Cardiovascular digital health journal最新文献

{"title":"Artificial intelligence augments detection accuracy of cardiac insertable cardiac monitors: Results from a pilot prospective observational study","authors":"Fabio Quartieri MD ,&nbsp;Manuel Marina-Breysse MD, MS ,&nbsp;Annalisa Pollastrelli MS ,&nbsp;Isabella Paini MScN ,&nbsp;Carlos Lizcano MS ,&nbsp;José María Lillo-Castellano PhD ,&nbsp;Andrea Grammatico PhD","doi":"10.1016/j.cvdhj.2022.07.071","DOIUrl":"10.1016/j.cvdhj.2022.07.071","url":null,"abstract":"<div><h3>Background</h3><p>Insertable cardiac monitors (ICMs) are indicated for long-term monitoring of patients with unexplained syncope or who are at risk for cardiac arrhythmias. The volume of ICM-transmitted information may result in long data review times to identify true and clinically relevant arrhythmias.</p></div><div><h3>Objective</h3><p>The purpose of this study was to evaluate whether artificial intelligence (AI) may improve ICM detection accuracy.</p></div><div><h3>Methods</h3><p>We performed a retrospective analysis of consecutive patients implanted with the Confirm Rx^TM ICM (Abbott) and followed in a prospective observational study. This device continuously monitors subcutaneous electrocardiograms (SECGs) and transmits to clinicians information about detected arrhythmias and patient-activated symptomatic episodes. All SECGs were classified by expert electrophysiologists and by the Willem^TM AI algorithm (IDOVEN).</p></div><div><h3>Results</h3><p>During mean follow-up of 23 months, of 20 ICM patients (mean age 68 ± 12 years; 50% women), 19 had 2261 SECGs recordings associated with cardiac arrhythmia detections or patient symptoms. True arrhythmias occurred in 11 patients: asystoles in 2, bradycardias in 3, ventricular tachycardias in 4, and atrial tachyarrhythmias (atrial tachycardia/atrial fibrillation [AT/AF]) in 10; with 6 patients having &gt;1 arrhythmia type. AI algorithm overall accuracy for arrhythmia classification was 95.4%, with 97.19% sensitivity, 94.52% specificity, 89.74% positive predictive value, and 98.55% negative predictive value. Application of AI would have reduced the number of false-positive results by 98.0% overall: 94.0% for AT/AF, 87.5% for ventricular tachycardia, 99.5% for bradycardia, and 98.8% for asystole.</p></div><div><h3>Conclusion</h3><p>Application of AI to ICM-detected episodes is associated with high classification accuracy and may significantly reduce health care staff workload by triaging ICM data.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"3 5","pages":"Pages 201-211"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/6e/d2/main.PMC9596320.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40655328","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":"Staff acceptability and patient usability of a self-screening kiosk for atrial fibrillation in general practice waiting rooms","authors":"Kirsty McKenzie BA(Hons), BSocSci Psychology(Hons), PhD ,&nbsp;Nicole Lowres BPhty, PhD ,&nbsp;Jessica Orchard BEc/LLB(Hons), MPH, PhD ,&nbsp;Charlotte Hespe MBBS ,&nbsp;Ben Freedman MBBS, PhD ,&nbsp;Katrina Giskes BHlthSc(Nutr.&Diet.), MBBS, PhD","doi":"10.1016/j.cvdhj.2022.07.073","DOIUrl":"10.1016/j.cvdhj.2022.07.073","url":null,"abstract":"<div><h3>Background</h3><p>Current Australian and European guidelines recommend opportunistic screening for atrial fibrillation (AF) among patients ≥65 years, but general practitioners (GPs) report time constraints as a major barrier to achieving this. Patient self-screening stations in GP waiting rooms may increase screening rates and case detection of AF, but the acceptability of patient self-screening from the practice staff perspective, and the usability by patients, is unknown.</p></div><div><h3>Objective</h3><p>To determine staff perspectives on AF self-screening stations and factors impacting acceptability, usability by patients, and sustainability.</p></div><div><h3>Methods</h3><p>We performed semi-structured interviews with 20 general practice staff and observations of 22 patients while they were undertaking self-screening. Interviews were coded and data analyzed using an iterative thematic analysis approach.</p></div><div><h3>Results</h3><p>GPs indicated high levels of acceptance of self-screening, and reported little impact on their workflow. Reception staff recognized the importance of screening for AF, but reported significant impacts on their workflow because some patients were unable to perform screening without assistance. Patient observations corroborated these findings and suggested some potential ways to improve usability.</p></div><div><h3>Conclusion</h3><p>AF self-screening in GP waiting rooms may be a viable method to increase opportunistic screening by GPs, but the impacts on reception workflow need to be mitigated for the method to be upscaled for more widespread screening. Furthermore, more age-appropriate station design may increase patient usability and thereby also reduce impact on reception workflow.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"3 5","pages":"Pages 212-219"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9596310/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40655330","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":"Psychosocial measures in relation to smartwatch alerts for atrial fibrillation detection","authors":"Andreas Filippaios MD ,&nbsp;Khanh-Van T. Tran MD, PhD ,&nbsp;Jordy Mehawej MD, ScM ,&nbsp;Eric Ding MS ,&nbsp;Tenes Paul DO ,&nbsp;Darleen Lessard MS ,&nbsp;Bruce Barton PhD ,&nbsp;Honghuang Lin PhD ,&nbsp;Syed Naeem MD ,&nbsp;Edith Mensah Otabil BA ,&nbsp;Kamran Noorishirazi BA ,&nbsp;Qiying Dai MD ,&nbsp;Hammad Sadiq MS ,&nbsp;Ki H. Chon PhD ,&nbsp;Apurv Soni MD, PhD ,&nbsp;Jane Saczynski PhD ,&nbsp;David D. McManus MD, ScM, FHRS","doi":"10.1016/j.cvdhj.2022.07.069","DOIUrl":"10.1016/j.cvdhj.2022.07.069","url":null,"abstract":"","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"3 5","pages":"Pages 198-200"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/bb/7c/main.PMC9596300.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9706737","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":"Enhancing convolutional neural network predictions of electrocardiograms with left ventricular dysfunction using a novel sub-waveform representation","authors":"Hossein Honarvar PhD ,&nbsp;Chirag Agarwal PhD ,&nbsp;Sulaiman Somani MD ,&nbsp;Akhil Vaid MD ,&nbsp;Joshua Lampert MD ,&nbsp;Tingyi Wanyan PhD ,&nbsp;Vivek Y. Reddy MD ,&nbsp;Girish N. Nadkarni MD ,&nbsp;Riccardo Miotto PhD ,&nbsp;Marinka Zitnik PhD ,&nbsp;Fei Wang PhD ,&nbsp;Benjamin S. Glicksberg PhD","doi":"10.1016/j.cvdhj.2022.07.074","DOIUrl":"10.1016/j.cvdhj.2022.07.074","url":null,"abstract":"<div><h3>Background</h3><p>Electrocardiogram (ECG) deep learning (DL) has promise to improve the outcomes of patients with cardiovascular abnormalities. In ECG DL, researchers often use convolutional neural networks (CNNs) and traditionally use the full duration of raw ECG waveforms that create redundancies in feature learning and result in inaccurate predictions with large uncertainties.</p></div><div><h3>Objective</h3><p>For enhancing these predictions, we introduced a sub-waveform representation that leverages the rhythmic pattern of ECG waveforms (data-centric approach) rather than changing the CNN architecture (model-centric approach).</p></div><div><h3>Results</h3><p>We applied the proposed representation to a population with 92,446 patients to identify left ventricular dysfunction. We found that the sub-waveform representation increases the performance metrics compared to the full-waveform representation. We observed a 2% increase for area under the receiver operating characteristic curve and 10% increase for area under the precision-recall curve. We also carefully examined three reliability components of explainability, interpretability, and fairness. We provided an explanation for enhancements obtained by heartbeat alignment mechanism. By developing a new scoring system, we interpreted the clinical relevance of ECG features and showed that sub-waveform representation further pushes the scores towards clinical predictions. Finally, we showed that the new representation significantly reduces prediction uncertainties within subgroups that contributes to individual fairness.</p></div><div><h3>Conclusion</h3><p>We expect that this added control over the granularity of ECG data will improve the DL modeling for new artificial intelligence technologies in the cardiovascular space.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"3 5","pages":"Pages 220-231"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/f4/42/main.PMC9596304.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9578311","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":"Detecting early physiologic changes through cardiac implantable electronic device data among patients with COVID-19","authors":"Meghan Reading Turchioe PhD, MPH, RN ,&nbsp;Rezwan Ahmed PhD ,&nbsp;Ruth Masterson Creber PhD, MSc, RN ,&nbsp;Kelly Axsom MD ,&nbsp;Evelyn Horn MD ,&nbsp;Gabriel Sayer MD ,&nbsp;Nir Uriel MD ,&nbsp;Kenneth Stein MD, FHRS ,&nbsp;David Slotwiner MD, FHRS","doi":"10.1016/j.cvdhj.2022.07.070","DOIUrl":"10.1016/j.cvdhj.2022.07.070","url":null,"abstract":"<div><h3>Background</h3><p>Cardiac implantable electronic devices (CIEDs) may enable early identification of COVID-19 to facilitate timelier intervention.</p></div><div><h3>Objective</h3><p>To characterize early physiologic changes associated with the onset of acute COVID-19 infection, as well as during and after acute infection, among patients with CIEDs.</p></div><div><h3>Methods</h3><p>CIED sensor data from March 2020 to February 2021 from 286 patients with a CIED were linked to clinical data from electronic health records. Three cohorts were created: known COVID-positive (n = 20), known COVID-negative (n = 166), and a COVID-untested control group (n = 100) included to account for testing bias. Associations between changes in CIED sensors from baseline (including HeartLogic index, a composite index predicting worsening heart failure) and COVID-19 status were evaluated using logistic regression models, Wilcoxon signed rank tests, and Mann-Whitney <em>U</em> tests.</p></div><div><h3>Results</h3><p>Significant differences existed between the cohorts by race, ethnicity, CIED device type, and medical admissions. Several sensors changed earlier for COVID-positive vs COVID-negative patients: HeartLogic index (mean 16.4 vs 9.2 days [<em>P</em> = .08]), respiratory rate (mean 8.5 vs 3.9 days [<em>P</em> = .01], and activity (mean 8.2 vs 3.5 days [<em>P</em> = .008]). Respiratory rate during the 7 days before testing significantly predicted a positive vs negative COVID-19 test, adjusting for age, sex, race, and device type (odds ratio 2.31 [95% confidence interval 1.33–5.13]).</p></div><div><h3>Conclusion</h3><p>Physiologic data from CIEDs could signal early signs of infection that precede clinical symptoms, which may be used to support early detection of infection to prevent decompensation in this at-risk population.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"3 5","pages":"Pages 247-255"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9349024/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9968902","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":"Expanding telehealth through technology: Use of digital health technologies during pediatric electrophysiology telehealth visits","authors":"Lisa Roelle PA ,&nbsp;Juliana Ocasio BA ,&nbsp;Lauren Littell MD ,&nbsp;Eli Fredman MD ,&nbsp;Nathan Miller RN ,&nbsp;Tracy Conner MD ,&nbsp;George Van Hare MD, FHRS ,&nbsp;Jennifer N. Avari Silva MD, FHRS","doi":"10.1016/j.cvdhj.2022.07.003","DOIUrl":"10.1016/j.cvdhj.2022.07.003","url":null,"abstract":"","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"3 5","pages":"Pages 256-261"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2d/a2/main.PMC9363236.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40627077","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":"Preventing preventable strokes: A study protocol to push guideline-driven atrial fibrillation patient education via patient portal","authors":"Michael Fitzpatrick DO ,&nbsp;Hammad Sadiq BS ,&nbsp;Sanjeev Rampam BS ,&nbsp;Almaz Araia BA ,&nbsp;Megan Miller BS ,&nbsp;Kevin Rivera Vargas BS ,&nbsp;Patrick Fry BS ,&nbsp;Anne Marie Smith MBA ,&nbsp;Mary Martin Lowe PhD ,&nbsp;Christina Catalano MBA ,&nbsp;Charles Harrison MD ,&nbsp;John Catanzaro MD ,&nbsp;Sybil Crawford PhD ,&nbsp;David McManus MD, MSc ,&nbsp;Alok Kapoor MD, MSc","doi":"10.1016/j.cvdhj.2022.07.068","DOIUrl":"10.1016/j.cvdhj.2022.07.068","url":null,"abstract":"<div><h3>Background</h3><p>The main approach to preventing stroke in patients with atrial fibrillation (AF) is anticoagulation (AC), but only about 60% of at-risk individuals are on AC. Patient-facing electronic health record–based interventions have produced mixed results. Little is known about the impact of health portal–based messaging on AC use.</p></div><div><h3>Objective</h3><p>The purpose of this study was describe a protocol we will use to measure the association between AC use and patient portal message opening. We also will measure patient attitudes toward education materials housed on a professional society Web site.</p></div><div><h3>Methods</h3><p>We will send portal messages to patients aged ≥18 years with AF 1 week before an office/teleconference visit with a primary care or cardiology provider. The message will be customized for 3 groups of patients: those on AC; those at elevated risk but off AC; and those not currently at risk but may be at risk in the future. Within the message, we will embed a link to UpBeat.org, a Web site of the Heart Rhythm Society containing patient educational materials. We also will embed a link to a survey. Among other things, the survey will request patients to rate their attitude toward the Heart Rhythm Society Web pages. To measure the effectiveness of the intervention, we will track AC use and its association with message opening, adjusting for potential confounders.</p></div><div><h3>Conclusion</h3><p>If we detect an increase in AC use correlates with message opening, we will be well positioned to conduct a future comparative effectiveness trial. If patients rate the UpBeat.org materials highly, patients from other institutions also may benefit from receiving these materials.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"3 5","pages":"Pages 241-246"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c1/dc/main.PMC9596318.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40655327","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":"Early preclinical experience of a mixed reality ultrasound system with active GUIDance for NEedle-based interventions: The GUIDE study","authors":"David Bloom MD ,&nbsp;Jamie N. Colombo DO ,&nbsp;Nathan Miller BSN ,&nbsp;Michael K. Southworth MS ,&nbsp;Christopher Andrews PhD ,&nbsp;Alexander Henry MS ,&nbsp;William B. Orr MD ,&nbsp;Jonathan R. Silva PhD ,&nbsp;Jennifer N. Avari Silva MD, FHRS","doi":"10.1016/j.cvdhj.2022.07.072","DOIUrl":"10.1016/j.cvdhj.2022.07.072","url":null,"abstract":"<div><h3>Background</h3><p>Use of ultrasound (US) to facilitate vascular access has increased compared to landmark-based procedures despite ergonomic challenges and need for extrapolation of 2-dimensional images to understand needle position. The MantUS™ system (Sentiar, Inc.,) uses a mixed reality (MxR) interface to display US images and integrate real-time needle tracking.</p></div><div><h3>Objective</h3><p>The purpose of this prospective preclinical study was to evaluate the feasibility and usability of MantUS in a simulated environment.</p></div><div><h3>Methods</h3><p>Participants were recruited from pediatric cardiology and critical care. Access was obtained in 2 vascular access training models: a femoral access model and a head and neck model for a total of 4 vascular access sites under 2 conditions—conventional US and MantUS. Participants were randomized for order of completion. Videos were obtained, and quality of access including time required, repositions, number of attempts, and angle of approach were quantified.</p></div><div><h3>Results</h3><p>Use of MantUS resulted in an overall reduction in number of needle repositions (<em>P</em> = .03) and improvement in quality of access as measured by distance (<em>P</em> &lt;.0001) and angle of elevation (<em>P</em> = .006). These findings were even more evident in the right femoral vein (RFV) access site, which was a simulated anatomic variant with a deeper more oblique vascular course. Use of MantUS resulted in faster time to access (<em>P</em> = .04), fewer number of both access attempts (<em>P</em> = .02), and number of needle repositions (<em>P</em> &lt;.0001) compared to conventional US. Postparticipant survey showed high levels of usability (87%) and a belief that MantUS may decrease adverse outcomes (73%) and failed access attempts (83%).</p></div><div><h3>Conclusion</h3><p>Use of MantUS improved vascular access among all comers, including the quality of access. This improvement was even more notable in the vascular variant (RFV). MantUS readily benefited users by providing improved spatial understanding. Further development of MantUS will focus on improving user interface and experience, with larger clinical usage and in-human studies.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"3 5","pages":"Pages 232-240"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9596321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40656253","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":"PREDICTORS OF ATRIAL FIBRILLATION BURDEN MEASURED BY A SINGLE LEAD SMARTPHONE ECG DEVICE: A DECAAF-II SUB ANALYSIS","authors":"Mario Mekhael,&nbsp;Charbel Noujaim,&nbsp;Chan H. Lim,&nbsp;Nour Chouman,&nbsp;Cong Zhao,&nbsp;He Hua,&nbsp;Abdel Hadi El Hajjar,&nbsp;Nassir F. Marrouche","doi":"10.1016/j.cvdhj.2022.07.067","DOIUrl":"10.1016/j.cvdhj.2022.07.067","url":null,"abstract":"","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"3 4","pages":"Pages S6-S7"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693622001165/pdfft?md5=28c723b6e34ca3d9a3fa168b6b052962&pid=1-s2.0-S2666693622001165-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45186224","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 FOR THE PREDICTION OF VENTRICULAR ARRHYTHMIAS AND SUDDEN CARDIAC DEATH USING ELECTROPHYSIOLOGICAL SIGNALS: A SYSTEMATIC REVIEW AND META-ANALYSIS","authors":"Maarten Kolk,&nbsp;Brototo Deb,&nbsp;Samuel Ruiperez-Campillo,&nbsp;Paul Clopton,&nbsp;Sanjiv M. Narayan,&nbsp;Reinoud Knops,&nbsp;Fleur V. Tjong","doi":"10.1016/j.cvdhj.2022.07.060","DOIUrl":"10.1016/j.cvdhj.2022.07.060","url":null,"abstract":"","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"3 4","pages":"Page S26"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693622001098/pdfft?md5=0180ef63b69b3eb364fd035bd8a89391&pid=1-s2.0-S2666693622001098-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48913018","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}