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Journal Description

JMIR Cardio is an open access journal. It covers electronic / digital health approaches in cardiology and for cardiovascular health, which includes ehealth and mhealth approaches for the prevention and treatment of cardiovascular conditions.

JMIR Cardio is also the official journal of the European Congress on eCardiology and eHealth. Best papers presented at the conference are selected for JMIR Cardio and as official partner organization, JMIR authors receive a discount (Promo Code: JMIRECARDIO17).

JMIR Cardio is indexed in PubMed and PubMed Central.

 

Recent Articles:

  • Source: Mar Gomis-Pastor; Copyright: Mar Gomis-Pastor; URL: http://cardio.jmir.org/2020/1/e19065/; License: Creative Commons Attribution (CC-BY).

    Interdisciplinary Mobile Health Model to Improve Clinical Care After Heart Transplantation: Implementation Strategy Study

    Abstract:

    Background: Solid organ transplantation could be the only life-saving treatment for end-stage heart failure. Nevertheless, multimorbidity and polypharmacy remain major problems after heart transplant. A technology-based behavioral intervention model was established to improve clinical practice in a heart transplant outpatient setting. To support the new strategy, the mHeart app, a mobile health (mHealth) tool, was developed for use by patients and providers. Objective: The primary objective of this study was to describe the implementation of the mHeart model and to outline the main facilitators identified when conceiving an mHealth approach. The secondary objectives were to evaluate the barriers, benefits, and willingness to use mHealth services reported by heart transplant recipients and cardiology providers. Methods: This was an implementation strategy study directed by a multidisciplinary cardiology team conducted in four stages: design of the model and the software, development of the mHeart tool, interoperability among systems, and quality and security requirements. A mixed methods study design was applied combining a literature review, several surveys, interviews, and focus groups. The approach involved merging engineering and behavioral theory science. Participants were chronic-stage heart transplant recipients, patient associations, health providers, stakeholders, and diverse experts from the legal, data protection, and interoperability fields. Results: An interdisciplinary and patient-centered process was applied to obtain a comprehensive care model. The heart transplant recipients (N=135) included in the study confirmed they had access to smartphones (132/135, 97.7%) and were willing to use the mHeart system (132/135, 97.7%). Based on stakeholder agreement (>75%, N=26), the major priorities identified of the mHealth approach were to improve therapy management, patient empowerment, and patient-provider interactions. Stakeholder agreement on the barriers to implementing the system was weak (<75%). Establishing the new model posed several challenges to the multidisciplinary team in charge. The main factors that needed to be overcome were ensuring data confidentiality, reducing workload, minimizing the digital divide, and increasing interoperability. Experts from various fields, scientific societies, and patient associations were essential to meet the quality requirements and the model scalability. Conclusions: The mHeart model will be applicable in distinct clinical and research contexts, and may inspire other cardiology health providers to create innovative ways to deal with therapeutic complexity and multimorbidity through health care systems. Professionals and patients are willing to use such innovative mHealth programs. The facilitators and key strategies described were needed for success in the implementation of the new holistic theory–based mHealth strategy.

  • Source: PhotoPin; Copyright: Nicola; URL: http://www.flickr.com/photos/15216811@N06/14321766378; License: Creative Commons Attribution (CC-BY).

    Using an Electronic App to Promote Home-Based Self-Care in Older Patients With Heart Failure: Qualitative Study on Patient and Informal Caregiver Challenges

    Abstract:

    Background: Heart failure (HF) affects many older individuals in North America, with recurrent hospitalizations despite postdischarge strategies to prevent readmission. Proper HF self-care can potentially lead to better clinical outcomes, yet many older patients find self-care challenging. Mobile health (mHealth) apps can provide support to patients with respect to HF self-care. However, many mHealth apps are not designed to consider potential patient barriers, such as literacy, numeracy, and cognitive impairment, leading to challenges for older patients. We previously demonstrated that a paper-based standardized diuretic decision support tool (SDDST) with daily weights and adjustment of diuretic dose led to improved self-care. Objective: The aim of this study is to better understand the self-care challenges that older patients with HF and their informal care providers (CPs) face on a daily basis, leading to the conversion of the SDDST into a user-centered mHealth app. Methods: We recruited 14 patients (male: 8/14, 57%) with a confirmed diagnosis of HF, aged ≥60 years, and 7 CPs from the HF clinic and the cardiology ward at the Hamilton General Hospital. Patients were categorized into 3 groups based on the self-care heart failure index: patients with adequate self-care, patients with inadequate self-care without a CP, or patients with inadequate self-care with a CP. We conducted semistructured interviews with patients and their CPs using persona-scenarios. Interviews were transcribed verbatim and analyzed for emerging themes using an inductive approach. Results: Six themes were identified: usability of technology, communication, app customization, complexity of self-care, usefulness of HF-related information, and long-term use and cost. Many of the challenges patients and CPs reported involved their unfamiliarity with technology and the lack of incentive for its use. However, participants were supportive and more likely to actively use the HF app when informed of the intervention’s inclusion of volunteer and nurse assistance. Conclusions: Patients with varying self-care adequacy levels were willing to use an mHealth app if it was simple in its functionality and user interface. To promote the adoption and usability of these tools, patients confirmed the need for researchers to engage with end users before developing an app. Findings from this study can be used to help inform the design of an mHealth app to ensure that it is adapted for the needs of older individuals with HF.

  • Source: Image created by the authors; Copyright: The Authors; URL: http://cardio.jmir.org/2020/1/e21962/; License: Creative Commons Attribution (CC-BY).

    Impact of Remote Titration Combined With Telemonitoring on the Optimization of Guideline-Directed Medical Therapy for Patients With Heart Failure: Internal...

    Abstract:

    Background: To improve health outcomes in patients with heart failure, guideline-directed medical therapy (GDMT) should be optimized to target doses. However, GDMT remains underutilized, with less than 25% of patients receiving target doses in clinical practice. Telemonitoring could provide reliable and real-time physiological data for clinical decision support to facilitate remote GDMT titration. Objective: This paper aims to present findings from an internal pilot study regarding the effectiveness of remote titration facilitated by telemonitoring. Methods: A 2-arm randomized controlled pilot trial comparing remote titration versus standard care in a heart function clinic was conducted. Patients were randomized to undergo remote medication titration facilitated by data from a smartphone-based telemonitoring system or standard titration performed during clinic visits. Results: A total of 42 patients with new-onset (10/42, 24%) and existing (32/42, 76%) heart failure and a mean age of 55.29 (SD 11.28) years were randomized between January and June 2019. Within 6 months of enrollment, 86% (18/21) of patients in the intervention group achieved optimal doses versus 48% (10/21) of patients in the control group. The median time to dose optimization was 11.0 weeks for the intervention group versus 18.8 weeks for the control group. The number of in-person visits in the intervention group was 54.5% lower than in the control group. Conclusions: The results of this pilot study suggest that remote titration facilitated by telemonitoring has the potential to increase the proportion of patients who achieve optimal GDMT doses, decrease time to dose optimization, and reduce the number of clinic visits. Remote titration may facilitate optimal and efficient titration of patients with heart failure while reducing the burden for patients to attend in-person clinic visits. Trial Registration: ClinicalTrials.gov NCT04205513; https://clinicaltrials.gov/ct2/show/NCT04205513

  • Source: The Authors / Placeit; Copyright: The Authors / Placeit; URL: http://cardio.jmir.org/2020/1/e17162/; License: Licensed by JMIR.

    The Atrial Fibrillation Health Literacy Information Technology Trial: Pilot Trial of a Mobile Health App for Atrial Fibrillation

    Abstract:

    Background: Atrial fibrillation (AF) is a common arrhythmia that adversely affects health-related quality of life (HRQoL). We conducted a pilot trial of individuals with AF using a smartphone to provide a relational agent as well as rhythm monitoring. We employed our pilot to measure acceptability and adherence and to assess its effectiveness in improving HRQoL and adherence. Objective: This study aims to measure acceptability and adherence and to assess its effectiveness to improve HRQoL and adherence. Methods: Participants were recruited from ambulatory clinics and randomized to a 30-day intervention or usual care. We collected baseline characteristics and conducted baseline and 30-day assessments of HRQoL using the Atrial Fibrillation Effect on Quality of Life (AFEQT) measure and self-reported adherence to anticoagulation. The intervention consisted of a smartphone-based relational agent, which simulates face-to-face counseling and delivered content on AF education, adherence, and symptom monitoring with prompted rhythm monitoring. We compared differences in AFEQT and adherence at 30 days, adjusted for baseline values. We quantified participants’ use and acceptability of the intervention. Results: A total of 120 participants were recruited and randomized (59 to control and 61 to intervention) to the pilot trial (mean age 72.1 years, SD 9.10; 62/120, 51.7% women). The control group had a 95% follow-up, and the intervention group had a 93% follow-up. The intervention group demonstrated significantly higher improvement in total AFEQT scores (adjusted mean difference 4.5; 95% CI 0.6-8.3; P=.03) and in daily activity (adjusted mean difference 7.1; 95% CI 1.8-12.4; P=.009) compared with the control between baseline and 30 days. The intervention group showed significantly improved self-reported adherence to anticoagulation therapy at 30 days (intervention 3.5%; control 23.2%; adjusted difference 16.6%; 95% CI 2.8%-30.4%; P<.001). Qualitative assessments of acceptability identified that participants found the relational agent useful, informative, and trustworthy. Conclusions: Individuals randomized to a 30-day smartphone intervention with a relational agent and rhythm monitoring showed significant improvement in HRQoL and adherence. Participants had favorable acceptability of the intervention with both objective use and qualitative assessments of acceptability.

  • Untitled. Source: Pexels; Copyright: Cristian Dina; URL: https://www.pexels.com/photo/white-smartphone-1851415/; License: Licensed by JMIR.

    “I Like the Idea of It…But Probably Wouldn’t Use It” - Health Care Provider Perspectives on Heart Failure mHealth: Qualitative Study

    Abstract:

    Background: Many mobile health (mHealth) technologies exist for patients with heart failure (HF). However, HF mhealth lacks evidence of efficacy, caregiver involvement, and clinically useful real-time data. Objective: We aim to capture health care providers’ perceived value of HF mHealth, particularly for pairing patient–caregiver-generated data with clinical intervention to inform the design of future HF mHealth. Methods: This study is a subanalysis of a larger qualitative study based on interviewing patients with HF, their caregivers, and health care providers. This analysis included interviews with health care providers (N=20), focusing on their perceived usefulness of HF mHealth tools and interventions. Results: A total of 5 themes emerged: (1) bio-psychosocial-spiritual monitoring, (2) use of sensors, (3) interoperability, (4) data sharing, and (5) usefulness of patient-reported outcomes in practice. Providers remain interested in mHealth technologies for HF patients and their caregivers. However, providers report being unconvinced of the clinical usefulness of robust real-time patient-reported outcomes. Conclusions: The use of assessments, sensors, and real-time data collection could provide value in patient care. Future research must continually explore how to maximize the utility of mHealth for HF patients, their caregivers, and health care providers.

  • Source: Unsplash; Copyright: Myriam Jessier; URL: https://unsplash.com/photos/3n3Or1UMnVQ; License: Licensed by JMIR.

    Google Trends Insights Into Reduced Acute Coronary Syndrome Admissions During the COVID-19 Pandemic: Infodemiology Study

    Abstract:

    Background: During the coronavirus disease (COVID-19) pandemic, a reduction in the presentation of acute coronary syndrome (ACS) has been noted in several countries. However, whether these trends reflect a reduction in ACS incidence or a decrease in emergency room visits is unknown. Using Google Trends, queries for chest pain that have previously been shown to closely correlate with coronary heart disease were compared with searches for myocardial infarction and COVID-19 symptoms. Objective: The current study evaluates if search terms (or topics) pertaining to chest pain symptoms correlate with the reported decrease in presentations of ACS. Methods: Google Trends data for search terms “chest pain,” “myocardial infarction,” “cough,” and “fever” were obtained from June 1, 2019, to May 31, 2020. Related queries were evaluated for a relationship to coronary heart disease. Results: Following the onset of the COVID-19 pandemic, chest pain searches increased in all countries studied by at least 34% (USA P=.003, Spain P=.007, UK P=.001, Italy P=.002), while searches for myocardial infarction dropped or remained unchanged. Rising searches for chest pain included “coronavirus chest pain,” “home remedies for chest pain,” and “natural remedies for chest pain.” Searches on COVID-19 symptoms (eg, cough, fever) rose initially but returned to baseline while chest pain–related searches remained elevated throughout May. Conclusions: Search engine queries for chest pain have risen during the pandemic as have related searches with alternative attribution for chest pain or home care for chest pain, suggesting that recent drops in ACS presentations may be due to patients avoiding the emergency room and potential treatment in the midst of the COVID-19 pandemic.

  • Source: Image created by the Authors; Copyright: The Authors; URL: https://cardio.jmir.org/2020/1/e16354; License: Creative Commons Attribution (CC-BY).

    Assisting Home-Based Resistance Training for Normotensive and Prehypertensive Individuals Using Ambient Lighting and Sonification Feedback: Sensor-Based...

    Abstract:

    Background: Physical exercise is an effective lifestyle intervention to improve blood pressure. Although aerobic sports can be performed anywhere, resistance exercises are traditionally performed at the gym; extending the latter to the home setting may promote an increase in the number of practitioners. Objective: This study aims to evaluate a sensor-based system that guides resistance exercises through ambient lighting and sonification (A/S) feedback in a home setting in 34 study participants who were normotensive and prehypertensive. Methods: Participants took part in a 1.5-hour exercise session in which they experienced the A/S feedback (ie, experimental condition) as well as a control condition (ie, no feedback) and a reference condition (ie, verbal feedback through a human remote coach). The system was evaluated for improving exercise form (range of motion, timing, and breathing patterns) as well as psychophysiological experience (perceived exertion, attentional focus, competence, and motivation). Results: A/S feedback was significantly better than the control for concentric (mean 2.48, SD 0.75 seconds; P<.001) and eccentric (mean 2.92, SD 1.05 seconds; P<.001) contraction times, concentric range of motion consistency (mean 15.64, SD 8.31 cm vs mean 17.94, SD 9.75 cm; P<.001), and perceived exertion (mean 3.37, SD 0.78 vs mean 3.64, SD 0.76; P<.001). However, A/S feedback did not outperform verbal feedback on any of these measures. The breathing technique was best in the control condition (ie, without any feedback). Participants did not show more positive changes in perceived competence with A/S feedback or verbal feedback. Conclusions: The system seemed to improve resistance exercise execution and perception in comparison with the control, but did not outperform a human tele-coach. Further research is warranted to improve the breathing technique.

  • Stress echocardiography in action (top); contrast enhanced echo images (bottom). Source: Image created by the Authors; Copyright: The Authors; URL: http://cardio.jmir.org/2020/1/e16975/; License: Creative Commons Attribution (CC-BY).

    Minimal Patient Clinical Variables to Accurately Predict Stress Echocardiography Outcome: Validation Study Using Machine Learning Techniques

    Abstract:

    Background: Stress echocardiography is a well-established diagnostic tool for suspected coronary artery disease (CAD). Cardiovascular risk factors are used in the assessment of the probability of CAD. The link between the outcome of stress echocardiography and patients’ variables including risk factors, current medication, and anthropometric variables has not been widely investigated. Objective: This study aimed to use machine learning to predict significant CAD defined by positive stress echocardiography results in patients with chest pain based on anthropometrics, cardiovascular risk factors, and medication as variables. This could allow clinical prioritization of patients with likely prediction of CAD, thus saving clinician time and improving outcomes. Methods: A machine learning framework was proposed to automate the prediction of stress echocardiography results. The framework consisted of four stages: feature extraction, preprocessing, feature selection, and classification stage. A mutual information–based feature selection method was used to investigate the amount of information that each feature carried to define the positive outcome of stress echocardiography. Two classification algorithms, support vector machine (SVM) and random forest classifiers, have been deployed. Data from 529 patients were used to train and validate the framework. Patient mean age was 61 (SD 12) years. The data consists of anthropological data and cardiovascular risk factors such as gender, age, weight, family history, diabetes, smoking history, hypertension, hypercholesterolemia, prior diagnosis of CAD, and prescribed medications at the time of the test. There were 82 positive (abnormal) and 447 negative (normal) stress echocardiography results. The framework was evaluated using the whole dataset including cases with prior diagnosis of CAD. Five-fold cross-validation was used to validate the performance of the framework. We also investigated the model in the subset of patients with no prior CAD. Results: The feature selection methods showed that prior diagnosis of CAD, sex, and prescribed medications such as angiotensin-converting enzyme inhibitor/angiotensin receptor blocker were the features that shared the most information about the outcome of stress echocardiography. SVM classifiers showed the best trade-off between sensitivity and specificity and was achieved with three features. Using only these three features, we achieved an accuracy of 67.63% with sensitivity and specificity 72.87% and 66.67% respectively. However, for patients with no prior diagnosis of CAD, only two features (sex and angiotensin-converting enzyme inhibitor/angiotensin receptor blocker use) were needed to achieve accuracy of 70.32% with sensitivity and specificity at 70.24%. Conclusions: This study shows that machine learning can predict the outcome of stress echocardiography based on only a few features: patient prior cardiac history, gender, and prescribed medication. Further research recruiting higher number of patients who underwent stress echocardiography could further improve the performance of the proposed algorithm with the potential of facilitating patient selection for early treatment/intervention avoiding unnecessary downstream testing.

  • Source: Image created by the Authors; Copyright: The Authors; URL: http://cardio.jmir.org/2020/1/e12141/; License: Creative Commons Attribution (CC-BY).

    The Added Value of In-Hospital Tracking of the Efficacy of Decongestion Therapy and Prognostic Value of a Wearable Thoracic Impedance Sensor in Acutely...

    Abstract:

    Background: Incomplete relief of congestion in acute decompensated heart failure (HF) is related to poor outcomes. However, congestion can be difficult to evaluate, stressing the urgent need for new objective approaches. Due to its inverse correlation with tissue hydration, continuous bioimpedance monitoring might be an effective method for serial fluid status assessments. Objective: This study aimed to determine whether in-hospital bioimpedance monitoring can be used to track fluid changes (ie, the efficacy of decongestion therapy) and the relationships between bioimpedance changes and HF hospitalization and all-cause mortality. Methods: A wearable bioimpedance monitoring device was used for thoracic impedance measurements. Thirty-six patients with signs of acute decompensated HF and volume overload were included. Changes in the resistance at 80 kHz (R80kHz) were analyzed, with fluid balance (fluid in/out) used as a reference. Patients were divided into two groups depending on the change in R80kHz during hospitalization: increase in R80kHz or decrease in R80kHz. Clinical outcomes in terms of HF rehospitalization and all-cause mortality were studied at 30 days and 1 year of follow-up. Results: During hospitalization, R80kHz increased for 24 patients, and decreased for 12 patients. For the total study sample, a moderate negative correlation was found between changes in fluid balance (in/out) and relative changes in R80kHz during hospitalization (rs=-0.51, P<.001). Clinical outcomes at both 30 days and 1 year of follow-up were significantly better for patients with an increase in R80kHz. At 1 year of follow-up, 88% (21/24) of patients with an increase in R80kHz were free from all-cause mortality, compared with 50% (6/12) of patients with a decrease in R80kHz (P=.01); 75% (18/24) and 25% (3/12) were free from all-cause mortality and HF hospitalization, respectively (P=.01). A decrease in R80kHz resulted in a significant hazard ratio of 4.96 (95% CI 1.82-14.37, P=.003) on the composite endpoint. Conclusions: The wearable bioimpedance device was able to track changes in fluid status during hospitalization and is a convenient method to assess the efficacy of decongestion therapy during hospitalization. Patients who do not show an improvement in thoracic impedance tend to have worse clinical outcomes, indicating the potential use of thoracic impedance as a prognostic parameter.

  • The picture shows that the pulse rate of the smart watch and the heart rate of the monitor ECG are simultaneously recorded at the onset of paroxysmal atrial fibrillation. Source: Image created by the Authors; Copyright: The Authors; URL: http://cardio.jmir.org/2020/1/e14857/; License: Creative Commons Attribution (CC-BY).

    Use of a Smart Watch for Early Detection of Paroxysmal Atrial Fibrillation: Validation Study

    Abstract:

    Background: Wearable devices with photoplethysmography (PPG) technology can be useful for detecting paroxysmal atrial fibrillation (AF), which often goes uncaptured despite being a leading cause of stroke. Objective: This study is the first part of a 2-phase study that aimed at developing a method for immediate detection of paroxysmal AF using PPG-integrated wearable devices. In this study, the diagnostic performance of 2 major smart watches, Apple Watch Series 3 and Fitbit (FBT) Charge HR Wireless Activity Wristband, each equipped with a PPG sensor, was compared, and the pulse rate data outputted from those devices were analyzed for precision and accuracy in reference to the heart rate data from electrocardiography (ECG) during AF. Methods: A total of 40 subjects from patients who underwent cardiac surgery at a single center between September 2017 and March 2018 were monitored for postoperative AF using telemetric ECG and PPG devices. AF was diagnosed using a 12-lead ECG by qualified physicians. Each subject was given a pair of smart watches, Apple Watch and FBT, for simultaneous pulse rate monitoring. The heart rate of all subjects was also recorded on the telemetry system. Time series pulse rate trends and heart rate trends were created and analyzed for trend pattern similarities. Those trend data were then used to determine the accuracy of PPG-based pulse rate measurements in reference to ECG-based heart rate measurements during AF. Results: Of the 20 AF events in group FBT, 6 (30%) showed a moderate or higher correlation (cross-correlation function>0.40) between pulse rate trend patterns and heart rate trend patterns. Of the 16 AF events in group Apple Watch (workout [W] mode), 12 (75%) showed a moderate or higher correlation between the 2 trend patterns. Linear regression analyses also showed a significant correlation between the pulse rates and the heart rates during AF in the subjects with Apple Watch. This correlation was not observed with FBT. The regression formula for Apple Watch W mode and FBT was X=14.203 + 0.841Y and X=58.225 + 0.228Y, respectively (where X denotes the mean of all average pulse rates during AF and Y denotes the mean of all corresponding average heart rates during AF), and the coefficient of determination (R2) was 0.685 and 0.057, respectively (P<.001 and .29, respectively). Conclusions: In this validation study, the detection precision of AF and measurement accuracy during AF were both better with Apple Watch W mode than with FBT.

  • Source: Flickr; Copyright: Timo Newton-Syms; URL: http://www.flickr.com/photos/19935963@N00/31731774776; License: Creative Commons Attribution + ShareAlike (CC-BY-SA).

    Using Mobile Health Tools to Assess Physical Activity Guideline Adherence and Smoking Urges: Secondary Analysis of mActive-Smoke

    Abstract:

    Background: Rates of cigarette smoking are decreasing because of public health initiatives, pharmacological aids, and clinician focus on smoking cessation. However, a sedentary lifestyle increases cardiovascular risk, and therefore, inactive smokers have a particularly enhanced risk of cardiovascular disease. Objective: In this secondary analysis of mActive-Smoke, a 12-week observational study, we investigated adherence to guideline-recommended moderate-to-vigorous physical activity (MVPA) in smokers and its association with the urge to smoke. Methods: We enrolled 60 active smokers (≥3 cigarettes per day) and recorded continuous step counts with the Fitbit Charge HR. MVPA was defined as a cadence of greater than or equal to 100 steps per minute. Participants were prompted to report instantaneous smoking urges via text message 3 times a day on a Likert scale from 1 to 9. We used a mixed effects linear model for repeated measures, controlling for demographics and baseline activity level, to investigate the association between MVPA and urge. Results: A total of 53 participants (mean age 40 [SD 12] years, 57% [30/53] women, 49% [26/53] nonwhite, and 38% [20/53] obese) recorded 6 to 12 weeks of data. Data from 3633 person-days were analyzed, with a mean of 69 days per participant. Among all participants, median daily MVPA was 6 min (IQR 2-13), which differed by sex (12 min [IQR 3-20] for men vs 3.5 min [IQR 1-9] for women; P=.004) and BMI (2.5 min [IQR 1-8.3] for obese vs 10 min [IQR 3-15] for nonobese; P=.04). The median total MVPA minutes per week was 80 (IQR 31-162). Only 10% (5/51; 95% CI 4% to 22%) of participants met national guidelines of 150 min per week of MVPA on at least 50% of weeks. Adjusted models showed no association between the number of MVPA minutes per day and mean daily smoking urge (P=.72). Conclusions: The prevalence of MVPA was low in adult smokers who rarely met national guidelines for MVPA. Given the poor physical activity attainment in smokers, more work is required to enhance physical activity in this population.

  • Source: Pexels; Copyright: Louis Bauer; URL: https://www.pexels.com/photo/close-up-of-leaf-249348/; License: Licensed by JMIR.

    Outsourcing the Remote Management of Cardiac Implantable Electronic Devices: Medical Care Quality Improvement Project

    Abstract:

    Background: Remote management is partially replacing routine follow-up in patients implanted with cardiac implantable electronic devices (CIEDs). Although it reduces clinical staff time compared with standard in-office follow-up, a new definition of roles and responsibilities may be needed to review remote transmissions in an effective, efficient, and timely manner. Whether remote triage may be outsourced to an external remote monitoring center (ERMC) is still unclear. Objective: The aim of this health care quality improvement project was to evaluate the feasibility of outsourcing remote triage to an ERMC to improve patient care and health care resource utilization. Methods: Patients (N=153) with implanted CIEDs were followed up for 8 months. An ERMC composed of nurses and physicians reviewed remote transmissions daily following a specific remote monitoring (RM) protocol. A 6-month benchmarking phase where patients’ transmissions were managed directly by hospital staff was evaluated as a term of comparison. Results: A total of 654 transmissions were recorded in the RM system and managed by the ERMC team within 2 working days, showing a significant time reduction compared with standard RM management (100% vs 11%, respectively, within 2 days; P<.001). A total of 84.3% (551/654) of the transmissions did not include a prioritized event and did not require escalation to the hospital clinician. High priority was assigned to 2.3% (15/654) of transmissions, which were communicated to the hospital team by email within 1 working day. Nonurgent device status events occurred in 88 cases and were communicated to the hospital within 2 working days. Of these, 11% (10/88) were followed by a hospitalization. Conclusions: The outsourcing of RM management to an ERMC safely provides efficacy and efficiency gains in patients’ care compared with a standard in-hospital management. Moreover, the externalization of RM management could be a key tool for saving dedicated staff and facility time with possible positive economic impact. Clinical Trial: ClinicalTrials.gov NCT01007474; http://clinicaltrials.gov/ct2/show/NCT01007474

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