@Article{info:doi/10.2196/63186,
author="Miao, Shumei
and Ji, Pei
and Zhu, Yongqian
and Meng, Haoyu
and Jing, Mang
and Sheng, Rongrong
and Zhang, Xiaoliang
and Ding, Hailong
and Guo, Jianjun
and Gao, Wen
and Yang, Guanyu
and Liu, Yun",
title="The Construction and Application of a Clinical Decision Support System for Cardiovascular Diseases: Multimodal Data-Driven Development and Validation Study",
journal="JMIR Med Inform",
year="2025",
month="Mar",
day="3",
volume="13",
pages="e63186",
keywords="CVD",
keywords="CDSS",
keywords="multimodel data",
keywords="knowledge engine",
keywords="development",
keywords="cardiovascular disease",
keywords="clinical decision support system",
abstract="Background: Due to the acceleration of the aging population and the prevalence of unhealthy lifestyles, the incidence of cardiovascular diseases (CVDs) in China continues to grow. However, due to the uneven distribution of medical resources across regions and significant disparities in diagnostic and treatment levels, the diagnosis and management of CVDs face considerable challenges. Objective: The purpose of this study is to build a cardiovascular diagnosis and treatment knowledge base by using new technology, form an auxiliary decision support system, and integrate it into the doctor's workstation, to improve the assessment rate and treatment standardization rate. This study offers new ideas for the prevention and management of CVDs. Methods: This study designed a clinical decision support system (CDSS) with data, learning, knowledge, and application layers. It integrates multimodal data from hospital laboratory information systems, hospital information systems, electronic medical records, electrocardiography, nursing, and other systems to build a knowledge model. The unstructured data were segmented using natural language processing technology, and medical entity words and entity combination relationships were extracted using IDCNN (iterated dilated convolutional neural network) and TextCNN (text convolutional neural network). The CDSS refers to global CVD assessment indicators to design quality control strategies and an intelligent treatment plan recommendation engine map, establishing a big data analysis platform to achieve multidimensional, visualized data statistics for management decision support. Results: The CDSS system is embedded and interfaced with the physician workstation, triggering in real-time during the clinical diagnosis and treatment process. It establishes a 3-tier assessment control through pop-up windows and screen domination operations. Based on the intelligent diagnostic and treatment reminders of the CDSS, patients are given intervention treatments. The important risk assessment and diagnosis rate indicators significantly improved after the system came into use, and gradually increased within 2 years. The indicators of mandatory control, directly became 100\% after the CDSS was online. The CDSS enhanced the standardization of clinical diagnosis and treatment. Conclusions: This study establishes a specialized knowledge base for CVDs, combined with clinical multimodal information, to intelligently assess and stratify cardiovascular patients. It automatically recommends intervention treatments based on assessments and clinical characterizations, proving to be an effective exploration of using a CDSS to build a disease-specific intelligent system. ",
doi="10.2196/63186",
url="https://medinform.jmir.org/2025/1/e63186"
}


@Article{info:doi/10.2196/60238,
author="Gong, Ke
and Chen, Yifan
and Song, Xinyue
and Fu, Zhizhong
and Ding, Xiaorong",
title="Causal Inference for Hypertension Prediction With Wearable Electrocardiogram and Photoplethysmogram Signals: Feasibility Study",
journal="JMIR Cardio",
year="2025",
month="Jan",
day="23",
volume="9",
pages="e60238",
keywords="hypertension",
keywords="causal inference",
keywords="wearable physiological signals",
keywords="electrocardiogram",
keywords="photoplethysmogram",
abstract="Background: Hypertension is a leading cause of cardiovascular disease and premature death worldwide, and it puts a heavy burden on the health care system. Therefore, it is very important to detect and evaluate hypertension and related cardiovascular events to enable early prevention, detection, and management. Hypertension can be detected in a timely manner with cardiac signals, such as through an electrocardiogram (ECG) and photoplethysmogram (PPG), which can be observed via wearable sensors. Most previous studies predicted hypertension from ECG and PPG signals with extracted features that are correlated with hypertension. However, correlation is sometimes unreliable and may be affected by confounding factors. Objective: The aim of this study was to investigate the feasibility of predicting the risk of hypertension by exploring features that are causally related to hypertension via causal inference methods. Additionally, we paid special attention to and verified the reliability and effectiveness of causality compared to correlation. Methods: We used a large public dataset from the Aurora Project, which was conducted by Microsoft Research. The dataset included diverse individuals who were balanced in terms of gender, age, and the condition of hypertension, with their ECG and PPG signals simultaneously acquired with wrist-worn wearable devices. We first extracted 205 features from the ECG and PPG signals, calculated 6 statistical metrics for these 205 features, and selected some valuable features out of the 205 features under each statistical metric. Then, 6 causal graphs of the selected features for each kind of statistical metric and hypertension were constructed with the equivalent greedy search algorithm. We further fused the 6 causal graphs into 1 causal graph and identified features that were causally related to hypertension from the causal graph. Finally, we used these features to detect hypertension via machine learning algorithms. Results: We validated the proposed method on 405 subjects. We identified 24 causal features that were associated with hypertension. The causal features could detect hypertension with an accuracy of 89\%, precision of 92\%, and recall of 82\%, which outperformed detection with correlation features (accuracy of 85\%, precision of 88\%, and recall of 77\%). Conclusions: The results indicated that the causal inference--based approach can potentially clarify the mechanism of hypertension detection with noninvasive signals and effectively detect hypertension. It also revealed that causality can be more reliable and effective than correlation for hypertension detection and other application scenarios. ",
doi="10.2196/60238",
url="https://cardio.jmir.org/2025/1/e60238"
}


@Article{info:doi/10.2196/64374,
author="You, Yuzi
and Liang, Wei
and Zhao, Yajie",
title="Development and Validation of a Predictive Model Based on Serum Silent Information Regulator 6 Levels in Chinese Older Adult Patients: Cross-Sectional Descriptive Study",
journal="JMIR Aging",
year="2025",
month="Jan",
day="15",
volume="8",
pages="e64374",
keywords="aging",
keywords="coronary artery disease",
keywords="nomogram",
keywords="SIRT6",
keywords="TyG index",
keywords="silent information regulator 6",
keywords="triglyceride glucose index",
abstract="Background: Serum levels of silent information regulator 6 (SIRT6), a key biomarker of aging, were identified as a predictor of coronary artery disease (CAD), but whether SIRT6 can distinguish severity of coronary artery lesions in older adult patients is unknown. Objectives: This study developed a nomogram to demonstrate the functionality of SIRT6 in assessing severity of coronary artery atherosclerosis. Methods: Patients aged 60 years and older with angina pectoris were screened for this single-center clinical study between October 1, 2022, and March 31, 2023. Serum specimens of eligible patients were collected for SIRT6 detection by enzyme-linked immunosorbent assay. Clinical data and putative predictors, including 29 physiological characteristics, biochemical parameters, carotid artery ultrasonographic results, and complete coronary angiography findings, were evaluated, with CAD diagnosis as the primary outcome. The nomogram was derived from the Extreme Gradient Boosting (XGBoost) model, with logistic regression for variable selection. Model performance was assessed by examining discrimination, calibration, and clinical use separately. A 10-fold cross-validation technique was used to compare all models. The models' performance was further evaluated on the internal validation set to ensure that the obtained results were not due to overoptimization. Results: Eligible patients (n=222) were divided into 2 cohorts: the development cohort (n=178) and the validation cohort (n=44). Serum SIRT6 levels were identified as both an independent risk factor and a predictor for CAD in older adults. The area under the receiver operating characteristic curve (AUROC) was 0.725 (95\% CI 0.653?0.797). The optimal cutoff value of SIRT6 for predicting CAD was 546.384 pg/mL. Predictors included in this nomogram were serum SIRT6 levels, triglyceride glucose (TyG) index, and apolipoprotein B. The model achieved an AUROC of 0.956 (95\% CI 0.928?0.983) in the development cohort. Similarly, in the internal validation cohort, the AUROC was 0.913 (95\% CI 0.828?0.999). All models demonstrated satisfactory calibration, with predicted outcomes closely aligning with actual results. Conclusions: SIRT6 shows promise in predicting CAD, with enhanced predictive abilities when combined with the TyG index. In clinical settings, monitoring fluctuations in SIRT6 and TyG may offer valuable insights for early CAD detection. The nomogram for CAD outcome prediction in older adult patients with angina pectoris may aid in clinical trial design and personalized clinical decision-making, particularly in institutions where SIRT6 is being explored as a biomarker for aging or cardiovascular health. ",
doi="10.2196/64374",
url="https://aging.jmir.org/2025/1/e64374"
}


@Article{info:doi/10.2196/67256,
author="Yang, Xiaomeng
and Li, Zeyan
and Lei, Lei
and Shi, Xiaoyu
and Zhang, Dingming
and Zhou, Fei
and Li, Wenjing
and Xu, Tianyou
and Liu, Xinyu
and Wang, Songyun
and Yuan, Quan
and Yang, Jian
and Wang, Xinyu
and Zhong, Yanfei
and Yu, Lilei",
title="Noninvasive Oral Hyperspectral Imaging--Driven Digital Diagnosis of Heart Failure With Preserved Ejection Fraction: Model Development and Validation Study",
journal="J Med Internet Res",
year="2025",
month="Jan",
day="7",
volume="27",
pages="e67256",
keywords="heart failure with preserved ejection fraction",
keywords="HFpEF",
keywords="hyperspectral imaging",
keywords="HSI",
keywords="diagnostic model",
keywords="digital health",
keywords="Shapley Additive Explanations",
keywords="SHAP",
keywords="machine learning",
keywords="artificial intelligence",
keywords="AI",
keywords="cardiovascular disease",
keywords="predictive modeling",
keywords="oral health",
abstract="Background: Oral microenvironmental disorders are associated with an increased risk of heart failure with preserved ejection fraction (HFpEF). Hyperspectral imaging (HSI) technology enables the detection of substances that are visually indistinguishable to the human eye, providing a noninvasive approach with extensive applications in medical diagnostics. Objective: The objective of this study is to develop and validate a digital, noninvasive oral diagnostic model for patients with HFpEF using HSI combined with various machine learning algorithms. Methods: Between April 2023 and August 2023, a total of 140 patients were recruited from Renmin Hospital of Wuhan University to serve as the training and internal testing groups for this study. Subsequently, from August 2024 to September 2024, an additional 35 patients were enrolled from Three Gorges University and Yichang Central People's Hospital to constitute the external testing group. After preprocessing to ensure image quality, spectral and textural features were extracted from the images. We extracted 25 spectral bands from each patient image and obtained 8 corresponding texture features to evaluate the performance of 28 machine learning algorithms for their ability to distinguish control participants from participants with HFpEF. The model demonstrating the optimal performance in both internal and external testing groups was selected to construct the HFpEF diagnostic model. Hyperspectral bands significant for identifying participants with HFpEF were identified for further interpretative analysis. The Shapley Additive Explanations (SHAP) model was used to provide analytical insights into feature importance. Results: Participants were divided into a training group (n=105), internal testing group (n=35), and external testing group (n=35), with consistent baseline characteristics across groups. Among the 28 algorithms tested, the random forest algorithm demonstrated superior performance with an area under the receiver operating characteristic curve (AUC) of 0.884 and an accuracy of 82.9\% in the internal testing group, as well as an AUC of 0.812 and an accuracy of 85.7\% in the external testing group. For model interpretation, we used the top 25 features identified by the random forest algorithm. The SHAP analysis revealed discernible distinctions between control participants and participants with HFpEF, thereby validating the diagnostic model's capacity to accurately identify participants with HFpEF. Conclusions: This noninvasive and efficient model facilitates the identification of individuals with HFpEF, thereby promoting early detection, diagnosis, and treatment. Our research presents a clinically advanced diagnostic framework for HFpEF, validated using independent data sets and demonstrating significant potential to enhance patient care. Trial Registration: China Clinical Trial Registry ChiCTR2300078855; https://www.chictr.org.cn/showproj.html?proj=207133 ",
doi="10.2196/67256",
url="https://www.jmir.org/2025/1/e67256"
}


@Article{info:doi/10.2196/55916,
author="Grossbard, Eitan
and Marziano, Yehonatan
and Sharabi, Adam
and Abutbul, Eliyahu
and Berman, Aya
and Kassif-Lerner, Reut
and Barkai, Galia
and Hakim, Hila
and Segal, Gad",
title="Consensus Between Radiologists, Specialists in Internal Medicine, and AI Software on Chest X-Rays in a Hospital-at-Home Service: Prospective Observational Study",
journal="JMIR Form Res",
year="2024",
month="Dec",
day="24",
volume="8",
pages="e55916",
keywords="chest x-ray",
keywords="hospital-at-home",
keywords="telemedicine",
keywords="artificial intelligence",
keywords="kappa",
keywords="x-ray",
keywords="home hospitalization",
keywords="clinical data",
keywords="chest",
keywords="implementation",
keywords="comparative analysis",
keywords="radiologist",
keywords="AI",
abstract="Background: Home hospitalization is a care modality growing in popularity worldwide. Telemedicine-driven hospital-at-home (HAH) services could replace traditional hospital departments for selected patients. Chest x-rays typically serve as a key diagnostic tool in such cases. Objective: The implementation, analysis, and clinical assimilation of chest x-rays into an HAH service has not been described yet. Our objective is to introduce this essential information to the realm of HAH services for the first time worldwide. Methods: The study involved a prospective follow-up, description, and analysis of the HAH patient population who underwent chest x-rays at home. A comparative analysis was performed to evaluate the level of agreement among three interpretation modalities: a radiologist, a specialist in internal medicine, and a designated artificial intelligence (AI) algorithm. Results: Between February 2021 and May 2023, 300 chest radiographs were performed at the homes of 260 patients, with the median age being 78 (IQR 65?87) years. The most frequent underlying morbidity was cardiovascular disease (n=185, 71.2\%). Of the x-rays, 286 (95.3\%) were interpreted by a specialist in internal medicine, 29 (9.7\%) by a specialized radiologist, and 95 (31.7\%) by the AI software. The overall raw agreement level among these three modalities exceeded 90\%. The consensus level evaluated using the Cohen $\kappa$ coefficient showed substantial agreement ($\kappa$=0.65) and moderate agreement ($\kappa$=0.49) between the specialist in internal medicine and the radiologist, and between the specialist in internal medicine and the AI software, respectively. Conclusions: Chest x-rays play a crucial role in the HAH setting. Rapid and reliable interpretation of these x-rays is essential for determining whether a patient requires transfer back to in-hospital surveillance. Our comparative results showed that interpretation by an experienced specialist in internal medicine demonstrates a significant level of consensus with that of the radiologists. However, AI algorithm-based interpretation needs to be further developed and revalidated prior to clinical applications. ",
doi="10.2196/55916",
url="https://formative.jmir.org/2024/1/e55916"
}


@Article{info:doi/10.2196/57641,
author="Zhu, Jinpu
and Yang, Fushuang
and Wang, Yang
and Wang, Zhongtian
and Xiao, Yao
and Wang, Lie
and Sun, Liping",
title="Accuracy of Machine Learning in Discriminating Kawasaki Disease and Other Febrile Illnesses: Systematic Review and Meta-Analysis",
journal="J Med Internet Res",
year="2024",
month="Nov",
day="18",
volume="26",
pages="e57641",
keywords="machine learning",
keywords="artificial intelligence",
keywords="Kawasaki disease",
keywords="febrile illness",
keywords="coronary artery lesions",
keywords="systematic review",
keywords="meta-analysis",
abstract="Background: Kawasaki disease (KD) is an acute pediatric vasculitis that can lead to coronary artery aneurysms and severe cardiovascular complications, often presenting with obvious fever in the early stages. In current clinical practice, distinguishing KD from other febrile illnesses remains a significant challenge. In recent years, some researchers have explored the potential of machine learning (ML) methods for the differential diagnosis of KD versus other febrile illnesses, as well as for predicting coronary artery lesions (CALs) in people with KD. However, there is still a lack of systematic evidence to validate their effectiveness. Therefore, we have conducted the first systematic review and meta-analysis to evaluate the accuracy of ML in differentiating KD from other febrile illnesses and in predicting CALs in people with KD, so as to provide evidence-based support for the application of ML in the diagnosis and treatment of KD. Objective: This study aimed to summarize the accuracy of ML in differentiating KD from other febrile illnesses and predicting CALs in people with KD. Methods: PubMed, Cochrane Library, Embase, and Web of Science were systematically searched until September 26, 2023. The risk of bias in the included original studies was appraised using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Stata (version 15.0; StataCorp) was used for the statistical analysis. Results: A total of 29 studies were incorporated. Of them, 20 used ML to differentiate KD from other febrile illnesses. These studies involved a total of 103,882 participants, including 12,541 people with KD. In the validation set, the pooled concordance index, sensitivity, and specificity were 0.898 (95\% CI 0.874-0.922), 0.91 (95\% CI 0.83-0.95), and 0.86 (95\% CI 0.80-0.90), respectively. Meanwhile, 9 studies used ML for early prediction of the risk of CALs in children with KD. These studies involved a total of 6503 people with KD, of whom 986 had CALs. The pooled concordance index in the validation set was 0.787 (95\% CI 0.738-0.835). Conclusions: The diagnostic and predictive factors used in the studies we included were primarily derived from common clinical data. The ML models constructed based on these clinical data demonstrated promising effectiveness in differentiating KD from other febrile illnesses and in predicting coronary artery lesions. Therefore, in future research, we can explore the use of ML methods to identify more efficient predictors and develop tools that can be applied on a broader scale for the differentiation of KD and the prediction of CALs. ",
doi="10.2196/57641",
url="https://www.jmir.org/2024/1/e57641"
}


@Article{info:doi/10.2196/54746,
author="Saraya, Norah
and McBride, Jonathon
and Singh, Karandeep
and Sohail, Omar
and Das, Jeet Porag",
title="Comparison of Auscultation Quality Using Contemporary Digital Stethoscopes",
journal="JMIR Cardio",
year="2024",
month="Nov",
day="8",
volume="8",
pages="e54746",
keywords="auscultation",
keywords="digital stethoscopes",
keywords="valvular heart disease",
doi="10.2196/54746",
url="https://cardio.jmir.org/2024/1/e54746"
}


@Article{info:doi/10.2196/58732,
author="Nguyen, Minh Hieu
and Anderson, William
and Chou, Shih-Hsiung
and McWilliams, Andrew
and Zhao, Jing
and Pajewski, Nicholas
and Taylor, Yhenneko",
title="Predictive Models for Sustained, Uncontrolled Hypertension and Hypertensive Crisis Based on Electronic Health Record Data: Algorithm Development and Validation",
journal="JMIR Med Inform",
year="2024",
month="Oct",
day="28",
volume="12",
pages="e58732",
keywords="machine learning",
keywords="risk prediction",
keywords="predictive model",
keywords="decision support",
keywords="blood pressure",
keywords="cardiovascular",
keywords="electronic health record",
abstract="Background: Assessing disease progression among patients with uncontrolled hypertension is important for identifying opportunities for intervention. Objective: We aim to develop and validate 2 models, one to predict sustained, uncontrolled hypertension (?2 blood pressure [BP] readings ?140/90 mm Hg or ?1 BP reading ?180/120 mm Hg) and one to predict hypertensive crisis (?1 BP reading ?180/120 mm Hg) within 1 year of an index visit (outpatient or ambulatory encounter in which an uncontrolled BP reading was recorded). Methods: Data from 142,897 patients with uncontrolled hypertension within Atrium Health Greater Charlotte in 2018 were used. Electronic health record--based predictors were based on the 1-year period before a patient's index visit. The dataset was randomly split (80:20) into a training set and a validation set. In total, 4 machine learning frameworks were considered: L2-regularized logistic regression, multilayer perceptron, gradient boosting machines, and random forest. Model selection was performed with 10-fold cross-validation. The final models were assessed on discrimination (C-statistic), calibration (eg, integrated calibration index), and net benefit (with decision curve analysis). Additionally, internal-external cross-validation was performed at the county level to assess performance with new populations and summarized using random-effect meta-analyses. Results: In internal validation, the C-statistic and integrated calibration index were 0.72 (95\% CI 0.71?0.72) and 0.015 (95\% CI 0.012?0.020) for the sustained, uncontrolled hypertension model, and 0.81 (95\% CI 0.79?0.82) and 0.009 (95\% CI 0.007?0.011) for the hypertensive crisis model. The models had higher net benefit than the default policies (ie, treat-all and treat-none) across different decision thresholds. In internal-external cross-validation, the pooled performance was consistent with internal validation results; in particular, the pooled C-statistics were 0.70 (95\% CI 0.69?0.71) and 0.79 (95\% CI 0.78?0.81) for the sustained, uncontrolled hypertension model and hypertensive crisis model, respectively. Conclusions: An electronic health record--based model predicted hypertensive crisis reasonably well in internal and internal-external validations. The model can potentially be used to support population health surveillance and hypertension management. Further studies are needed to improve the ability to predict sustained, uncontrolled hypertension. ",
doi="10.2196/58732",
url="https://medinform.jmir.org/2024/1/e58732"
}


@Article{info:doi/10.2196/60503,
author="Xie, Fagen
and Lee, Ming-sum
and Allahwerdy, Salam
and Getahun, Darios
and Wessler, Benjamin
and Chen, Wansu",
title="Identifying the Severity of Heart Valve Stenosis and Regurgitation Among a Diverse Population Within an Integrated Health Care System: Natural Language Processing Approach",
journal="JMIR Cardio",
year="2024",
month="Sep",
day="30",
volume="8",
pages="e60503",
keywords="echocardiography report",
keywords="heart valve",
keywords="stenosis",
keywords="regurgitation",
keywords="natural language processing",
keywords="algorithm",
abstract="Background: Valvular heart disease (VHD) is a leading cause of cardiovascular morbidity and mortality that poses a substantial health care and economic burden on health care systems. Administrative diagnostic codes for ascertaining VHD diagnosis are incomplete. Objective: This study aimed to develop a natural language processing (NLP) algorithm to identify patients with aortic, mitral, tricuspid, and pulmonic valve stenosis and regurgitation from transthoracic echocardiography (TTE) reports within a large integrated health care system. Methods: We used reports from echocardiograms performed in the Kaiser Permanente Southern California (KPSC) health care system between January 1, 2011, and December 31, 2022. Related terms/phrases of aortic, mitral, tricuspid, and pulmonic stenosis and regurgitation and their severities were compiled from the literature and enriched with input from clinicians. An NLP algorithm was iteratively developed and fine-trained via multiple rounds of chart review, followed by adjudication. The developed algorithm was applied to 200 annotated echocardiography reports to assess its performance and then the study echocardiography reports. Results: A total of 1,225,270 TTE reports were extracted from KPSC electronic health records during the study period. In these reports, valve lesions identified included 111,300 (9.08\%) aortic stenosis, 20,246 (1.65\%) mitral stenosis, 397 (0.03\%) tricuspid stenosis, 2585 (0.21\%) pulmonic stenosis, 345,115 (28.17\%) aortic regurgitation, 802,103 (65.46\%) mitral regurgitation, 903,965 (73.78\%) tricuspid regurgitation, and 286,903 (23.42\%) pulmonic regurgitation. Among the valves, 50,507 (4.12\%), 22,656 (1.85\%), 1685 (0.14\%), and 1767 (0.14\%) were identified as prosthetic aortic valves, mitral valves, tricuspid valves, and pulmonic valves, respectively. Mild and moderate were the most common severity levels of heart valve stenosis, while trace and mild were the most common severity levels of regurgitation. Males had a higher frequency of aortic stenosis and all 4 valvular regurgitations, while females had more mitral, tricuspid, and pulmonic stenosis. Non-Hispanic Whites had the highest frequency of all 4 valvular stenosis and regurgitations. The distribution of valvular stenosis and regurgitation severity was similar across race/ethnicity groups. Frequencies of aortic stenosis, mitral stenosis, and regurgitation of all 4 heart valves increased with age. In TTE reports with stenosis detected, younger patients were more likely to have mild aortic stenosis, while older patients were more likely to have severe aortic stenosis. However, mitral stenosis was opposite (milder in older patients and more severe in younger patients). In TTE reports with regurgitation detected, younger patients had a higher frequency of severe/very severe aortic regurgitation. In comparison, older patients had higher frequencies of mild aortic regurgitation and severe mitral/tricuspid regurgitation. Validation of the NLP algorithm against the 200 annotated TTE reports showed excellent precision, recall, and F1-scores. Conclusions: The proposed computerized algorithm could effectively identify heart valve stenosis and regurgitation, as well as the severity of valvular involvement, with significant implications for pharmacoepidemiological studies and outcomes research. ",
doi="10.2196/60503",
url="https://cardio.jmir.org/2024/1/e60503",
url="http://www.ncbi.nlm.nih.gov/pubmed/39348175"
}


@Article{info:doi/10.2196/62890,
author="Kim, Kwan Yun
and Seo, Won-Doo
and Lee, Jung Sun
and Koo, Hyung Ja
and Kim, Chul Gyung
and Song, Seok Hee
and Lee, Minji",
title="Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study",
journal="J Med Internet Res",
year="2024",
month="Sep",
day="17",
volume="26",
pages="e62890",
keywords="early cardiac arrest warning system",
keywords="electric medical record",
keywords="explainable clinical decision support system",
keywords="pseudo-real-time evaluation",
keywords="ensemble learning",
keywords="cost-sensitive learning",
abstract="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. ",
doi="10.2196/62890",
url="https://www.jmir.org/2024/1/e62890"
}


@Article{info:doi/10.2196/49530,
author="Liang, Huey-Wen
and Wu, Chueh-Hung
and Lin, Chen
and Chang, Hsiang-Chih
and Lin, Yu-Hsuan
and Chen, Shao-Yu
and Hsu, Wei-Chen",
title="Rest-Activity Rhythm Differences in Acute Rehabilitation Between Poststroke Patients and Non--Brain Disease Controls: Comparative Study",
journal="J Med Internet Res",
year="2024",
month="Jul",
day="4",
volume="26",
pages="e49530",
keywords="circadian rhythms",
keywords="stroke rehabilitation, rest-activity rhythms, relative amplitude, delirium screening, interdaily stability",
abstract="Background: Circadian rhythm disruptions are a common concern for poststroke patients undergoing rehabilitation and might negatively impact their functional outcomes. Objective: Our research aimed to uncover unique patterns and disruptions specific to poststroke rehabilitation patients and identify potential differences in specific rest-activity rhythm indicators when compared to inpatient controls with non--brain-related lesions, such as patients with spinal cord injuries. Methods: We obtained a 7-day recording with a wearable actigraphy device from 25 poststroke patients (n=9, 36\% women; median age 56, IQR 46-71) and 25 age- and gender-matched inpatient control participants (n=15, 60\% women; median age 57, IQR 46.5-68.5). To assess circadian rhythm, we used a nonparametric method to calculate key rest-activity rhythm indicators---relative amplitude, interdaily stability, and intradaily variability. Relative amplitude, quantifying rest-activity rhythm amplitude while considering daily variations and unbalanced amplitudes, was calculated as the ratio of the difference between the most active 10 continuous hours and the least active 5 continuous hours to the sum of these 10 and 5 continuous hours. We also examined the clinical correlations between rest-activity rhythm indicators and delirium screening tools, such as the 4 A's Test and the Barthel Index, which assess delirium and activities of daily living. Results: Patients who had a stroke had higher least active 5-hour values compared to the control group (median 4.29, IQR 2.88-6.49 vs median 1.84, IQR 0.67-4.34; P=.008). The most active 10-hour values showed no significant differences between the groups (stroke group: median 38.92, IQR 14.60-40.87; control group: median 31.18, IQR 18.02-46.84; P=.93). The stroke group presented a lower relative amplitude compared to the control group (median 0.74, IQR 0.57-0.85 vs median 0.88, IQR 0.71-0.96; P=.009). Further analysis revealed no significant differences in other rest-activity rhythm metrics between the two groups. Among the patients who had a stroke, a negative correlation was observed between the 4 A's Test scores and relative amplitude ($\rho$=--0.41; P=.045). Across all participants, positive correlations emerged between the Barthel Index scores and both interdaily stability ($\rho$=0.34; P=.02) and the most active 10-hour value ($\rho$=0.42; P=.002). Conclusions: This study highlights the relevance of circadian rhythm disruptions in poststroke rehabilitation and provides insights into potential diagnostic and prognostic implications for rest-activity rhythm indicators as digital biomarkers. ",
doi="10.2196/49530",
url="https://www.jmir.org/2024/1/e49530"
}


@Article{info:doi/10.2196/50701,
author="Klier, Kristina
and Koch, Lucas
and Graf, Lisa
and Schink{\"o}the, Timo
and Schmidt, Annette",
title="Diagnostic Accuracy of Single-Lead Electrocardiograms Using the Kardia Mobile App and the Apple Watch 4: Validation Study",
journal="JMIR Cardio",
year="2023",
month="Nov",
day="23",
volume="7",
pages="e50701",
keywords="accuracy",
keywords="electrocardiography",
keywords="eHealth",
keywords="mHealth",
keywords="mobile health",
keywords="app",
keywords="applications",
keywords="mobile monitoring",
keywords="electrocardiogram",
keywords="ECG",
keywords="telemedicine",
keywords="diagnostic",
keywords="diagnosis",
keywords="monitoring",
keywords="heart",
keywords="cardiology",
keywords="mobile phone",
abstract="Background: To date, the 12-lead electrocardiogram (ECG) is the gold standard for cardiological diagnosis in clinical settings. With the advancements in technology, a growing number of smartphone apps and gadgets for recording, visualizing, and evaluating physical performance as well as health data is available. Although this new smart technology is innovative and time- and cost-efficient, less is known about its diagnostic accuracy and reliability. Objective: This study aimed to examine the agreement between the mobile single-lead ECG measurements of the Kardia Mobile App and the Apple Watch 4 compared to the 12-lead gold standard ECG in healthy adults under laboratory conditions. Furthermore, it assessed whether the measurement error of the devices increases with an increasing heart rate. Methods: This study was designed as a prospective quasi-experimental 1-sample measurement, in which no randomization of the sampling was carried out. In total, ECGs at rest from 81 participants (average age 24.89, SD?8.58 years; n=58, 72\% male) were recorded and statistically analyzed. Bland-Altman plots were created to graphically illustrate measurement differences. To analyze the agreement between the single-lead ECGs and the 12-lead ECG, Pearson correlation coefficient (r) and Lin concordance correlation coefficient (CCCLin) were calculated. Results: The results showed a higher agreement for the Apple Watch (mean deviation QT: 6.85\%; QT interval corrected for heart rate using Fridericia formula [QTcF]: 7.43\%) than Kardia Mobile (mean deviation QT: 9.53\%; QTcF: 9.78\%) even if both tend to underestimate QT and QTcF intervals. For Kardia Mobile, the QT and QTcF intervals correlated significantly with the gold standard (rQT=0.857 and rQTcF=0.727; P<.001). CCCLin corresponded to an almost complete heuristic agreement for the QT interval (0.835), whereas the QTcF interval was in the range of strong agreement (0.682). Further, for the Apple Watch, Pearson correlations were highly significant and in the range of a large effect (rQT=0.793 and rQTcF=0.649; P<.001). CCCLin corresponded to a strong heuristic agreement for both the QT (0.779) and QTcF (0.615) intervals. A small negative correlation between the measurement error and increasing heart rate could be found of each the devices and the reference. Conclusions: Smart technology seems to be a promising and reliable approach for nonclinical health monitoring. Further research is needed to broaden the evidence regarding its validity and usability in different target groups. ",
doi="10.2196/50701",
url="https://cardio.jmir.org/2023/1/e50701",
url="http://www.ncbi.nlm.nih.gov/pubmed/37995111"
}


@Article{info:doi/10.2196/47292,
author="Simonson, K. Julie
and Anderson, Misty
and Polacek, Cate
and Klump, Erika
and Haque, N. Saira",
title="Characterizing Real-World Implementation of Consumer Wearables for the Detection of Undiagnosed Atrial Fibrillation in Clinical Practice: Targeted Literature Review",
journal="JMIR Cardio",
year="2023",
month="Nov",
day="3",
volume="7",
pages="e47292",
keywords="arrhythmias",
keywords="atrial fibrillation",
keywords="clinical workflow",
keywords="consumer wearable devices",
keywords="smartwatches",
keywords="wearables",
keywords="remote patient monitoring",
keywords="virtual care",
keywords="mobile phone",
abstract="Background: Atrial fibrillation (AF), the most common cardiac arrhythmia, is often undiagnosed because of lack of awareness and frequent asymptomatic presentation. As AF is associated with increased risk of stroke, early detection is clinically relevant. Several consumer wearable devices (CWDs) have been cleared by the US Food and Drug Administration for irregular heart rhythm detection suggestive of AF. However, recommendations for the use of CWDs for AF detection in clinical practice, especially with regard to pathways for workflows and clinical decisions, remain lacking. Objective: We conducted a targeted literature review to identify articles on CWDs characterizing the current state of wearable technology for AF detection, identifying approaches to implementing CWDs into the clinical workflow, and characterizing provider and patient perspectives on CWDs for patients at risk of AF. Methods: PubMed, ClinicalTrials.gov, UpToDate Clinical Reference, and DynaMed were searched for articles in English published between January 2016 and July 2023. The searches used predefined Medical Subject Headings (MeSH) terms, keywords, and search strings. Articles of interest were specifically on CWDs; articles on ambulatory monitoring tools, tools available by prescription, or handheld devices were excluded. Search results were reviewed for relevancy and discussed among the authors for inclusion. A qualitative analysis was conducted and themes relevant to our study objectives were identified. Results: A total of 31 articles met inclusion criteria: 7 (23\%) medical society reports or guidelines, 4 (13\%) general reviews, 5 (16\%) systematic reviews, 5 (16\%) health care provider surveys, 7 (23\%) consumer or patient surveys or interviews, and 3 (10\%) analytical reports. Despite recognition of CWDs by medical societies, detailed guidelines regarding CWDs for AF detection were limited, as was the availability of clinical tools. A main theme was the lack of pragmatic studies assessing real-world implementation of CWDs for AF detection. Clinicians expressed concerns about data overload; potential for false positives; reimbursement issues; and the need for clinical tools such as care pathways and guidelines, preferably developed or endorsed by professional organizations. Patient-facing challenges included device costs and variability in digital literacy or technology acceptance. Conclusions: This targeted literature review highlights the lack of a comprehensive body of literature guiding real-world implementation of CWDs for AF detection and provides insights for informing additional research and developing appropriate tools and resources for incorporating these devices into clinical practice. The results should also provide an impetus for the active involvement of medical societies and other health care stakeholders in developing appropriate tools and resources for guiding the real-world use of CWDs for AF detection. These resources should target clinicians, patients, and health care systems with the goal of facilitating clinician or patient engagement and using an evidence-based approach for establishing guidelines or frameworks for administrative workflows and patient care pathways. ",
doi="10.2196/47292",
url="https://cardio.jmir.org/2023/1/e47292",
url="http://www.ncbi.nlm.nih.gov/pubmed/37921865"
}


@Article{info:doi/10.2196/48096,
author="Klier, Kristina
and Patel, J. Yash
and Schink{\"o}the, Timo
and Harbeck, Nadia
and Schmidt, Annette",
title="Corrected QT Interval (QTc) Diagnostic App for the Oncological Routine: Development Study",
journal="JMIR Cardio",
year="2023",
month="Sep",
day="11",
volume="7",
pages="e48096",
keywords="telemedicine",
keywords="mobile health",
keywords="mHealth",
keywords="eHealth",
keywords="tele-cardiology",
keywords="cardiology",
keywords="long QT syndrome",
keywords="prolonged QT interval",
keywords="electrocardiography",
keywords="ECG",
keywords="telehealth",
keywords="app",
keywords="application",
keywords="oncology",
keywords="cancer",
keywords="diagnosis",
keywords="diagnostic",
keywords="heart",
keywords="arrhythmia",
keywords="cardiotoxic",
keywords="side effects",
keywords="adverse effects",
abstract="Background: Numerous antineoplastic drugs such as chemotherapeutics have cardiotoxic side effects and can lead to long QT syndrome (LQTS). When diagnosed and treated in time, the potentially fatal outcomes of LQTS can be prevented. Therefore, regular electrocardiogram (ECG) assessments are critical to ensure patient safety. However, these assessments are associated with patient discomfort and require timely support of the attending oncologist by a cardiologist. Objective: This study aimed to examine whether this approach can be made more efficient and comfortable by a smartphone app (QTc Tracker), supporting single-lead ECG records on site and transferring to a tele-cardiologist for an immediate diagnosis. Methods: To evaluate the QTc Tracker, it was implemented in 54 cancer centers in Germany. In total, 266 corrected QT interval (QTc) diagnoses of 122 patients were recorded. Moreover, a questionnaire on routine ECG workflow, turnaround time, and satisfaction (1=best, 6=worst) was answered by the centers before and after the implementation of the QTc Tracker. Results: Compared to the routine ECG workflow, the QTc Tracker enabled a substantial turnaround time reduction of 98\% (mean 2.67, 95\% CI 1.72-2.67 h) and even further time efficiency in combination with a cardiologic on-call service (mean 12.10, 95\% CI 5.67-18.67 min). Additionally, nurses and patients reported higher satisfaction when using the QTc Tracker. In particular, patients' satisfaction sharply improved from 2.59 (95\% CI 2.41-2.88) for the routine ECG workflow to 1.25 (95\% CI 0.99-1.51) for the QTc Tracker workflow. Conclusions: These results reveal a significant improvement regarding reduced turnaround time and increased user satisfaction. Best patient care might be guaranteed as the exposure of patients with an uncontrolled risk of QTc prolongations can be avoided by using the fast and easy QTc Tracker. In particular, as regular side-effect monitoring, the QTc Tracker app promises more convenience for patients and their physicians. Finally, future studies are needed to empirically test the usability and validity of such mobile ECG assessment methods. Trial Registration: ClinicalTrials.gov NCT04055493; https://classic.clinicaltrials.gov/ct2/show/NCT04055493 ",
doi="10.2196/48096",
url="https://cardio.jmir.org/2023/1/e48096",
url="http://www.ncbi.nlm.nih.gov/pubmed/37695655"
}


@Article{info:doi/10.2196/44983,
author="Stremmel, Christopher
and Breitschwerdt, R{\"u}diger",
title="Digital Transformation in the Diagnostics and Therapy of Cardiovascular Diseases: Comprehensive Literature Review",
journal="JMIR Cardio",
year="2023",
month="Aug",
day="30",
volume="7",
pages="e44983",
keywords="cardiovascular",
keywords="digital medicine",
keywords="telehealth",
keywords="artificial intelligence",
keywords="telemedicine",
keywords="mobile phone",
keywords="review",
abstract="Background: The digital transformation of our health care system has experienced a clear shift in the last few years due to political, medical, and technical innovations and reorganization. In particular, the cardiovascular field has undergone a significant change, with new broad perspectives in terms of optimized treatment strategies for patients nowadays. Objective: After a short historical introduction, this comprehensive literature review aimed to provide a detailed overview of the scientific evidence regarding digitalization in the diagnostics and therapy of cardiovascular diseases (CVDs). Methods: We performed an extensive literature search of the PubMed database and included all related articles that were published as of March 2022. Of the 3021 studies identified, 1639 (54.25\%) studies were selected for a structured analysis and presentation (original articles: n=1273, 77.67\%; reviews or comments: n=366, 22.33\%). In addition to studies on CVDs in general, 829 studies could be assigned to a specific CVD with a diagnostic and therapeutic approach. For data presentation, all 829 publications were grouped into 6 categories of CVDs. Results: Evidence-based innovations in the cardiovascular field cover a wide medical spectrum, starting from the diagnosis of congenital heart diseases or arrhythmias and overoptimized workflows in the emergency care setting of acute myocardial infarction to telemedical care for patients having chronic diseases such as heart failure, coronary artery disease, or hypertension. The use of smartphones and wearables as well as the integration of artificial intelligence provides important tools for location-independent medical care and the prevention of adverse events. Conclusions: Digital transformation has opened up multiple new perspectives in the cardiovascular field, with rapidly expanding scientific evidence. Beyond important improvements in terms of patient care, these innovations are also capable of reducing costs for our health care system. In the next few years, digital transformation will continue to revolutionize the field of cardiovascular medicine and broaden our medical and scientific horizons. ",
doi="10.2196/44983",
url="https://cardio.jmir.org/2023/1/e44983",
url="http://www.ncbi.nlm.nih.gov/pubmed/37647103"
}


@Article{info:doi/10.2196/44003,
author="Zepeda-Echavarria, Alejandra
and van de Leur, R. Rutger
and van Sleuwen, Meike
and Hassink, J. Rutger
and Wildbergh, X. Thierry
and Doevendans, A. Pieter
and Jaspers, Joris
and van Es, Ren{\'e}",
title="Electrocardiogram Devices for Home Use: Technological and Clinical Scoping Review",
journal="JMIR Cardio",
year="2023",
month="Jul",
day="7",
volume="7",
pages="e44003",
keywords="electrocardiogram",
keywords="mobile ECG",
keywords="home use ECG",
keywords="wearables",
keywords="medical devices",
keywords="ECG clinical validation, ECG technical characteristics",
abstract="Background: Electrocardiograms (ECGs) are used by physicians to record, monitor, and diagnose the electrical activity of the heart. Recent technological advances have allowed ECG devices to move out of the clinic and into the home environment. There is a great variety of mobile ECG devices with the capabilities to be used in home environments. Objective: This scoping review aimed to provide a comprehensive overview of the current landscape of mobile ECG devices, including the technology used, intended clinical use, and available clinical evidence. Methods: We conducted a scoping review to identify studies concerning mobile ECG devices in the electronic database PubMed. Secondarily, an internet search was performed to identify other ECG devices available in the market. We summarized the devices' technical information and usability characteristics based on manufacturer data such as datasheets and user manuals. For each device, we searched for clinical evidence on the capabilities to record heart disorders by performing individual searches in PubMed and ClinicalTrials.gov, as well as the Food and Drug Administration (FDA) 510(k) Premarket Notification and De Novo databases. Results: From the PubMed database and internet search, we identified 58 ECG devices with available manufacturer information. Technical characteristics such as shape, number of electrodes, and signal processing influence the capabilities of the devices to record cardiac disorders. Of the 58 devices, only 26 (45\%) had clinical evidence available regarding their ability to detect heart disorders such as rhythm disorders, more specifically atrial fibrillation. Conclusions: ECG devices available in the market are mainly intended to be used for the detection of arrhythmias. No devices are intended to be used for the detection of other cardiac disorders. Technical and design characteristics influence the intended use of the devices and use environments. For mobile ECG devices to be intended to detect other cardiac disorders, challenges regarding signal processing and sensor characteristics should be solved to increase their detection capabilities. Devices recently released include the use of other sensors on ECG devices to increase their detection capabilities. ",
doi="10.2196/44003",
url="https://cardio.jmir.org/2023/1/e44003",
url="http://www.ncbi.nlm.nih.gov/pubmed/37418308"
}


@Article{info:doi/10.2196/31302,
author="Albuquerque de Almeida, Fernando
and Corro Ramos, Isaac
and Al, Maiwenn
and Rutten-van M{\"o}lken, Maureen",
title="Home Telemonitoring and a Diagnostic Algorithm in the Management of Heart Failure in the Netherlands: Cost-effectiveness Analysis",
journal="JMIR Cardio",
year="2022",
month="Aug",
day="4",
volume="6",
number="2",
pages="e31302",
keywords="discrete event simulation",
keywords="cost-effectiveness",
keywords="early warning systems",
keywords="home telemonitoring",
keywords="diagnostic algorithm",
keywords="heart failure",
abstract="Background: Heart failure is a major health concern associated with significant morbidity, mortality, and reduced quality of life in patients. Home telemonitoring (HTM) facilitates frequent or continuous assessment of disease signs and symptoms, and it has shown to improve compliance by involving patients in their own care and prevent emergency admissions by facilitating early detection of clinically significant changes. Diagnostic algorithms (DAs) are predictive mathematical relationships that make use of a wide range of collected data for calculating the likelihood of a particular event and use this output for prioritizing patients with regard to their treatment. Objective: This study aims to assess the cost-effectiveness of HTM and a DA in the management of heart failure in the Netherlands. Three interventions were analyzed: usual care, HTM, and HTM plus a DA. Methods: A previously published discrete event simulation model was used. The base-case analysis was performed according to the Dutch guidelines for economic evaluation. Sensitivity, scenario, and value of information analyses were performed. Particular attention was given to the cost-effectiveness of the DA at various levels of diagnostic accuracy of event prediction and to different patient subgroups. Results: HTM plus the DA extendedly dominates HTM alone, and it has a deterministic incremental cost-effectiveness ratio compared with usual care of {\texteuro}27,712 (currency conversion rate in purchasing power parity at the time of study: {\texteuro}1=US \$1.29; further conversions are not applicable in cost-effectiveness terms) per quality-adjusted life year. The model showed robustness in the sensitivity and scenario analyses. HTM plus the DA had a 96.0\% probability of being cost-effective at the appropriate {\texteuro}80,000 per quality-adjusted life year threshold. An optimal point for the threshold value for the alarm of the DA in terms of its cost-effectiveness was estimated. New York Heart Association class IV patients were the subgroup with the worst cost-effectiveness results versus usual care, while HTM plus the DA was found to be the most cost-effective for patients aged <65 years and for patients in New York Heart Association class I. Conclusions: Although the increased costs of adopting HTM plus the DA in the management of heart failure may seemingly be an additional strain on scarce health care resources, the results of this study demonstrate that, by increasing patient life expectancy by 1.28 years and reducing their hospitalization rate by 23\% when compared with usual care, the use of this technology may be seen as an investment, as HTM plus the DA in its current form extendedly dominates HTM alone and is cost-effective compared with usual care at normally accepted thresholds in the Netherlands. ",
doi="10.2196/31302",
url="https://cardio.jmir.org/2022/2/e31302",
url="http://www.ncbi.nlm.nih.gov/pubmed/35925670"
}


@Article{info:doi/10.2196/31230,
author="Santala, E. Onni
and Lipponen, A. Jukka
and J{\"a}ntti, Helena
and Rissanen, T. Tuomas
and Tarvainen, P. Mika
and Laitinen, P. Tomi
and Laitinen, M. Tiina
and Castr{\'e}n, Maaret
and V{\"a}liaho, Eemu-Samuli
and Rantula, A. Olli
and Naukkarinen, S. Noora
and Hartikainen, K. Juha E.
and Halonen, Jari
and Martikainen, J. Tero",
title="Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study",
journal="JMIR Cardio",
year="2022",
month="Jun",
day="21",
volume="6",
number="1",
pages="e31230",
keywords="atrial fibrillation",
keywords="heart rate variability",
keywords="HRV",
keywords="algorithm",
keywords="stroke",
keywords="mobile health",
keywords="mHealth",
keywords="Awario analysis Service, screening",
keywords="risk",
keywords="stroke risk",
keywords="heart rate",
keywords="feasibility",
keywords="reliability",
keywords="artificial intelligence",
keywords="mobile patch",
keywords="wearable",
keywords="arrhythmia",
keywords="screening",
abstract="Background: The detection of atrial fibrillation (AF) is a major clinical challenge as AF is often paroxysmal and asymptomatic. Novel mobile health (mHealth) technologies could provide a cost-effective and reliable solution for AF screening. However, many of these techniques have not been clinically validated. Objective: The purpose of this study is to evaluate the feasibility and reliability of artificial intelligence (AI) arrhythmia analysis for AF detection with an mHealth patch device designed for personal well-being. Methods: Patients (N=178) with an AF (n=79, 44\%) or sinus rhythm (n=99, 56\%) were recruited from the emergency care department. A single-lead, 24-hour, electrocardiogram-based heart rate variability (HRV) measurement was recorded with the mHealth patch device and analyzed with a novel AI arrhythmia analysis software. Simultaneously registered 3-lead electrocardiograms (Holter) served as the gold standard for the final rhythm diagnostics. Results: Of the HRV data produced by the single-lead mHealth patch, 81.5\% (3099/3802 hours) were interpretable, and the subject-based median for interpretable HRV data was 99\% (25th percentile=77\% and 75th percentile=100\%). The AI arrhythmia detection algorithm detected AF correctly in all patients in the AF group and suggested the presence of AF in 5 patients in the control group, resulting in a subject-based AF detection accuracy of 97.2\%, a sensitivity of 100\%, and a specificity of 94.9\%. The time-based AF detection accuracy, sensitivity, and specificity of the AI arrhythmia detection algorithm were 98.7\%, 99.6\%, and 98.0\%, respectively. Conclusions: The 24-hour HRV monitoring by the mHealth patch device enabled accurate automatic AF detection. Thus, the wearable mHealth patch device with AI arrhythmia analysis is a novel method for AF screening. Trial Registration: ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335 ",
doi="10.2196/31230",
url="https://cardio.jmir.org/2022/1/e31230",
url="http://www.ncbi.nlm.nih.gov/pubmed/35727618"
}


@Article{info:doi/10.2196/35615,
author="Kwun, Ju-Seung
and Yoon, Chang-Hwan
and Kim, Sun-Hwa
and Jeon, Ki-Hyun
and Kang, Si-Hyuck
and Lee, Wonjae
and Youn, Tae-Jin
and Chae, In-Ho",
title="Surveillance of Arrhythmia in Patients After Myocardial Infarction Using Wearable Electrocardiogram Patch Devices: Prospective Cohort Study",
journal="JMIR Cardio",
year="2022",
month="Jun",
day="9",
volume="6",
number="1",
pages="e35615",
keywords="myocardial infarction",
keywords="arrhythmia",
keywords="wearable electronic device",
keywords="wearable",
keywords="ECG",
keywords="electrocardiogram",
keywords="patch",
keywords="patch devices",
keywords="atrial fibrillation",
keywords="heart",
keywords="rhythm",
keywords="cardiology",
keywords="cardiologist",
keywords="cohort study",
keywords="tachycardia",
keywords="beta-blocker",
abstract="Background: Acute myocardial infarction may be associated with new-onset arrhythmias. Patients with myocardial infarction may manifest serious arrhythmias such as ventricular tachyarrhythmias or atrial fibrillation. Frequent, prolonged electrocardiogram (ECG) monitoring can prevent devastating outcomes caused by these arrhythmias. Objective: We aimed to investigate the incidence of arrhythmias in patients following myocardial infarction using a patch-type device---AT-Patch (ATP-C120; ATsens). Methods: This study is a nonrandomized, single-center, prospective cohort study. We evaluated 71 patients who had had a myocardial infarction and had been admitted to our hospital. The ATP-C120 device was attached to the patient for 11 days and analyzed by 2 cardiologists for new-onset arrhythmic events. Results: One participant was concordantly diagnosed with atrial fibrillation. The cardiologists diagnosed atrial premature beats in 65 (92\%) and 60 (85\%) of 71 participants, and ventricular premature beats in 38 (54\%) and 44 (62\%) participants, respectively. Interestingly, 40 (56\%) patients showed less than 2 minutes of sustained paroxysmal atrial tachycardia confirmed by both cardiologists. Among participants with atrial tachycardia, the use of $\beta$-blockers was significantly lower compared with patients without tachycardia (70\% vs 90\%, P=.04). However, different dosages of $\beta$-blockers did not make a significant difference. Conclusions: Wearable ECG monitoring patch devices are easy to apply and can correlate symptoms and ECG rhythm disturbances in patients following myocardial infarction. Further study is necessary regarding clinical implications and appropriate therapies for arrhythmias detected early after myocardial infarction to prevent adverse outcomes. ",
doi="10.2196/35615",
url="https://cardio.jmir.org/2022/1/e35615",
url="http://www.ncbi.nlm.nih.gov/pubmed/35679117"
}


@Article{info:doi/10.2196/cardio.8710,
author="Kropp, Caley
and Ellis, Jordan
and Nekkanti, Rajasekhar
and Sears, Samuel",
title="Monitoring Patients With Implantable Cardioverter Defibrillators Using Mobile Phone Electrocardiogram: Case Study",
journal="JMIR Cardio",
year="2018",
month="Feb",
day="21",
volume="2",
number="1",
pages="e5",
keywords="atrial fibrillation",
keywords="ICD",
keywords="ECG",
keywords="mobile phone monitoring",
keywords="mobile health",
keywords="electrophysiology",
abstract="Background: Preventable poor health outcomes associated with atrial fibrillation continue to make early detection a priority. A one-lead mobile electrocardiogram (mECG) device given to patients with an implantable cardioverter defibrillator (ICD) allowed users to receive real-time ECG readings in 30 seconds. Objective: Three cases were selected from an institutional review board-approved clinical trial aimed at assessing mECG device usage and satisfaction, patient engagement, quality of life (QoL), and cardiac anxiety. These three specific cases were selected to examine a variety of possible patient presentations and user experiences. Methods: Three ICD patients with mobile phones who were being seen in an adult device clinic were asked to participate. The participants chosen represented individuals with varying degrees of reported education and patient engagement. Participants were instructed to use the mECG device at least once per day for 30 days. Positive ECGs for atrial fibrillation were evaluated in clinic. At follow-up, information was collected regarding their frequency of use of the mECG device and three psychological outcomes in the domains of patient engagement, QoL, and cardiac anxiety. Results: Each patient used the technology approximately daily or every other day as prescribed. At the 30-day follow-up, usage reports indicated an average of 32 readings per month per participant. At 90-day follow-up, usage reports indicated an average of 34 readings per month per participant. Two of the three participants self-reported a significant improvement in their physical QoL from baseline to completion, while simultaneously self-reporting a significant decrease in their mental QoL. All three participants reported high levels of device acceptance and technology satisfaction. Conclusions: This case study demonstrates that ICD patients with varying degrees of education and patient engagement were relatively active in their use of mECGs. All three participants using the mECG technology reported high technology satisfaction and device acceptance. High sensitivity, specificity, and accuracy of mECG technology may allow routine atrial fibrillation screening at lower costs, in addition to improving patient outcomes. ",
doi="10.2196/cardio.8710",
url="http://cardio.jmir.org/2018/1/e5/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31758776"
}