UR - http://www.jmir.org/2018/3/e87/ UR - http://dx.doi.org/10.2196/jmir.9690 UR - http://www.ncbi.nlm.nih.gov/pubmed/29549067 ID - info:doi/10.2196/jmir.9690 ER - TY - JOUR AU - Norlund, Fredrika AU - Wallin, Emma AU - Olsson, Gustaf Erik Martin AU - Wallert, John AU - Burell, Gunilla AU - von Essen, Louise AU - Held, Claes PY - 2018/03/08 TI - Internet-Based Cognitive Behavioral Therapy for Symptoms of Depression and Anxiety Among Patients With a Recent Myocardial Infarction: The U-CARE Heart Randomized Controlled Trial JO - J Med Internet Res SP - e88 VL - 20 IS - 3 KW - eHealth KW - treatment adherence and compliance KW - patient acceptance of health care KW - patient selection KW - cardiac rehabilitation N2 - Background: Symptoms of depression and anxiety are common after a myocardial infarction (MI). Internet-based cognitive behavioral therapy (iCBT) has shown good results in other patient groups. Objective: The aim of this study was to evaluate the effectiveness of an iCBT treatment to reduce self-reported symptoms of depression and anxiety among patients with a recent MI. Methods: In total, 3928 patients were screened for eligibility in 25 Swedish hospitals. Of these, 239 patients (33.5%, 80/239 women, mean age 60 years) with a recent MI and symptoms of depression or anxiety were randomly allocated to a therapist-guided, 14-week iCBT treatment (n=117), or treatment as usual (TAU; n=122). The iCBT treatment was designed for post-MI patients. The primary outcome was the total score of the Hospital Anxiety and Depression Scale (HADS) 14 weeks post baseline, assessed over the internet. Treatment effect was evaluated according to the intention-to-treat principle, with multiple imputations. For the main analysis, a pooled treatment effect was estimated, controlling for age, sex, and baseline HADS. Results: There was a reduction in HADS scores over time in the total study sample (mean delta=?5.1, P<.001) but no difference between the study groups at follow-up (beta=?0.47, 95% CI ?1.95 to 1.00, P=.53). Treatment adherence was low. A total of 46.2% (54/117) of the iCBT group did not complete the introductory module. Conclusions: iCBT treatment for an MI population did not result in lower levels of symptoms of depression or anxiety compared with TAU. Low treatment adherence might have influenced the result. Trial Registration: ClinicalTrials.gov NCT01504191; https://clinicaltrials.gov/ct2/show/NCT01504191 (Archived at Webcite at http://www.webcitation.org/6xWWSEQ22) UR - http://www.jmir.org/2018/3/e88/ UR - http://dx.doi.org/10.2196/jmir.9710 UR - http://www.ncbi.nlm.nih.gov/pubmed/29519777 ID - info:doi/10.2196/jmir.9710 ER - TY - JOUR AU - Langeland, Halvor AU - Bergum, Daniel AU - Løberg, Magnus AU - Bjørnstad, Knut AU - Damås, Kristian Jan AU - Mollnes, Eirik Tom AU - Skjærvold, Nils-Kristian AU - Klepstad, Pål PY - 2018/01/19 TI - Transitions Between Circulatory States After Out-of-Hospital Cardiac Arrest: Protocol for an Observational, Prospective Cohort Study JO - JMIR Res Protoc SP - e17 VL - 7 IS - 1 KW - out-of-hospital cardiac arrest KW - critical care KW - hemodynamics KW - inflammation KW - biomarkers N2 - Background: The post cardiac arrest syndrome (PCAS) is responsible for the majority of in-hospital deaths following cardiac arrest (CA). The major elements of PCAS are anoxic brain injury and circulatory failure. Objective: This study aimed to investigate the clinical characteristics of circulatory failure and inflammatory responses after out-of-hospital cardiac arrest (OHCA) and to identify patterns of circulatory and inflammatory responses, which may predict circulatory deterioration in PCAS. Methods: This study is a single-center cohort study of 50 patients who receive intensive care after OHCA. The patients are followed for 5 days where detailed information from circulatory variables, including measurements by pulmonary artery catheters (PACs), is obtained in high resolution. Blood samples for inflammatory and endothelial biomarkers are taken at inclusion and thereafter daily. Every 10 min, the patients will be assessed and categorized in one of three circulatory categories. These categories are based on mean arterial pressure; heart rate; serum lactate concentrations; superior vena cava oxygen saturation; and need for fluid, vasoactive medications, and other interventions. We will analyze predictors of circulatory failure and their relation to inflammatory biomarkers. Results: Patient inclusion started in January 2016. Conclusions: This study will obtain advanced hemodynamic data with high resolution during the acute phase of PCAS and will analyze the details in circulatory state transitions related to circulatory failure. We aim to identify early predictors of circulatory deterioration and favorable outcome after CA. Trial Registration: ClinicalTrials.gov: NCT02648061; https://clinicaltrials.gov/ct2/show/NCT02648061 (Archived by WebCite at http://www.webcitation.org/6wVASuOla) UR - http://www.researchprotocols.org/2018/1/e17/ UR - http://dx.doi.org/10.2196/resprot.8558 UR - http://www.ncbi.nlm.nih.gov/pubmed/29351897 ID - info:doi/10.2196/resprot.8558 ER - TY - JOUR AU - Marvel, Adeline Francoise AU - Wang, Jane AU - Martin, Shay Seth PY - 2018/01/18 TI - Digital Health Innovation: A Toolkit to Navigate From Concept to Clinical Testing JO - JMIR Cardio SP - e2 VL - 2 IS - 1 KW - digital health innovation models KW - mHealth KW - innovation framework KW - development of smartphone applications KW - wearable technology KW - healthcare transformation UR - http://cardio.jmir.org/2018/1/e2/ UR - http://dx.doi.org/10.2196/cardio.7586 UR - http://www.ncbi.nlm.nih.gov/pubmed/31758761 ID - info:doi/10.2196/cardio.7586 ER - TY - JOUR AU - Alonzo, A. Angelo PY - 2017/10/13 TI - Studying Acute Coronary Syndrome Through the World Wide Web: Experiences and Lessons JO - JMIR Res Protoc SP - e182 VL - 6 IS - 10 KW - acute coronary syndrome KW - care-seeking KW - Internet study KW - Internet recruitment UR - http://www.researchprotocols.org/2017/10/e182/ UR - http://dx.doi.org/10.2196/resprot.6788 UR - http://www.ncbi.nlm.nih.gov/pubmed/29030328 ID - info:doi/10.2196/resprot.6788 ER - TY - JOUR AU - Brokmann, C. Jörg AU - Conrad, Clemens AU - Rossaint, Rolf AU - Bergrath, Sebastian AU - Beckers, K. Stefan AU - Tamm, Miriam AU - Czaplik, Michael AU - Hirsch, Frederik PY - 2016/12/01 TI - Treatment of Acute Coronary Syndrome by Telemedically Supported Paramedics Compared With Physician-Based Treatment: A Prospective, Interventional, Multicenter Trial JO - J Med Internet Res SP - e314 VL - 18 IS - 12 KW - acute coronary syndrome KW - prehospital emergency care KW - telemedicine KW - telehealth KW - myocardial infarction N2 - Background: Prehospital treatment of acute coronary syndrome (ACS) in German emergency medical services (EMSs) is reserved for EMS physicians due to legal issues. Objective: The objective of this prospective, interventional, multicenter trial was to evaluate the quality of telemedically-delegated therapy and the possible complications in patients with ACS. Methods: After approval by the ethics committee and trial registration, a one-year study phase was started in August 2012 with 5 ambulances, telemedically equipped and staffed with paramedics, in 4 German EMS districts. The paramedics could contact an EMS-physician?staffed telemedicine center. After initiation of an audio connection, real-time data transmission was automatically established. If required, 12-lead electrocardiogram (ECG) and still pictures could be sent. Video was streamed from inside each ambulance. All drugs, including opioids, were delegated to the paramedics based on standardized, predefined algorithms. To compare telemedically-delegated medication and treatment in ACS cases with regular EMS missions, a matched pair analysis with historical controls was performed. Results: Teleconsultation was performed on 150 patients having a cardiovascular emergency. In 39 cases, teleconsultation was started due to suspected ACS. No case had a medical complication. Correct handling of 12-lead ECG was performed equally between the groups (study group, n=38 vs control group, n=39, P>.99). There were no differences in correct handling of intravenous administration of acetylsalicylic acid, heparin, or morphine between both the groups (study group vs control group): acetylsalicylic acid, n=31 vs n=33, P=.73; unfractionated heparin, n=34 vs n=33, P>.99; morphine, n=29 vs n=27, P=.50. The correct handling of oxygen administration was significantly higher in the study group (n=29 vs n=18, P=.007). Conclusions: Telemedical delegation of guideline conform medication and therapy by paramedics in patients with ACS and was found to be feasible and safe. The quality of guideline-adherent therapy was not significantly different in both the groups except for the correct administration of oxygen, which was significantly higher in the study group. Trial Registration: Clinicaltrials.gov NCT01644006; http://clinicaltrials.gov/ct2/show/NCT01644006 (Archived by WebCite at http://www.webcitation.org/6mPam3eDy). UR - http://www.jmir.org/2016/12/e314/ UR - http://dx.doi.org/10.2196/jmir.6358 UR - http://www.ncbi.nlm.nih.gov/pubmed/27908843 ID - info:doi/10.2196/jmir.6358 ER - TY - JOUR PY - 2013// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e4587 VL - 5 IS - 2 UR - UR - http://dx.doi.org/10.5210/ojphi.v5i2.4587 UR - http://www.ncbi.nlm.nih.gov/pubmed/23923097 ID - info:doi/10.5210/ojphi.v5i2.4587 ER -