Sociodemographic determinants of oral anticoagulant prescription in patients with atrial fibrillations: findings from the PINNACLE registry using machine learning.

Background: Current risk scores that are solely based on clinical factors have shown modest predictive ability for understanding of factors associated with gaps in real-world prescription of oral anticoagulation (OAC) in patients with atrial fibrillation (AF). Objective: In this study, we sought to identify the role of social and geographic determinants, beyond clinical factors associated with variation in OAC prescriptions using a large national registry of ambulatory patients with AF. Methods: Between January 2017 and June 2018, we identified patients with AF from the American College of Cardiology PINNACLE (Practice Innovation and Clinical Excellence) Registry. We examined associations between patient and site-of-care factors and prescription of OAC across U.S. counties. Several machine learning (ML) methods were used to identify factors associated with OAC prescription. Results: Among 864,339 patients with AF, 586,560 (68%) were prescribed OAC. County OAC prescription rates ranged from 26.8% to 93%, with higher OAC use in the Western United States. Supervised ML analysis in predicting likelihood of OAC prescriptions and identified a rank order of patient features associated with OAC prescription. In the ML models, in addition to clinical factors, medication use (aspirin, antihypertensives, antiarrhythmic agents, lipid modifying agents), and age, household income, clinic size, and U.S. region were among the most important predictors of an OAC prescription. Conclusion: In a contemporary, national cohort of patients with AF underuse of OAC remains high, with notable geographic variation. Our results demonstrated the role of several important demographic and socioeconomic factors in underutilization of OAC in patients with AF.

Lessons learned in the Apple Heart Study and implications for the data management of future digital clinical trials.

The digital clinical trial is fast emerging as a pragmatic trial that can improve a trial's design including recruitment and retention, data collection and analytics. To that end, digital platforms such as electronic health records or wearable technologies that enable passive data collection can be leveraged, alleviating burden from the participant and study coordinator. However, there are challenges. For example, many of these data sources not originally intended for research may be noisier than traditionally obtained measures. Further, the secure flow of passively collected data and their integration for analysis is non-trivial. The Apple Heart Study was a prospective, single-arm, site-less digital trial designed to evaluate the ability of an app to detect atrial fibrillation. The study was designed with pragmatic features, such as an app for enrollment, a wearable device (the Apple Watch) for data collection, and electronic surveys for participant-reported outcomes that enabled a high volume of patient enrollment and accompanying data. These elements led to challenges including identifying the number of unique participants, maintaining participant-level linkage of multiple complex data streams, and participant adherence and engagement. Novel solutions were derived that inform future designs with an emphasis on data management. We build upon the excellent framework of the Clinical Trials Transformation Initiative to provide a comprehensive set of guidelines for data management of the digital clinical trial that include an increased role of collaborative data scientists in the design and conduct of the modern digital trial.

The development of a mobile app-focused deduplication strategy for the Apple Heart Study that informs recommendations for future digital trials.

An app-based clinical trial enrolment process can contribute to duplicated records, carrying data management implications. Our objective was to identify duplicated records in real time in the Apple Heart Study (AHS). We leveraged personal identifiable information (PII) to develop a dissimilarity score (DS) using the Damerau-Levenshtein distance. For computational efficiency, we focused on four types of records at the highest risk of duplication. We used the receiver operating curve (ROC) and resampling methods to derive and validate a decision rule to classify duplicated records. We identified 16,398 (4%) duplicated participants, resulting in 419,297 unique participants out of a total of 438,435 possible. Our decision rule yielded a high positive predictive value (96%) with negligible impact on the trial's original findings. Our findings provide principled solutions for future digital trials. When establishing deduplication procedures for digital trials, we recommend collecting device identifiers in addition to participant identifiers; collecting and ensuring secure access to PII; conducting a pilot study to identify reasons for duplicated records; establishing an initial deduplication algorithm that can be refined; creating a data quality plan that informs refinement; and embedding the initial deduplication algorithm in the enrolment platform to ensure unique enrolment and linkage to previous records.

Performance Metrics for the Comparative Analysis of Clinical Risk Prediction Models Employing Machine Learning.

BACKGROUND: New methods such as machine learning techniques have been increasingly used to enhance the performance of risk predictions for clinical decision-making. However, commonly reported performance metrics may not be sufficient to capture the advantages of these newly proposed models for their adoption by health care professionals to improve care. Machine learning models often improve risk estimation for certain subpopulations that may be missed by these metrics. METHODS AND RESULTS: This article addresses the limitations of commonly reported metrics for performance comparison and proposes additional metrics. Our discussions cover metrics related to overall performance, discrimination, calibration, resolution, reclassification, and model implementation. Models for predicting acute kidney injury after percutaneous coronary intervention are used to illustrate the use of these metrics. CONCLUSIONS: We demonstrate that commonly reported metrics may not have sufficient sensitivity to identify improvement of machine learning models and propose the use of a comprehensive list of performance metrics for reporting and comparing clinical risk prediction models.

Arrhythmias Other Than Atrial Fibrillation in Those With an Irregular Pulse Detected With a Smartwatch: Findings From the Apple Heart Study.

[Figure: see text].

Association of Wearable Device Use With Pulse Rate and Health Care Use in Adults With Atrial Fibrillation.

Importance: Increasingly, individuals with atrial fibrillation (AF) use wearable devices (hereafter wearables) that measure pulse rate and detect arrhythmia. The associations of wearables with health outcomes and health care use are unknown. Objective: To characterize patients with AF who use wearables and compare pulse rate and health care use between individuals who use wearables and those who do not. Design, Setting, and Participants: This retrospective, propensity-matched cohort study included 90 days of follow-up of patients in a tertiary care, academic health system. Included patients were adults with at least 1 AF-specific International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) code from 2017 through 2019. Electronic medical records were reviewed to identify 125 individuals who used wearables and had adequate pulse-rate follow-up who were then matched using propensity scores 4 to 1 with 500 individuals who did not use wearables. Data were analyzed from June 2020 through February 2021. Exposure: Using commercially available wearables with pulse rate or rhythm evaluation capabilities. Main Outcomes and Measures: Mean pulse rates from measures taken in the clinic or hospital and a composite health care use score were recorded. The composite outcome included evaluation and management, ablation, cardioversion, telephone encounters, and number of rate or rhythm control medication orders. Results: Among 16 320 patients with AF included in the analysis, 348 patients used wearables and 15 972 individuals did not use wearables. Prior to matching, patients using wearables were younger (mean [SD] age, 64.0 [13.0] years vs 70.0 [13.8] years; P < .001) and healthier (mean [SD] CHA2DS2-VASc [congestive heart failure, hypertension, age ≥ 65 years or 65-74 years, diabetes, prior stroke/transient ischemic attack, vascular disease, sex] score, 3.6 [2.0] vs 4.4 [2.0]; P < .001) compared with individuals not using wearables, with similar gender distribution (148 [42.5%] women vs 6722 women [42.1%]; P = .91). After matching, mean pulse rate was similar between 125 patients using wearables and 500 patients not using wearables (75.01 [95% CI, 72.74-77.27] vs 75.79 [95% CI, 74.68-76.90] beats per minute [bpm]; P = .54), whereas mean composite use score was higher among individuals using wearables (3.55 [95% CI, 3.31-3.80] vs 3.27 [95% CI, 3.14-3.40]; P = .04). Among measures in the composite outcome, there was a significant difference in use of ablation, occurring in 22 individuals who used wearables (17.6%) vs 37 individuals who did not use wearables (7.4%) (P = .001). In the regression analyses, mean composite use score was 0.28 points (95% CI, 0.01 to 0.56 points) higher among individuals using wearables compared with those not using wearables and mean pulse was similar, with a -0.79 bpm (95% CI -3.28 to 1.71 bpm) difference between the groups. Conclusions and Relevance: This study found that follow-up health care use among individuals with AF was increased among those who used wearables compared with those with similar pulse rates who did not use wearables. Given the increasing use of wearables by patients with AF, prospective, randomized, long-term evaluation of the associations of wearable technology with health outcomes and health care use is needed.

Optimizing the Potential for Telehealth in Cardiovascular Care (in the Era of COVID-19): Time Will Tell.

Before the coronavirus disease 2019 (COVID-19) pandemic, use of telehealth services had been limited in cardiovascular care. Potential benefits of telehealth include improved access to care, more efficient care management, reduced costs, the ability to assess patients within their homes while involving key caretakers in medical decisions, maintaining social distance, and increased patient satisfaction. Challenges include changes in payment models, issues with data security and privacy, potential depersonalization of the patient-clinician relationship, limitations in the use of digital health technologies, and the potential impact on disparities, including socioeconomic, gender, and age-related issues and access to technology and broadband. Implementation and expansion of telehealth from a policy and reimbursement practice standpoint are filled with difficult decisions, yet addressing these are critical to the future of health care.

Variation in Antithrombotic Therapy and Clinical Outcomes in Patients With Preexisting Atrial Fibrillation Undergoing Transcatheter Aortic Valve Replacement: Insights From the Society of Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapy Registry.

BACKGROUND: Optimal antithrombotic management of patients with preexisting atrial fibrillation undergoing transcatheter aortic valve replacement is challenging given the need to balance the risk of bleeding and thromboembolism. We aimed to examine variation in care and association of antithrombotic therapies with 1-year outcomes of stroke, bleeding, and mortality in patients undergoing transcatheter aortic valve replacement with concomitant atrial fibrillation in the United States. METHODS: Patients who underwent transcatheter aortic valve replacement with preexisting atrial fibrillation from November 2011 through September 2015 in the Society of Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapy registry linked to the Medicare database were examined according to receipt of oral anticoagulants (OACs) or antiplatelet therapies (APTs) or a combination of these (OAC+APT) at discharge. To assess the associations of antithrombotic therapies with 1-year outcomes of stroke, bleeding, and mortality, we utilized inverse probability weighting for antithrombotic therapies and multivariable regression modeling to adjust for patient- and hospital-level variables. RESULTS: In the 11 382 patients included in our study, 5833 (51.2%) were discharged on OAC+APT, 4786 (42.0%) on APT alone, and 763 (6.7%) on OAC alone. There was significant variability in discharge medication patterns, including 42% of patients discharged without OAC therapy. In adjusted analyses, the risk for all-cause mortality and stroke was not significantly different when comparing the 3 different antithrombotic strategies. Risk of bleeding was higher with OAC+APT compared with APT alone (hazard ratio, 1.16 [95% CI, 1.05­1.27]) and similar compared with OAC alone (hazard ratio, 1.17 [95% CI, 0.93­1.47]). CONCLUSIONS: There was significant variability in discharge medication patterns across US sites in patients with atrial fibrillation undergoing transcatheter aortic valve replacement, including significant underuse of OAC in this high-risk cohort. The use of OAC+APT (versus OAC alone or APT alone) was not associated with a lower risk of stroke or mortality but was associated with increased risk of bleeding complications at 1 year compared with APT alone.

Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction.

Importance: Accurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making, and novel methods hold promise for using existing data to generate additional insights. Objective: To evaluate whether contemporary machine learning methods can facilitate risk prediction by including a larger number of variables and identifying complex relationships between predictors and outcomes. Design, Setting, and Participants: This cohort study used the American College of Cardiology Chest Pain-MI Registry to identify all AMI hospitalizations between January 1, 2011, and December 31, 2016. Data analysis was performed from February 1, 2018, to October 22, 2020. Main Outcomes and Measures: Three machine learning models were developed and validated to predict in-hospital mortality based on patient comorbidities, medical history, presentation characteristics, and initial laboratory values. Models were developed based on extreme gradient descent boosting (XGBoost, an interpretable model), a neural network, and a meta-classifier model. Their accuracy was compared against the current standard developed using a logistic regression model in a validation sample. Results: A total of 755 402 patients (mean [SD] age, 65 [13] years; 495 202 [65.5%] male) were identified during the study period. In independent validation, 2 machine learning models, gradient descent boosting and meta-classifier (combination including inputs from gradient descent boosting and a neural network), marginally improved discrimination compared with logistic regression (C statistic, 0.90 for best performing machine learning model vs 0.89 for logistic regression). Nearly perfect calibration in independent validation data was found in the XGBoost (slope of predicted to observed events, 1.01; 95% CI, 0.99-1.04) and the meta-classifier model (slope of predicted-to-observed events, 1.01; 95% CI, 0.99-1.02), with more precise classification across the risk spectrum. The XGBoost model reclassified 32 393 of 121 839 individuals (27%) and the meta-classifier model reclassified 30 836 of 121 839 individuals (25%) deemed at moderate to high risk for death in logistic regression as low risk, which were more consistent with the observed event rates. Conclusions and Relevance: In this cohort study using a large national registry, none of the tested machine learning models were associated with substantive improvement in the discrimination of in-hospital mortality after AMI, limiting their clinical utility. However, compared with logistic regression, XGBoost and meta-classifier models, but not the neural network, offered improved resolution of risk for high-risk individuals.

Bleeding avoidance strategies and percutaneous coronary intervention outcomes: A 10-year observation from a Japanese Multicenter Registry.

BACKGROUND: Bleeding avoidance strategies (BASs) are increasingly adopted for patients undergoing percutaneous coronary intervention (PCI) due to bleeding complications. However, their association with bleeding events outside of Western countries remains unclear. In collaboration with the National Cardiovascular Data Registry (NCDR) CathPCI registry, we aimed to assess the time trend and impact of BAS utilization among Japanese patients. METHODS: Our study included 19,656 consecutive PCI patients registered over 10 years. These patients were divided into 4-time frame groups (T1: 2008-2011, T2: 2012-2013, T3: 2014-2015, and T4: 2016-2018). BAS was defined as the use of transradial approach or vascular closure device (VCD) use after transfemoral approach (TFA). Model performance of the NCDR CathPCI bleeding model was evaluated. The degree of bleeding reduction associated with BAS adoption was estimated via multilevel mixed-effects multivariable logistic regression analysis. RESULTS: The NCDR CathPCI bleeding risk score demonstrated good discrimination in the Japanese population (C-statistics 0.79-0.81). The BAS adoption rate increased from 43% (T1) to 91% (T4), whereas the crude CathPCI-defined bleeding rate decreased from 10% (T1) to 7% (T4). Adjusted odds ratios for bleeding events were 0.25 (95% confidence interval, 0.14-0.45, P< .001) for those undergoing TFA with VCD in T4 and 0.26 (95% confidence interval 0.20-0.35, P< .001) for transradial approach in T4 compared to patients that received TFA without VCD in T1. CONCLUSIONS: BAS use over the studied time frames was associated with lower risk of bleeding complications among Japanese. Nonetheless, observed bleeding rates remained higher compared to the US population.

Comparative Trends in Percutaneous Coronary Intervention in Japan and the United States, 2013 to 2017.

BACKGROUND: Adoption of the results of large-scale randomized controlled trials in percutaneous coronary intervention (PCI) may differ internationally, yet few studies have described the potential variations in PCI practice patterns. OBJECTIVES: Using representative national registries, we compared temporal trends in procedural volume, patient characteristics, pre-procedural testing, procedural characteristics, and quality metrics in the United States and Japan. METHODS: The National Cardiovascular Data Registry CathPCI was used to describe care in the United States, and the J-PCI was used to assess practice patterns in Japan (numbers of participating hospitals: 1,752 in the United States and 1,108 in Japan). Both registries were summarized between 2013 and 2017. RESULTS: PCI volume increased by 15.8% in the United States from 550,872 in 2013 to 637,650 in 2017, primarily because of an increase in nonelective PCIs (p for trend <0.001). In Japan, the volume of PCIs increased by 36%, from 181,750 in 2013 to 247,274 in 2017, primarily because of an increase in elective PCIs (p for trend <0.001). The proportion of PCI cases for elective conditions was >2-fold greater in Japan (72.7%) than in the United States (33.8%; p < 0.001). Overall, the ratio of nonelective PCI (vs. elective PCI; 27.3% vs. 66.2%; p < 0.001) and the performance of noninvasive stress testing in patients with stable disease (15.2% vs. 55.3%; p < 0.001) was lower in Japan than in the United States. Computed tomography angiography was more commonly used in Japan (22.3% vs. 2.0%; p < 0.001). CONCLUSIONS: Elective PCI is more than twice as common in Japan as in the United States in contemporary practice. Computed tomography angiography is much more frequently used pre-procedurally in Japan than in the United States.

'Real-world' observational studies in arrhythmia research: data sources, methodology, and interpretation. A position document from European Heart Rhythm Association (EHRA), endorsed by Heart Rhythm Society (HRS), Asia-Pacific HRS (APHRS), and Latin America HRS (LAHRS)

The field of observational studies or "real world studies" is in rapid development with many new techniques introduced and increased understanding of traditional methods. For this reason, the current paper provides an overview of current methods with focus on new techniques. Some highlights can be emphasized: We provide an overview of sources of data for observational studies. There is an overview of sources of bias and confounding. Next There is an overview of causal inference techniques that are increasingly used. The most commonly used techniques for statistical modelling are reviewed with focus on the important distinction of risk versus prediction. The final section provides examples of common problems with reporting observational data.

Management of acute myocardial infarction during the COVID-19 pandemic: A Consensus Statement from the Society for Cardiovascular Angiography and Interventions (SCAI), the American College of Cardiology (ACC), and the American College of Emergency Physicians (ACEP).

The worldwide pandemic caused by the novel acute respiratory syndrome coronavirus 2 has resulted in a new and lethal disease termed coronavirus disease 2019 (COVID-19). Although there is an association between cardiovascular disease and COVID-19, the majority of patients who need cardiovascular care for the management of ischemic heart disease may not be infected with this novel coronavirus. The objective of this document is to provide recommendations for a systematic approach for the care of patients with an acute myocardial infarction (AMI) during the COVID-19 pandemic. There is a recognition of two major challenges in providing recommendations for AMI care in the COVID-19 era. Cardiovascular manifestations of COVID-19 are complex with patients presenting with AMI, myocarditis simulating an ST-elevation myocardial infarction (STEMI) presentation, stress cardiomyopathy, non-ischemic cardiomyopathy, coronary spasm, or nonspecific myocardial injury, and the prevalence of COVID-19 disease in the US population remains unknown with risk of asymptomatic spread. This document addresses the care of these patients focusing on (a) varied clinical presentations; (b) appropriate personal protection equipment (PPE) for health care workers; (c) the roles of the emergency department, emergency medical system, and the cardiac catheterization laboratory (CCL); and (4) regional STEMI systems of care. During the COVID-19 pandemic, primary percutaneous coronary intervention (PCI) remains the standard of care for STEMI patients at PCI-capable hospitals when it can be provided in a timely manner, with an expert team outfitted with PPE in a dedicated CCL room. A fibrinolysis-based strategy may be entertained at non-PCI-capable referral hospitals or in specific situations where primary PCI cannot be executed or is not deemed the best option.

Management of Acute Myocardial Infarction During the COVID-19 Pandemic: A Position Statement From the Society for Cardiovascular Angiography and Interventions (SCAI), the American College of Cardiology (ACC), and the American College of Emergency Physicians (ACEP).

The worldwide pandemic caused by the novel acute respiratory syndrome coronavirus 2 has resulted in a new and lethal disease termed coronavirus disease-2019 (COVID-19). Although there is an association between cardiovascular disease and COVID-19, the majority of patients who need cardiovascular care for the management of ischemic heart disease may not be infected with this novel coronavirus. The objective of this document is to provide recommendations for a systematic approach for the care of patients with an acute myocardial infarction (AMI) during the COVID-19 pandemic. There is a recognition of two major challenges in providing recommendations for AMI care in the COVID-19 era. Cardiovascular manifestations of COVID-19 are complex with patients presenting with AMI, myocarditis simulating an ST-elevation myocardial infarction (STEMI) presentation, stress cardiomyopathy, non-ischemic cardiomyopathy, coronary spasm, or nonspecific myocardial injury, and the prevalence of COVID-19 disease in the U.S. population remains unknown with risk of asymptomatic spread. This document addresses the care of these patients focusing on 1) the varied clinical presentations; 2) appropriate personal protection equipment (PPE) for health care workers; 3) role of the Emergency Department, Emergency Medical System and the Cardiac Catheterization Laboratory; and 4) Regional STEMI systems of care. During the COVID-19 pandemic, primary PCI remains the standard of care for STEMI patients at PCI capable hospitals when it can be provided in a timely fashion, with an expert team outfitted with PPE in a dedicated CCL room. A fibrinolysis-based strategy may be entertained at non-PCI capable referral hospitals or in specific situations where primary PCI cannot be executed or is not deemed the best option.

Myocardial infarction with non-obstructive coronary arteries as compared with myocardial infarction and obstructive coronary disease: outcomes in a Medicare population.

AIMS: The prognosis of patients with MINOCA (myocardial infarction with non-obstructive coronary arteries) is poorly understood. We examined major adverse cardiac events (MACE) defined as all-cause mortality, re-hospitalization for acute myocardial infarction (AMI), heart failure (HF), or stroke 12-months post-AMI in patients with MINOCA versus AMI patients with obstructive coronary artery disease (MICAD). METHODS AND RESULTS: Multicentre, observational cohort study of patients with AMI (≥65 years) from the National Cardiovascular Data Registry CathPCI Registry (July 2009-December 2013) who underwent coronary angiography with linkage to the Centers for Medicare and Medicaid (CMS) claims data. Patients were classified as MICAD or MINOCA by the presence or absence of an epicardial vessel with ≥50% stenosis. The primary endpoint was MACE at 12 months, and secondary endpoints included the components of MACE over 12 months. Among 286 780 AMI admissions (276 522 unique patients), 16 849 (5.9%) had MINOCA. The 12-month rates of MACE (18.7% vs. 27.6%), mortality (12.3% vs. 16.7%), and re-hospitalization for AMI (1.3% vs. 6.1%) and HF (5.9% vs. 9.3%) were significantly lower for MINOCA vs. MICAD patients (P < 0.001), but was similar between MINOCA and MICAD patients for re-hospitalization for stroke (1.6% vs. 1.4%, P = 0.128). Following risk-adjustment, MINOCA patients had a 43% lower risk of MACE over 12 months (hazard ratio = 0.57, 95% confidence interval 0.55-0.59), in comparison to MICAD patients. This pattern was similar for adjusted risks of the MACE components. CONCLUSION: This study confirms an unfavourable prognosis in elderly patients with MINOCA undergoing coronary angiography, with one in five patients with MINOCA suffering a major adverse event over 12 months.

'Real-world' observational studies in arrhythmia research: data sources, methodology, and interpretation. A position document from European Heart Rhythm Association (EHRA), endorsed by Heart Rhythm Society (HRS), Asia-Pacific HRS (APHRS), and Latin America HRS (LAHRS).

The field of observational studies or "real world studies" is in rapid development with many new techniques introduced and increased understanding of traditional methods. For this reason the current paper provides an overview of current methods with focus on new techniques. Some highlights can be emphasized: We provide an overview of sources of data for observational studies. There is an overview of sources of bias and confounding. Next There is an overview of causal inference techniques that are increasingly used. The most commonly used techniques for statistical modelling are reviewed with focus on the important distinction of risk versus prediction. The final section provides examples of common problems with reporting observational data.

Innovation in Cardiology: The ACC Innovation Program.

The last half century has seen extraordinary advances in the field of cardiology, including innovations in medications, diagnostic modalities, and therapeutics. Even so, cardiovascular disease remains the leading cause of morbidity and mortality globally, with suboptimal quality of care, inconsistent health outcomes, and unsustainable costs. It is clear that cardiovascular medicine must undergo a digital transformation to enhance the delivery of quality care and to improve outcomes. To meet this need, the American College of Cardiology developed an innovation program focused on the digital transformation of cardiovascular care, with the goal of improving heart health for individuals and populations.

Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation.

BACKGROUND: Optical sensors on wearable devices can detect irregular pulses. The ability of a smartwatch application (app) to identify atrial fibrillation during typical use is unknown. METHODS: Participants without atrial fibrillation (as reported by the participants themselves) used a smartphone (Apple iPhone) app to consent to monitoring. If a smartwatch-based irregular pulse notification algorithm identified possible atrial fibrillation, a telemedicine visit was initiated and an electrocardiography (ECG) patch was mailed to the participant, to be worn for up to 7 days. Surveys were administered 90 days after notification of the irregular pulse and at the end of the study. The main objectives were to estimate the proportion of notified participants with atrial fibrillation shown on an ECG patch and the positive predictive value of irregular pulse intervals with a targeted confidence interval width of 0.10. RESULTS: We recruited 419,297 participants over 8 months. Over a median of 117 days of monitoring, 2161 participants (0.52%) received notifications of irregular pulse. Among the 450 participants who returned ECG patches containing data that could be analyzed - which had been applied, on average, 13 days after notification - atrial fibrillation was present in 34% (97.5% confidence interval [CI], 29 to 39) overall and in 35% (97.5% CI, 27 to 43) of participants 65 years of age or older. Among participants who were notified of an irregular pulse, the positive predictive value was 0.84 (95% CI, 0.76 to 0.92) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular pulse notification and 0.71 (97.5% CI, 0.69 to 0.74) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular tachogram. Of 1376 notified participants who returned a 90-day survey, 57% contacted health care providers outside the study. There were no reports of serious app-related adverse events. CONCLUSIONS: The probability of receiving an irregular pulse notification was low. Among participants who received notification of an irregular pulse, 34% had atrial fibrillation on subsequent ECG patch readings and 84% of notifications were concordant with atrial fibrillation. This siteless (no on-site visits were required for the participants), pragmatic study design provides a foundation for large-scale pragmatic studies in which outcomes or adherence can be reliably assessed with user-owned devices. (Funded by Apple; Apple Heart Study ClinicalTrials.gov number, NCT03335800.).