Cardiovascular digital health journal最新文献

{"title":"A smartwatch-based CPR feedback device improves chest compression quality among health care professionals and lay rescuers","authors":"Adam S. LaPrad PhD ,&nbsp;Bridgid Joseph DPN, RN, CCNS ,&nbsp;Sara Chokshi PhD ,&nbsp;Kelly Aldrich DNP, MS, RN-BC ,&nbsp;David Kessler MD, MSc ,&nbsp;Beno W. Oppenheimer MD","doi":"10.1016/j.cvdhj.2024.03.006","DOIUrl":"10.1016/j.cvdhj.2024.03.006","url":null,"abstract":"<div><h3>Background</h3><p>Cardiopulmonary resuscitation (CPR) quality significantly impacts patient outcomes during cardiac arrests. With advancements in health care technology, smartwatch-based CPR feedback devices have emerged as potential tools to enhance CPR delivery.</p></div><div><h3>Objective</h3><p>This study evaluated a novel smartwatch-based CPR feedback device in enhancing chest compression quality among health care professionals and lay rescuers.</p></div><div><h3>Methods</h3><p>A single-center, open-label, randomized crossover study was conducted with 30 subjects categorized into 3 groups based on rescuer category. The Relay Response BLS smartwatch application was compared to a defibrillator-based feedback device (Zoll OneStep CPR Pads). Following an introduction to the technology, subjects performed chest compressions in 3 modules: baseline unaided, aided by the smartwatch-based feedback device, and aided by the defibrillator-based feedback device. Outcome measures included effectiveness, learnability, and usability.</p></div><div><h3>Results</h3><p>Across all groups, the smartwatch-based device significantly improved mean compression depth effectiveness (68.4% vs 29.7%; <em>P</em> &lt; .05) and mean rate effectiveness (87.5% vs 30.1%; <em>P</em> &lt; .05), compared to unaided compressions. Compression variability was significantly reduced with the smartwatch-based device (coefficient of variation: 14.9% vs 26.6%), indicating more consistent performance. Fifteen of 20 professional rescuers reached effective compressions using the smartwatch-based device in an average 2.6 seconds. A usability questionnaire revealed strong preference for the smartwatch-based device over the defibrillator-based device.</p></div><div><h3>Conclusion</h3><p>The smartwatch-based device enhances the quality of CPR delivery by keeping compressions within recommended ranges and reducing performance variability. Its user-friendliness and rapid learnability suggest potential for widespread adoption in both professional and lay rescuer scenarios, contributing positively to CPR training and real-life emergency responses.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"5 3","pages":"Pages 122-131"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266669362400029X/pdfft?md5=70e80c21c66f84b5799e882cf1629c69&pid=1-s2.0-S266669362400029X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140794674","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":"Point-of-care testing preferences 2020–2022: Trends over the years","authors":"Sakeina Howard-Wilson DO ,&nbsp;Ziyue Wang ,&nbsp;Taylor Orwig ,&nbsp;Denise Dunlap MS, PhD ,&nbsp;Nathaniel Hafer PhD ,&nbsp;Bryan Buchholz PhD ,&nbsp;Shiv Sutaria MD ,&nbsp;David D. McManus MD, ScM ,&nbsp;Craig M. Lilly MD","doi":"10.1016/j.cvdhj.2024.03.002","DOIUrl":"10.1016/j.cvdhj.2024.03.002","url":null,"abstract":"<div><h3>Background</h3><p>The use of point-of-care (POC) tests prior to the COVID-19 pandemic was relatively infrequent outside of the health care context. Little is known about how public opinions regarding POC tests have changed during the pandemic.</p></div><div><h3>Methods</h3><p>We redeployed a validated survey to uncompensated volunteers to assess preferences for point-of-care testing (POCT) benefits and concerns between June and September 2022. We received a total of 292 completed surveys. Linear regression analysis was used to compare differences in survey average response scores (ARSs) from 2020 to 2022.</p></div><div><h3>Results</h3><p>Respondent ARSs indicated agreement for all 16 POCT benefits in 2022. Of 14 POCT concerns, there were only 2 statements that respondents agreed with most frequently, which were that “Insurance might not cover the costs of the POC test” (ARS 0.9, ± 1.0) and “POC tests might not provide a definitive result” (ARS 0.1, ± 1.0). Additionally, when comparing survey responses from 2020 to 2022, we observed 8 significant trends for POCT harms and benefits.</p></div><div><h3>Conclusion</h3><p>The public’s opinion on POC tests has become more favorable over time. However, concerns regarding the affordability and reliability of POCT results persist. We suggest that stakeholders address these concerns by developing accurate POC tests that continue to improve care and facilitate access to health care for all.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"5 3","pages":"Pages 149-155"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693624000148/pdfft?md5=d2feee74a47388b571f02ec9e74c06e7&pid=1-s2.0-S2666693624000148-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140278395","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":"Determinants of global cardiac implantable electrical device remote monitoring utilization – Results from an international survey","authors":"Bert Vandenberk MD, PhD ,&nbsp;Neal Ferrick MD ,&nbsp;Elaine Y. Wan MD ,&nbsp;Sanjiv M. Narayan MD, PhD ,&nbsp;Aileen M. Ferrick PhD ,&nbsp;Satish R. Raj MD, MSCI","doi":"10.1016/j.cvdhj.2024.03.003","DOIUrl":"10.1016/j.cvdhj.2024.03.003","url":null,"abstract":"<div><h3>Background</h3><p>Despite near-global availability of remote monitoring (RM) in patients with cardiac implantable electronic devices (CIED), there is a high geographical variability in the uptake and use of RM. The underlying reasons for this geographic disparity remain largely unknown.</p></div><div><h3>Objectives</h3><p>To study the determinants of worldwide RM utilization and identify locoregional barriers of RM uptake.</p></div><div><h3>Methods</h3><p>An international survey was administered to all CIED clinic personnel using the Heart Rhythm Society global network collecting demographic information, as well as information on the use of RM, the organization of the CIED clinic, and details on local reimbursement and clinic funding. The most complete response from each center was included in the current analysis. Stepwise forward multivariate linear regression was performed to identify determinants of the percentage of patients with a CIED on RM.</p></div><div><h3>Results</h3><p>A total of 302 responses from 47 different countries were included, 61.3% by physicians and 62.3% from hospital-based CIED clinics. The median percentage of CIED patients on RM was 80% (interquartile range, 40–90). Predictors of RM use were gross national income per capita (0.76% per US$1000, 95% CI 0.72–1.00, <em>P</em> &lt; .001), office-based clinics (7.48%, 95% CI 1.53–13.44, <em>P</em> = .014), and presence of clinic funding (per-patient payment model 7.90% [95% CI 0.63–15.17, <em>P</em> = .033); global budget 3.56% (95% CI -6.14 to 13.25, <em>P</em> = .471]).</p></div><div><h3>Conclusion</h3><p>The high variability in RM utilization can partly be explained by economic and structural barriers that may warrant specific efforts by all stakeholders to increase RM utilization.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"5 3","pages":"Pages 141-148"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266669362400015X/pdfft?md5=7126c8593dc209a8a0c81fa9897f63c6&pid=1-s2.0-S266669362400015X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140271344","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":"Feasibility of remote monitoring for fatal coronary heart disease using Apple Watch ECGs","authors":"Liam Butler PhD ,&nbsp;Alexander Ivanov MD ,&nbsp;Turgay Celik MD ,&nbsp;Ibrahim Karabayir PhD ,&nbsp;Lokesh Chinthala MS ,&nbsp;Melissa M. Hudson MD ,&nbsp;Kiri K. Ness PhD ,&nbsp;Daniel A. Mulrooney MD, MS ,&nbsp;Stephanie B. Dixon MD, MPH ,&nbsp;Mohammad S. Tootooni PhD ,&nbsp;Adam J. Doerr MD ,&nbsp;Byron C. Jaeger PhD ,&nbsp;Robert L. Davis MD, MPH ,&nbsp;David D. McManus MD, ScM ,&nbsp;David Herrington MD, MHS ,&nbsp;Oguz Akbilgic DBA, PhD","doi":"10.1016/j.cvdhj.2024.03.007","DOIUrl":"10.1016/j.cvdhj.2024.03.007","url":null,"abstract":"<div><h3>Background</h3><p>Fatal coronary heart disease (FCHD) is often described as sudden cardiac death (affects &gt;4 million people/year), where coronary artery disease is the only identified condition. Electrocardiographic artificial intelligence (ECG-AI) models for FCHD risk prediction using ECG data from wearable devices could enable wider screening/monitoring efforts.</p></div><div><h3>Objectives</h3><p>To develop a single-lead ECG–based deep learning model for FCHD risk prediction and assess concordance between clinical and Apple Watch ECGs.</p></div><div><h3>Methods</h3><p>An FCHD single-lead (“lead I” from 12-lead ECGs) ECG-AI model was developed using 167,662 ECGs (50,132 patients) from the University of Tennessee Health Sciences Center. Eighty percent of the data (5-fold cross-validation) was used for training and 20% as a holdout. Cox proportional hazards (CPH) models incorporating ECG-AI predictions with age, sex, and race were also developed. The models were tested on paired clinical single-lead and Apple Watch ECGs from 243 St. Jude Lifetime Cohort Study participants. The correlation and concordance of the predictions were assessed using Pearson correlation (R), Spearman correlation (ρ), and Cohen’s kappa.</p></div><div><h3>Results</h3><p>The ECG-AI and CPH models resulted in AUC = 0.76 and 0.79, respectively, on the 20% holdout and AUC = 0.85 and 0.87 on the Atrium Health Wake Forest Baptist external validation data. There was moderate-strong positive correlation between predictions (R = 0.74, ρ = 0.67, and κ = 0.58) when tested on the 243 paired ECGs. The clinical (lead I) and Apple Watch predictions led to the same low/high-risk FCHD classification for 99% of the participants. CPH prediction correlation resulted in an R = 0.81, ρ = 0.76, and κ = 0.78.</p></div><div><h3>Conclusion</h3><p>Risk of FCHD can be predicted from single-lead ECGs obtained from wearable devices and are statistically concordant with lead I of a 12-lead ECG.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"5 3","pages":"Pages 115-121"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693624000306/pdfft?md5=75e33d0289fa6cf13b0db8075297b6a9&pid=1-s2.0-S2666693624000306-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140780704","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":"Use of digital health technologies in periprocedural pediatric cardiac ablation","authors":"Nathan Miller RN ,&nbsp;David Catherall MEng ,&nbsp;Anthony G. Pompa MD ,&nbsp;Lisa Roelle PA-C ,&nbsp;Tracy Conner MD ,&nbsp;William B. Orr MD ,&nbsp;Jennifer N. Avari Silva MD","doi":"10.1016/j.cvdhj.2024.03.004","DOIUrl":"https://doi.org/10.1016/j.cvdhj.2024.03.004","url":null,"abstract":"","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"5 3","pages":"Pages 173-177"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693624000276/pdfft?md5=7930ea8cba108acdf84139fd1e0d4f1d&pid=1-s2.0-S2666693624000276-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424469","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":"Artificial intelligence–based screening for cardiomyopathy in an obstetric population: A pilot study","authors":"Demilade Adedinsewo MD, MPH ,&nbsp;Andrea Carolina Morales-Lara MD ,&nbsp;Heather Hardway PhD ,&nbsp;Patrick Johnson BS ,&nbsp;Kathleen A. Young MD ,&nbsp;Wendy Tatiana Garzon-Siatoya MD ,&nbsp;Yvonne S. Butler Tobah MD ,&nbsp;Carl H. Rose MD ,&nbsp;David Burnette BS ,&nbsp;Kendra Seccombe APRN ,&nbsp;Mia Fussell BS ,&nbsp;Sabrina Phillips MD ,&nbsp;Francisco Lopez-Jimenez MD ,&nbsp;Zachi I. Attia PhD ,&nbsp;Paul A. Friedman MD ,&nbsp;Rickey E. Carter PhD ,&nbsp;Peter A. Noseworthy MD","doi":"10.1016/j.cvdhj.2024.03.005","DOIUrl":"10.1016/j.cvdhj.2024.03.005","url":null,"abstract":"<div><h3>Background</h3><p>Cardiomyopathy is a leading cause of pregnancy-related mortality and the number one cause of death in the late postpartum period. Delay in diagnosis is associated with severe adverse outcomes.</p></div><div><h3>Objective</h3><p>To evaluate the performance of an artificial intelligence–enhanced electrocardiogram (AI-ECG) and AI-enabled digital stethoscope to detect left ventricular systolic dysfunction in an obstetric population.</p></div><div><h3>Methods</h3><p>We conducted a single-arm prospective study of pregnant and postpartum women enrolled at 3 sites between October 28, 2021, and October 27, 2022. Study participants completed a standard 12-lead ECG, digital stethoscope ECG and phonocardiogram recordings, and a transthoracic echocardiogram within 24 hours. Diagnostic performance was evaluated using the area under the curve (AUC).</p></div><div><h3>Results</h3><p>One hundred women were included in the final analysis. The median age was 31 years (Q1: 27, Q3: 34). Thirty-eight percent identified as non-Hispanic White, 32% as non-Hispanic Black, and 21% as Hispanic. Five percent and 6% had left ventricular ejection fraction (LVEF) &lt;45% and &lt;50%, respectively. The AI-ECG model had near-perfect classification performance (AUC: 1.0, 100% sensitivity; 99%–100% specificity) for detection of cardiomyopathy at both LVEF categories. The AI-enabled digital stethoscope had an AUC of 0.98 (95% CI: 0.95, 1.00) and 0.97 (95% CI: 0.93, 1.00), for detection of LVEF &lt;45% and &lt;50%, respectively, with 100% sensitivity and 90% specificity.</p></div><div><h3>Conclusion</h3><p>We demonstrate an AI-ECG and AI-enabled digital stethoscope were effective for detecting cardiac dysfunction in an obstetric population. Larger studies, including an evaluation of the impact of screening on clinical outcomes, are essential next steps.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"5 3","pages":"Pages 132-140"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693624000288/pdfft?md5=8265564a5f2a14869fb74975a76af834&pid=1-s2.0-S2666693624000288-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140787503","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":"Cellular-Enabled Remote Patient Monitoring for Pregnancies Complicated by Hypertension","authors":"Rebecca D. Jones MPH,&nbsp;Cheng Peng PhD, MPA, MS,&nbsp;Crystal D. Jones RDCS, MHS,&nbsp;Brianna Long MS,&nbsp;Victoria Helton MD,&nbsp;Hari Eswaran PhD","doi":"10.1016/j.cvdhj.2024.03.001","DOIUrl":"10.1016/j.cvdhj.2024.03.001","url":null,"abstract":"<div><h3>Introduction</h3><p>Unmanaged hypertension in pregnancy is the second most common cause of direct maternal death and disproportionately affects women in rural areas. While telehealth technologies have worked to reduce barriers to healthcare, lack of internet access has created new challenges. Cellular-enabled remote patient monitoring devices provide an alternative option for those without access to internet.</p></div><div><h3>Objective</h3><p>This study aimed to assess maternal and neonatal clinical outcomes and patient acceptability of an integrated model of cellular-enabled remote patient monitoring devices for blood pressure supported by a 24/7 nurse call center.</p></div><div><h3>Methods</h3><p>In a mixed-methods study, 20 women with hypertension during pregnancy were given a cellular-enabled BodyTrace blood pressure cuff. Participants’ blood pressures were continuously monitored by a nurse call center. Participants completed a baseline survey, post-survey, and semi-structured interview after 8 weeks of device use.</p></div><div><h3>Results</h3><p>Participants reported a significant decrease in perceived stress after device use (<em>P</em> = .0004), high satisfaction with device usability (mean = 78.38, SD = 13.68), and high intention to continue device use (mean = 9.05, SD = 1.96). Relatively low hospitalization and emergency department rates was observed (mean = 0.35, SD = 0.59; mean = 0.75, SD = 0.91). Participant-perceived benefits of device use included convenience, perceived better care owing to increased monitoring, and patient empowerment. Perceived disadvantages included higher blood pressure readings compared to clinical readings and excessive calls from call center.</p></div><div><h3>Conclusion</h3><p>Remote patient monitoring for women whose pregnancies are complicated by hypertension can reduce barriers and improve health outcomes for women living in rural and low-health-resource areas.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"5 3","pages":"Pages 156-163"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693624000136/pdfft?md5=19a64676a8ec4cb896ee85b8bac0b90a&pid=1-s2.0-S2666693624000136-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140274982","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":"Association between amount of biventricular pacing and heart failure status measured by a multisensor implantable defibrillator algorithm","authors":"Luca Santini MD ,&nbsp;Leonardo Calò MD ,&nbsp;Antonio D’Onofrio MD ,&nbsp;Michele Manzo MD ,&nbsp;Antonio Dello Russo MD ,&nbsp;Gianluca Savarese MD ,&nbsp;Domenico Pecora MD ,&nbsp;Claudia Amellone MD ,&nbsp;Vincenzo Ezio Santobuono MD, PhD ,&nbsp;Raimondo Calvanese MD ,&nbsp;Miguel Viscusi MD ,&nbsp;Ennio Pisanò MD ,&nbsp;Antonio Pangallo MD ,&nbsp;Antonio Rapacciuolo MD ,&nbsp;Matteo Bertini MD, PhD ,&nbsp;Carlo Lavalle MD ,&nbsp;Amato Santoro MD ,&nbsp;Monica Campari MS ,&nbsp;Sergio Valsecchi PhD ,&nbsp;Giuseppe Boriani MD, PhD","doi":"10.1016/j.cvdhj.2024.02.005","DOIUrl":"https://doi.org/10.1016/j.cvdhj.2024.02.005","url":null,"abstract":"<div><h3>Background</h3><p>Achieving a high biventricular pacing percentage (BiV%) is crucial for optimizing outcomes in cardiac resynchronization therapy (CRT). The HeartLogic index, a multiparametric heart failure (HF) risk score, incorporates implantable cardioverter-defibrillator (ICD)-measured variables and has demonstrated its predictive ability for impending HF decompensation.</p></div><div><h3>Objective</h3><p>This study aimed to investigate the relationship between daily BiV% in CRT ICD patients and their HF status, assessed using the HeartLogic algorithm.</p></div><div><h3>Methods</h3><p>The HeartLogic algorithm was activated in 306 patients across 26 centers, with a median follow-up of 26 months (25th–75th percentile: 15–37).</p></div><div><h3>Results</h3><p>During the follow-up period, 619 HeartLogic alerts were recorded in 186 patients. Overall, daily values associated with the best clinical status (highest first heart sound, intrathoracic impedance, patient activity; lowest combined index, third heart sound, respiration rate, night heart rate) were associated with a BiV% exceeding 99%. We identified 455 instances of BiV% dropping below 98% after consistent pacing periods. Longer episodes of reduced BiV% (hazard ratio: 2.68; 95% CI: 1.02–9.72; <em>P</em> = .045) and lower BiV% (hazard ratio: 3.97; 95% CI: 1.74–9.06; <em>P</em>=.001) were linked to a higher risk of HeartLogic alerts. BiV% drops exceeding 7 days predicted alerts with 90% sensitivity (95% CI [74%–98%]) and 55% specificity (95% CI [51%–60%]), while BiV% ≤96% predicted alerts with 74% sensitivity (95% CI [55%–88%]) and 81% specificity (95% CI [77%–85%]).</p></div><div><h3>Conclusion</h3><p>A clear correlation was observed between reduced daily BiV% and worsening clinical conditions, as indicated by the HeartLogic index. Importantly, even minor reductions in pacing percentage and duration were associated with an increased risk of HF alerts.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"5 3","pages":"Pages 164-172"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693624000124/pdfft?md5=601cba59beb6c2265565914c3c53d6ca&pid=1-s2.0-S2666693624000124-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424468","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":"Can eHealth programs for cardiac arrhythmias be scaled-up by using the KardiaMobile algorithm?","authors":"Bridget M.I. Slaats Bsc ,&nbsp;Sebastiaan Blok PhD ,&nbsp;G. Aernout Somsen MD, PhD ,&nbsp;Igor I. Tulevski MD, PhD ,&nbsp;Reinoud E. Knops MD, PhD, FHRS ,&nbsp;Bert-Jan H. van den Born MD, PhD ,&nbsp;Michiel M. Winter MD, PhD","doi":"10.1016/j.cvdhj.2023.11.004","DOIUrl":"10.1016/j.cvdhj.2023.11.004","url":null,"abstract":"<div><h3>Background</h3><p>Remote monitoring devices for atrial fibrillation are known to positively contribute to the diagnostic process and therapy compliance. However, automatic algorithms within devices show varying sensitivity and specificity, so manual double-checking of electrocardiographic (ECG) recordings remains necessary.</p></div><div><h3>Objective</h3><p>The purpose of this study was to investigate the validity of the KardiaMobile algorithm within the Dutch telemonitoring program (HartWacht).</p></div><div><h3>Methods</h3><p>This retrospective study determined the diagnostic accuracy of the algorithm using assessments by a telemonitoring team as reference. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and F1 scores were determined.</p></div><div><h3>Results</h3><p>A total of 2298 patients (59.5% female; median age 57 ± 15 years) recorded 86,816 ECGs between April 2019 and January 2021. The algorithm showed sensitivity of 0.956, specificity 0.985, PPV 0.996, NPV 0.847, and F1 score 0.976 for the detection of sinus rhythm. A total of 29 false-positive outcomes remained uncorrected within the same patients. The algorithm showed sensitivity of 0.989, specificity 0.953, PPV 0.835, NPV 0.997, and F1 score 0.906 for detection of atrial fibrillation. A total of 2 false-negative outcomes remained uncorrected.</p></div><div><h3>Conclusion</h3><p>Our research showed high validity of the algorithm for the detection of both sinus rhythm and, to a lesser extent, atrial fibrillation. This finding suggests that the algorithm could function as a standalone instrument particularly for detection of sinus rhythm.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"5 2","pages":"Pages 78-84"},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693623000774/pdfft?md5=2c239dcb8183411753a06321db03891a&pid=1-s2.0-S2666693623000774-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135764518","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":"Postoperative atrial fibrillation: Prediction of subsequent recurrences with clinical risk modeling and artificial intelligence electrocardiography","authors":"Alanna M. Chamberlain PhD ,&nbsp;Nicholas P. Bergeron MD ,&nbsp;Abdullah K. Al-Abcha MD ,&nbsp;Susan A. Weston MS ,&nbsp;Ruoxiang Jiang BS ,&nbsp;Zachi I. Attia PhD ,&nbsp;Paul A. Friedman MD, FHRS ,&nbsp;Bernard J. Gersh MB, ChB, DPhil, FHRS ,&nbsp;Peter A. Noseworthy MD, MBA, FHRS ,&nbsp;Konstantinos C. Siontis MD, FHRS","doi":"10.1016/j.cvdhj.2024.02.004","DOIUrl":"10.1016/j.cvdhj.2024.02.004","url":null,"abstract":"","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"5 2","pages":"Pages 111-114"},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693624000112/pdfft?md5=a4269d0baaf257d8af5722c244cd44ee&pid=1-s2.0-S2666693624000112-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140470448","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}