JMIR Cardio最新文献

{"title":"Effective Prediction of Mortality by Heart Disease Among Women in Jordan Using the Chi-Squared Automatic Interaction Detection Model: Retrospective Validation Study.","authors":"Salam Bani Hani,&nbsp;Muayyad Ahmad","doi":"10.2196/48795","DOIUrl":"https://doi.org/10.2196/48795","url":null,"abstract":"<p><strong>Background: </strong>Many current studies have claimed that the actual risk of heart disease among women is equal to that in men. Using a large machine learning algorithm (MLA) data set to predict mortality in women, data mining techniques have been used to identify significant aspects of variables that help in identifying the primary causes of mortality within this target category of the population.</p><p><strong>Objective: </strong>This study aims to predict mortality caused by heart disease among women, using an artificial intelligence technique-based MLA.</p><p><strong>Methods: </strong>A retrospective design was used to retrieve big data from the electronic health records of 2028 women with heart disease. Data were collected for Jordanian women who were admitted to public health hospitals from 2015 to the end of 2021. We checked the extracted data for noise, consistency issues, and missing values. After categorizing, organizing, and cleaning the extracted data, the redundant data were eliminated.</p><p><strong>Results: </strong>Out of 9 artificial intelligence models, the Chi-squared Automatic Interaction Detection model had the highest accuracy (93.25%) and area under the curve (0.825) among the build models. The participants were 62.6 (SD 15.4) years old on average. Angina pectoris was the most frequent diagnosis in the women's extracted files (n=1,264,000, 62.3%), followed by congestive heart failure (n=764,000, 37.7%). Age, systolic blood pressure readings with a cutoff value of >187 mm Hg, medical diagnosis (women diagnosed with congestive heart failure were at a higher risk of death [n=31, 16.58%]), pulse pressure with a cutoff value of 98 mm Hg, and oxygen saturation (measured using pulse oximetry) with a cutoff value of 93% were the main predictors for death among women.</p><p><strong>Conclusions: </strong>To predict the outcomes in this study, we used big data that were extracted from the clinical variables from the electronic health records. The Chi-squared Automatic Interaction Detection model-an MLA-confirmed the precise identification of the key predictors of cardiovascular mortality among women and can be used as a practical tool for clinical prediction.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e48795"},"PeriodicalIF":0.0,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401188/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9998534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrocardiogram Devices for Home Use: Technological and Clinical Scoping Review.","authors":"Alejandra Zepeda-Echavarria, Rutger R van de Leur, Meike van Sleuwen, Rutger J Hassink, Thierry X Wildbergh, Pieter A Doevendans, Joris Jaspers, René van Es","doi":"10.2196/44003","DOIUrl":"10.2196/44003","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e44003"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362423/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9857189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smartphone-Based Remote Monitoring in Heart Failure With Reduced Ejection Fraction: Retrospective Cohort Study of Secondary Care Use and Costs.","authors":"Sameer Zaman, Yorissa Padayachee, Moulesh Shah, Jack Samways, Alice Auton, Nicholas M Quaife, Mark Sweeney, James P Howard, Indira Tenorio, Patrik Bachtiger, Tahereh Kamalati, Punam A Pabari, Nick W F Linton, Jamil Mayet, Nicholas S Peters, Carys Barton, Graham D Cole, Carla M Plymen","doi":"10.2196/45611","DOIUrl":"10.2196/45611","url":null,"abstract":"<p><strong>Background: </strong>Despite effective therapies, the economic burden of heart failure with reduced ejection fraction (HFrEF) is driven by frequent hospitalizations. Treatment optimization and admission avoidance rely on frequent symptom reviews and monitoring of vital signs. Remote monitoring (RM) aims to prevent admissions by facilitating early intervention, but the impact of noninvasive, smartphone-based RM of vital signs on secondary health care use and costs in the months after a new diagnosis of HFrEF is unknown.</p><p><strong>Objective: </strong>The purpose of this study is to conduct a secondary care health use and health-economic evaluation for patients with HFrEF using smartphone-based noninvasive RM and compare it with matched controls receiving usual care without RM.</p><p><strong>Methods: </strong>We conducted a retrospective study of 2 cohorts of newly diagnosed HFrEF patients, matched 1:1 for demographics, socioeconomic status, comorbidities, and HFrEF severity. They are (1) the RM group, with patients using the RM platform for >3 months and (2) the control group, with patients referred before RM was available who received usual heart failure care without RM. Emergency department (ED) attendance, hospital admissions, outpatient use, and the associated costs of this secondary care activity were extracted from the Discover data set for a 3-month period after diagnosis. Platform costs were added for the RM group. Secondary health care use and costs were analyzed using Kaplan-Meier event analysis and Cox proportional hazards modeling.</p><p><strong>Results: </strong>A total of 146 patients (mean age 63 years; 42/146, 29% female) were included (73 in each group). The groups were well-matched for all baseline characteristics except hypertension (P=.03). RM was associated with a lower hazard of ED attendance (hazard ratio [HR] 0.43; P=.02) and unplanned admissions (HR 0.26; P=.02). There were no differences in elective admissions (HR 1.03, P=.96) or outpatient use (HR 1.40; P=.18) between the 2 groups. These differences were sustained by a univariate model controlling for hypertension. Over a 3-month period, secondary health care costs were approximately 4-fold lower in the RM group than the control group, despite the additional cost of RM itself (mean cost per patient GBP £465, US $581 vs GBP £1850, US $2313, respectively; P=.04).</p><p><strong>Conclusions: </strong>This retrospective cohort study shows that smartphone-based RM of vital signs is feasible for HFrEF. This type of RM was associated with an approximately 2-fold reduction in ED attendance and a 4-fold reduction in emergency admissions over just 3 months after a new diagnosis with HFrEF. Costs were significantly lower in the RM group without increasing outpatient demand. This type of RM could be adjunctive to standard care to reduce admissions, enabling other resources to help patients unable to use RM.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e45611"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10334716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9773776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Outcomes After Heart Transplantation in Pediatric Patients Using National Registry Data: Evaluation of Machine Learning Approaches.","authors":"Michael O Killian,&nbsp;Shubo Tian,&nbsp;Aiwen Xing,&nbsp;Dana Hughes,&nbsp;Dipankar Gupta,&nbsp;Xiaoyu Wang,&nbsp;Zhe He","doi":"10.2196/45352","DOIUrl":"https://doi.org/10.2196/45352","url":null,"abstract":"<p><strong>Background: </strong>The prediction of posttransplant health outcomes for pediatric heart transplantation is critical for risk stratification and high-quality posttransplant care.</p><p><strong>Objective: </strong>The purpose of this study was to examine the use of machine learning (ML) models to predict rejection and mortality for pediatric heart transplant recipients.</p><p><strong>Methods: </strong>Various ML models were used to predict rejection and mortality at 1, 3, and 5 years after transplantation in pediatric heart transplant recipients using United Network for Organ Sharing data from 1987 to 2019. The variables used for predicting posttransplant outcomes included donor and recipient as well as medical and social factors. We evaluated 7 ML models-extreme gradient boosting (XGBoost), logistic regression, support vector machine, random forest (RF), stochastic gradient descent, multilayer perceptron, and adaptive boosting (AdaBoost)-as well as a deep learning model with 2 hidden layers with 100 neurons and a rectified linear unit (ReLU) activation function followed by batch normalization for each and a classification head with a softmax activation function. We used 10-fold cross-validation to evaluate model performance. Shapley additive explanations (SHAP) values were calculated to estimate the importance of each variable for prediction.</p><p><strong>Results: </strong>RF and AdaBoost models were the best-performing algorithms for different prediction windows across outcomes. RF outperformed other ML algorithms in predicting 5 of the 6 outcomes (area under the receiver operating characteristic curve [AUROC] 0.664 and 0.706 for 1-year and 3-year rejection, respectively, and AUROC 0.697, 0.758, and 0.763 for 1-year, 3-year, and 5-year mortality, respectively). AdaBoost achieved the best performance for prediction of 5-year rejection (AUROC 0.705).</p><p><strong>Conclusions: </strong>This study demonstrates the comparative utility of ML approaches for modeling posttransplant health outcomes using registry data. ML approaches can identify unique risk factors and their complex relationship with outcomes, thereby identifying patients considered to be at risk and informing the transplant community about the potential of these innovative approaches to improve pediatric care after heart transplantation. Future studies are required to translate the information derived from prediction models to optimize counseling, clinical care, and decision-making within pediatric organ transplant centers.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e45352"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10334720/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9829057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiac Rehabilitation Facebook Intervention: Feasibility Randomized Controlled Trial.","authors":"Lee Anne Siegmund,&nbsp;James F Bena,&nbsp;Shannon L Morrison","doi":"10.2196/46828","DOIUrl":"https://doi.org/10.2196/46828","url":null,"abstract":"<p><strong>Background: </strong>The adherence to cardiac rehabilitation is low. Social media has been used to improve motivation and cardiac rehabilitation completion, but the authors did not find Facebook interventions for these purposes in the literature.</p><p><strong>Objective: </strong>The purpose of this study was to determine the feasibility of the Cardiac Rehabilitation Facebook Intervention (Chat) for affecting changes in exercise motivation and need satisfaction and adherence to cardiac rehabilitation.</p><p><strong>Methods: </strong>The Behavioral Regulation in Exercise Questionnaire-3 and Psychological Need Satisfaction for Exercise were used to measure motivation and need satisfaction (competence, autonomy, and relatedness) before and after the Chat intervention. To support need satisfaction, the intervention included educational posts, supportive posts, and interaction with peers. The feasibility measures included recruitment, engagement, and acceptability. Groups were compared using analysis of variance and Kruskal-Wallis tests. Paired t tests were used to assess motivation and need satisfaction change, and Pearson or Spearman correlations were used for continuous variables.</p><p><strong>Results: </strong>A total of 32 participants were lost to follow-up and 22 were included in the analysis. Higher motivation at intake (relative autonomy index 0.53, 95% CI 0.14-0.78; P=.01) and change in need satisfaction-autonomy (relative autonomy index 0.61, 95% CI 0.09-0.87; P=.02) were associated with more completed sessions. No between-group differences were found. Engagement included \"likes\" (n=210) and \"hits\" (n=157). For acceptability, mean scores on a 1 (not at all) to 5 (quite a bit) Likert scale for feeling supported and in touch with providers were 4.6 and 4.4, respectively.</p><p><strong>Conclusions: </strong>Acceptability of the Chat group was high; however, intervention feasibility could not be determined due to the small sample size. Those with greater motivation at intake completed more sessions, indicating its importance in cardiac rehabilitation completion. Despite challenges with recruitment and engagement, important lessons were learned.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov NCT02971813; https://clinicaltrials.gov/ct2/show/NCT02971813.</p><p><strong>International registered report identifier (irrid): </strong>RR2-10.2196/resprot.7554.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e46828"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10334713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9763248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determining Optimal Intervals for In-Person Visits During Video-Based Telemedicine Among Patients With Hypertension: Cluster Randomized Controlled Trial.","authors":"Yuji Nishizaki,&nbsp;Haruo Kuroki,&nbsp;So Ishii,&nbsp;Shigeyuki Ohtsu,&nbsp;Chizuru Watanabe,&nbsp;Hiroto Nishizawa,&nbsp;Masashi Nagao,&nbsp;Masanori Nojima,&nbsp;Ryo Watanabe,&nbsp;Daisuke Sato,&nbsp;Kensuke Sato,&nbsp;Yumi Kawata,&nbsp;Hiroo Wada,&nbsp;Goichiro Toyoda,&nbsp;Katsumi Ohbayashi","doi":"10.2196/45230","DOIUrl":"https://doi.org/10.2196/45230","url":null,"abstract":"<p><strong>Background: </strong>Introducing telemedicine in outpatient treatment may improve patient satisfaction and convenience. However, the optimal in-person visit interval for video-based telemedicine among patients with hypertension remains unreported in Japan.</p><p><strong>Objective: </strong>We determined the optimal in-person visit interval for video-based telemedicine among patients with hypertension.</p><p><strong>Methods: </strong>This was a cluster randomized controlled noninferiority trial. The target sites were 8 clinics in Japan that had a telemedicine system, and the target patients were individuals with essential hypertension. Among patients receiving video-based telemedicine, those who underwent in-person visits at 6-month intervals were included in the intervention group, and those who underwent in-person visits at 3-month intervals were included in the control group. The follow-up period of the participants was 6 months. The primary end point of the study was the change in systolic blood pressure, and the secondary end points were the rate of treatment continuation after 6 months, patient satisfaction, health economic evaluation, and safety evaluation.</p><p><strong>Results: </strong>Overall, 64 patients were enrolled. Their mean age was 54.5 (SD 10.3) years, and 60.9% (39/64) of patients were male. For the primary end point, the odds ratio for the estimated difference in the change in systolic blood pressure between the 2 groups was 1.18 (90% CI -3.68 to 6.04). Notably, the criteria for noninferiority were met. Patient satisfaction was higher in the intervention group than in the control group. Furthermore, the indirect costs indicated that lost productivity was significantly lesser in the intervention group than in the control group. Moreover, the treatment continuation rate did not differ between the intervention and control groups, and there were no adverse events in either group.</p><p><strong>Conclusions: </strong>Blood pressure control status and safety did not differ between the intervention and control groups. In-person visits at 6-month intervals may cause a societal cost reduction and improve patient satisfaction during video-based telemedicine.</p><p><strong>Trial registration: </strong>UMIN Clinical Trials Registry (UMIN-CTR) UMIN000040953; https://tinyurl.com/2p8devm9.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e45230"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9706101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing the Efficacy of Targeted and Blast Portal Messaging in Message Opening Rate and Anticoagulation (AC) Initiation in Patients with Atrial Fibrillation in Preventing Preventable Strokes Study II: Prospective Cohort Study (Preprint)","authors":"Alok Kapoor, Parth Patel, Soumya Chennupati, Daniel Mbusa, H. Sadiq, Sanjeev Rampam, Robert Leung, Megan Miller, Kevin Rivera Vargas, Patrick Fry, M. Lowe, Christina Catalano, Charles Harrison, John Catanzaro, Sybil Crawford, Anne Marie Smith","doi":"10.2196/49590","DOIUrl":"https://doi.org/10.2196/49590","url":null,"abstract":"","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139370576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Outcomes of a Virtual Cardiac Rehabilitation Program for Patients Undergoing Atrial Fibrillation Ablation: A Pilot Study (Preprint)","authors":"Satish Misra, Karen Niazi, Kamala Swayampakala, Amanda Brown, Melissa Lang, Elizabeth Davenport, Sherry Saxonhouse, John Fedor, Brian Powell, Joseph Thompson, John Holshouser, Rohit Mehta","doi":"10.2196/49345","DOIUrl":"https://doi.org/10.2196/49345","url":null,"abstract":"","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136345719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continuous Data-Driven Monitoring in Critical Congenital Heart Disease: Clinical Deterioration Model Development.","authors":"Ruben S Zoodsma,&nbsp;Rian Bosch,&nbsp;Thomas Alderliesten,&nbsp;Casper W Bollen,&nbsp;Teus H Kappen,&nbsp;Erik Koomen,&nbsp;Arno Siebes,&nbsp;Joppe Nijman","doi":"10.2196/45190","DOIUrl":"https://doi.org/10.2196/45190","url":null,"abstract":"<p><strong>Background: </strong>Critical congenital heart disease (cCHD)-requiring cardiac intervention in the first year of life for survival-occurs globally in 2-3 of every 1000 live births. In the critical perioperative period, intensive multimodal monitoring at a pediatric intensive care unit (PICU) is warranted, as their organs-especially the brain-may be severely injured due to hemodynamic and respiratory events. These 24/7 clinical data streams yield large quantities of high-frequency data, which are challenging in terms of interpretation due to the varying and dynamic physiology innate to cCHD. Through advanced data science algorithms, these dynamic data can be condensed into comprehensible information, reducing the cognitive load on the medical team and providing data-driven monitoring support through automated detection of clinical deterioration, which may facilitate timely intervention.</p><p><strong>Objective: </strong>This study aimed to develop a clinical deterioration detection algorithm for PICU patients with cCHD.</p><p><strong>Methods: </strong>Retrospectively, synchronous per-second data of cerebral regional oxygen saturation (rSO₂) and 4 vital parameters (respiratory rate, heart rate, oxygen saturation, and invasive mean blood pressure) in neonates with cCHD admitted to the University Medical Center Utrecht, the Netherlands, between 2002 and 2018 were extracted. Patients were stratified based on mean oxygen saturation during admission to account for physiological differences between acyanotic and cyanotic cCHD. Each subset was used to train our algorithm in classifying data as either stable, unstable, or sensor dysfunction. The algorithm was designed to detect combinations of parameters abnormal to the stratified subpopulation and significant deviations from the patient's unique baseline, which were further analyzed to distinguish clinical improvement from deterioration. Novel data were used for testing, visualized in detail, and internally validated by pediatric intensivists.</p><p><strong>Results: </strong>A retrospective query yielded 4600 hours and 209 hours of per-second data in 78 and 10 neonates for, respectively, training and testing purposes. During testing, stable episodes occurred 153 times, of which 134 (88%) were correctly detected. Unstable episodes were correctly noted in 46 of 57 (81%) observed episodes. Twelve expert-confirmed unstable episodes were missed in testing. Time-percentual accuracy was 93% and 77% for, respectively, stable and unstable episodes. A total of 138 sensorial dysfunctions were detected, of which 130 (94%) were correct.</p><p><strong>Conclusions: </strong>In this proof-of-concept study, a clinical deterioration detection algorithm was developed and retrospectively evaluated to classify clinical stability and instability, achieving reasonable performance considering the heterogeneous population of neonates with cCHD. Combined analysis of baseline (ie, patient-specific) deviations","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e45190"},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10230358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9613630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Association Between Mobile App Use and Change in Functional Capacity Among Cardiac Rehabilitation Participants: Cohort Study.","authors":"Janah May Oclaman, Michelle L Murray, Donald J Grandis, Alexis L Beatty","doi":"10.2196/44433","DOIUrl":"10.2196/44433","url":null,"abstract":"<p><strong>Background: </strong>Cardiac rehabilitation (CR) is underused in the United States and globally, with participation disparities across gender, socioeconomic status, race, and ethnicities. The pandemic led to greater adoption of telehealth CR and mobile app use.</p><p><strong>Objective: </strong>Our primary objective was to estimate the association between CR mobile app use and change in functional capacity from enrollment to completion in patients participating in a CR program that offered in-person, hybrid, and telehealth CR. Our secondary objectives were to study the association between mobile app use and changes in blood pressure (BP) or program completion.</p><p><strong>Methods: </strong>We conducted a retrospective cohort study of participants enrolled in CR at an urban CR program in the United States. Participants were English speaking, at least 18 years of age, participated in the program between May 22, 2020, and May 21, 2022, and downloaded the CR mobile app. Mobile app use was quantified by number of exercise logs, vitals logs, and education material views. The primary outcome was change in functional capacity, measured by change in 6-minute walk distance (6MWD) from enrollment to completion. The secondary outcome was change in BP from enrollment to completion. We estimated associations using multivariable linear or logistic regression models adjusted for age, sex, race, ethnicity, socioeconomic status by ZIP code, insurance, and primary diagnosis for CR referral.</p><p><strong>Results: </strong>A total of 107 participants (mean age 62.9, SD 13.02 years; 90/107, 84.1% male; and 57/105, 53.3% self-declared as White Caucasian) used the mobile app and completed the CR program. Participants had a mean 64.0 (SD 54.1) meter increase in 6MWD between enrollment and completion (P<.001). From enrollment to completion, participants with an elevated BP at baseline (≥130/80 mmHg) experienced a significant decrease in BP (systolic BP -11.5 mmHg; P=.002 and diastolic BP -7.7 mmHg; P=.003). We found no significant association between total app interactions and change in 6MWD (coefficient -0.03, 95% CI -0.1 to 0.07; P=.59) or change in BP (systolic coefficient 0.002, 95% CI -0.03 to 0.03; P=.87 and diastolic coefficient -0.005, 95% CI -0.03 to 0.02; P=.65). There was no significant association between total exercise logs and change in 6MWD (coefficient 0.1, 95% CI -0.3 to 0.4; P=.57) or total BP logs and change in BP (systolic coefficient -0.02, 95% CI -0.1 to 0.06; P=.63 and diastolic coefficient -0.02, 95% CI -0.09 to 0.04; P=.50). There was no significant association between total app interactions and completion of CR (adjusted odds ratio 1.00, 95% CI 0.99-1.01; P=.44).</p><p><strong>Conclusions: </strong>CR mobile app use as part of an in-person, hybrid, or telehealth CR program was not associated with greater improvement in functional capacity or BP or with program completion.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e44433"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10227697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9555396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}