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Background: Disorders affecting cardiac conduction are associated with substantial morbidity. Understanding the epidemiology and risk factors for conduction disorders may enable earlier diagnosis and preventive efforts., Objectives: The purpose of this study was to quantify contemporary frequency and risk factors for electrocardiogram (ECG)-defined cardiac conduction disorders in a large multi-institutional primary care sample., Methods: We quantified prevalence and incidence of conduction disorders among adults receiving longitudinal primary care between 2001 and 2019, each with at least one 12-lead ECG performed prior to the start of follow-up and at least one ECG during follow-up. We defined conduction disorders using curated terms extracted from ECG diagnostic statements by cardiologists. We grouped conduction disorders by inferred anatomic location of abnormal conduction. We tested associations between clinical factors and incident conduction disease using multivariable proportional hazards regression., Results: We analyzed 189,163 individuals (median age 55 years; 58% female). The overall prevalence of conduction disorders was 27% among men and 15% among women. Among 119,926 individuals (median age 55 years; 51% female), 6,802 developed an incident conduction system abnormality over a median of 10 years (Q1, Q3: 6, 15 years) of follow-up. Incident conduction disorders were more common in men (8.78 events/1,000 person-years) vs women (4.34 events/1,000 person-years, P  < 0.05). In multivariable models, clinical factors including older age (HR: 1.25 per 5-year increase [95% CI: 1.24-1.26]) and myocardial infarction (HR: 1.39 [95% CI: 1.26-1.54]) were associated with incident conduction disorders., Conclusions: Cardiac conduction disorders are common in a primary care population, especially among older individuals with cardiovascular risk factors., Competing Interests: Dr Lubitz is a full-time employee of Novartis as of July 2022. He received sponsored research support from Bristol Myers Squibb, Pfizer, Boehringer Ingelheim, Fitbit, Medtronic, Premier, and IBM, and has consulted for Bristol Myers Squibb, Pfizer, Blackstone Life Sciences, and Invitae. Dr Ellinor receives sponsored research support from Bayer AG and IBM and has consulted for Novartis, MyoKardia, and Bayer AG. Dr Ho has received sponsored research support from Bayer AG. Dr Batra receives sponsored research support from Bayer AG and IBM and has consulted for Novartis and Prometheus Biosciences. Dr Philippakis is a Venture Partners and employee at GV, has consulted for Novartis and Rakuten, and receives sponsored research support from Verily, Microsoft, IBM, Intel, Pfizer, Abbvie, Biogen, Ionnis and Bayer. Investigators were supported by 10.13039/100000002National Institutes of Health grants K23HL169839-01 (Dr Khurshid), R38HL150212 (Dr Haimovich); R01HL139731 (Dr Lubitz), R01HL157635 (Dr Lubitz), R01HL134893 (Dr Ho), R01HL140224 (Dr Ho), K24HL153669 (Dr Ho), 2R01HL092577 (Dr Ellinor), and 1R01AG066010, R01HL092577 (Dr Benjamin); 10.13039/100000968American Heart Association grants 18SFRN34250007 (Dr Lubitz), AF 18SFRN34110082 (Dr Benjamin), 2023CDA1050571 (Dr Khurshid) and 18SFRN34110082 (Dr Ellinor); and by a grant from the European Union, MAESTRIA 965286 (Dr Ellinor). Dr Kornej has received funding from the 10.13039/100018694Marie Sklodowska-Curie Actions under the European Union’s 10.13039/100010661Horizon 2020 research and innovation programme (Agreement No 838259). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© 2024 The Authors.)

Background: Deep learning interpretation of echocardiographic images may facilitate automated assessment of cardiac structure and function., Objectives: We developed a deep learning model to interpret echocardiograms and examined the association of deep learning-derived echocardiographic measures with incident outcomes., Methods: We trained and validated a 3-dimensional convolutional neural network model for echocardiographic view classification and quantification of left atrial dimension, left ventricular wall thickness, chamber diameter, and ejection fraction. The training sample comprised 64,028 echocardiograms (n = 27,135) from a retrospective multi-institutional ambulatory cardiology electronic health record sample. Validation was performed in a separate longitudinal primary care sample and an external health care system data set. Cox models evaluated the association of model-derived left heart measures with incident outcomes., Results: Deep learning discriminated echocardiographic views (area under the receiver operating curve >0.97 for parasternal long axis, apical 4-chamber, and apical 2-chamber views vs human expert annotation) and quantified standard left heart measures (R 2 range = 0.53 to 0.91 vs study report values). Model performance was similar in 2 external validation samples. Model-derived left heart measures predicted incident heart failure, atrial fibrillation, myocardial infarction, and death. A 1-SD lower model-left ventricular ejection fraction was associated with 43% greater risk of heart failure (HR: 1.43; 95% CI: 1.23-1.66) and 17% greater risk of death (HR: 1.17; 95% CI: 1.06-1.30). Similar results were observed for other model-derived left heart measures., Conclusions: Deep learning echocardiographic interpretation accurately quantified standard measures of left heart structure and function, which in turn were associated with future clinical outcomes. Deep learning may enable automated echocardiogram interpretation and disease prediction at scale., Competing Interests: Funding Support and Author Disclosures Dr Lau is supported by the National Institutes of Health (NIH) (K23-HL159243) and the American Heart Association (AHA) (853922). Dr Al-Alusi is supported by the NIH (T32-HL007208). Dr Ellinor is supported by the NIH (1R01HL092577, K24HL105780), AHA (18SFRN34110082), Foundation Leducq (14CVD01), and by MAESTRIA (965286). Dr Lubitz was supported by NIH grants 1R01HL139731 and R01HL157635, and AHA 18SFRN34250007 during this work. Dr Ho is supported by the NIH (R01 HL134893, R01 HL140224, R0 1HL160003, and K24 HL153669). Dr Lau has consulted for Roche Diagnostics. Dr Di Achille is a full-time employee of Google as of June 2022. Ms Kopparapu is a full-time employee of DeepMind as of August 2022. Dr Batra is a full-time employee of Flagship Pioneering as of January 2023. Dr Batra has received sponsored research support from Bayer AG and IBM; and has consulted for Novartis and Prometheus Biosciences. Dr Ellinor has received sponsored research support from Bayer AG and IBM Health; and has served on advisory boards or consulted for Bayer AG, Quest Diagnostics, MyoKardia, and Novartis. Dr Picard has consulted for Vertex. Dr Ho has received past sponsored research support from Bayer AG. Dr Lubitz is a full-time employee of Novartis as of July 2022; has received sponsored research support from Bristol Myers Squibb, Pfizer, Boehringer Ingelheim, Fitbit, Medtronic, Premier, and IBM; and has consulted for Bristol Myers Squibb, Pfizer, Blackstone Life Sciences, and Invitae. All other authors have reported that they had no relationships relevant to the contents of this paper to disclose., (Copyright © 2023 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background: Differentiating among cardiac diseases associated with left ventricular hypertrophy (LVH) informs diagnosis and clinical care., Objective: To evaluate if artificial intelligence-enabled analysis of the 12-lead electrocardiogram (ECG) facilitates automated detection and classification of LVH., Methods: We used a pretrained convolutional neural network to derive numerical representations of 12-lead ECG waveforms from patients in a multi-institutional healthcare system who had cardiac diseases associated with LVH (n = 50,709), including cardiac amyloidosis (n = 304), hypertrophic cardiomyopathy (n = 1056), hypertension (n = 20,802), aortic stenosis (n = 446), and other causes (n = 4766). We then regressed LVH etiologies relative to no LVH on age, sex, and the numerical 12-lead representations using logistic regression ("LVH-Net"). To assess deep learning model performance on single-lead data analogous to mobile ECGs, we also developed 2 single-lead deep learning models by training models on lead I ("LVH-Net Lead I") or lead II ("LVH-Net Lead II") from the 12-lead ECG. We compared the performance of the LVH-Net models to alternative models fit on (1) age, sex, and standard ECG measures, and (2) clinical ECG-based rules for diagnosing LVH., Results: The areas under the receiver operator characteristic curve of LVH-Net by specific LVH etiology were cardiac amyloidosis 0.95 [95% CI, 0.93-0.97], hypertrophic cardiomyopathy 0.92 [95% CI, 0.90-0.94], aortic stenosis LVH 0.90 [95% CI, 0.88-0.92], hypertensive LVH 0.76 [95% CI, 0.76-0.77], and other LVH 0.69 [95% CI 0.68-0.71]. The single-lead models also discriminated LVH etiologies well., Conclusion: An artificial intelligence-enabled ECG model is favorable for detection and classification of LVH and outperforms clinical ECG-based rules., (© 2023 Pub-.)

Background: The Affordable Care Act of 2010 extended health insurance through expansion of Medicaid and subsidies for commercial insurance. Prior work has produced differing results in associating expanded insurance with improvements in health care processes and outcomes. Evaluating specific mechanisms of care processes and their association with insurance expansion may help reconcile those results., Methods and Results: We used inpatient hospitalization data in the Nationwide Inpatient Sample (NIS) Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality from 1/1/2008 to 9/30/2015. We included all hospitalizations for acute myocardial infarction (AMI). As a primary outcome, we defined percent rate of AMI hospitalizations receiving percutaneous coronary intervention (PCI) per month. In the non-Medicare (intervention) group, there was a relative decrease of 0.2% of the monthly trend before and after expansion (95% CI [-0.3%, -0.1%]). In the Medicare group, there was a relative decrease of 0.1% of the monthly trend before and after expansion (95% CI [-0.2%, 0%])., Conclusions: We did not detect a relative difference in PCI for AMI associated with insurance expansion under health reform., Competing Interests: Declaration of competing interest Dr. Haimovich and Ms. Cui have nothing to disclose. Dr. Yeh reports grants and personal fees from Abbott Vascular, grants and personal fees from AstraZeneca, grants and personal fees from Boston Scientific, grants and personal fees from Medtronic, personal fees from Elixir Medical, personal fees from Shockwave Medical, outside the submitted work. Dr. Wasfy reports consulting fees from Pfizer. Dr. Ferris is the national director of transformation of the National Health Service and has served as the CEO of the Massachusetts General Physicians Organization and has served on the board of Health Catalyst., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Electronic health record (EHR) datasets are statistically powerful but are subject to ascertainment bias and missingness. Using the Mass General Brigham multi-institutional EHR, we approximated a community-based cohort by sampling patients receiving longitudinal primary care between 2001-2018 (Community Care Cohort Project [C3PO], n = 520,868). We utilized natural language processing (NLP) to recover vital signs from unstructured notes. We assessed the validity of C3PO by deploying established risk models for myocardial infarction/stroke and atrial fibrillation. We then compared C3PO to Convenience Samples including all individuals from the same EHR with complete data, but without a longitudinal primary care requirement. NLP reduced the missingness of vital signs by 31%. NLP-recovered vital signs were highly correlated with values derived from structured fields (Pearson r range 0.95-0.99). Atrial fibrillation and myocardial infarction/stroke incidence were lower and risk models were better calibrated in C3PO as opposed to the Convenience Samples (calibration error range for myocardial infarction/stroke: 0.012-0.030 in C3PO vs. 0.028-0.046 in Convenience Samples; calibration error for atrial fibrillation 0.028 in C3PO vs. 0.036 in Convenience Samples). Sampling patients receiving regular primary care and using NLP to recover missing data may reduce bias and maximize generalizability of EHR research., (© 2022. The Author(s).)

Aims: Physical activity may be an important modifiable risk factor for atrial fibrillation (AF), but associations have been variable and generally based on self-reported activity., Methods and Results: We analysed 93 669 participants of the UK Biobank prospective cohort study without prevalent AF who wore a wrist-based accelerometer for 1 week. We categorized whether measured activity met the standard recommendations of the European Society of Cardiology, American Heart Association, and World Health Organization [moderate-to-vigorous physical activity (MVPA) ≥150 min/week]. We tested associations between guideline-adherent activity and incident AF (primary) and stroke (secondary) using Cox proportional hazards models adjusted for age, sex, and each component of the Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) risk score. We also assessed correlation between accelerometer-derived and self-reported activity. The mean age was 62 ± 8 years and 57% were women. Over a median of 5.2 years, 2338 incident AF events occurred. In multivariable adjusted models, guideline-adherent activity was associated with lower risks of AF [hazard ratio (HR) 0.82, 95% confidence interval (CI) 0.75-0.89; incidence 3.5/1000 person-years, 95% CI 3.3-3.8 vs. 6.5/1000 person-years, 95% CI 6.1-6.8] and stroke (HR 0.76, 95% CI 0.64-0.90; incidence 1.0/1000 person-years, 95% CI 0.9-1.1 vs. 1.8/1000 person-years, 95% CI 1.6-2.0). Correlation between accelerometer-derived and self-reported MVPA was weak (Spearman r = 0.16, 95% CI 0.16-0.17). Self-reported activity was not associated with incident AF or stroke., Conclusions: Greater accelerometer-derived physical activity is associated with lower risks of AF and stroke. Future preventive efforts to reduce AF risk may be most effective when targeting adherence to objective activity thresholds., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2021. For permissions, please email: journals.permissions@oup.com.)

Background: The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI., Methods and Findings: We used the same cohort and candidate variables used to develop the current NCDR CathPCI Registry AKI model, including 947,091 patients who underwent PCI procedures between June 1, 2009, and June 30, 2011. The mean age of these patients was 64.8 years, and 32.8% were women, with a total of 69,826 (7.4%) AKI events. We replicated the current AKI model as the baseline model and compared it with a series of new models. Temporal validation was performed using data from 970,869 patients undergoing PCIs between July 1, 2016, and March 31, 2017, with a mean age of 65.7 years; 31.9% were women, and 72,954 (7.5%) had AKI events. Each model was derived by implementing one of two strategies for preprocessing candidate variables (preselecting and transforming candidate variables or using all candidate variables in their original forms), one of three variable-selection methods (stepwise backward selection, lasso regularization, or permutation-based selection), and one of two methods to model the relationship between variables and outcome (logistic regression or gradient descent boosting). The cohort was divided into different training (70%) and test (30%) sets using 100 different random splits, and the performance of the models was evaluated internally in the test sets. The best model, according to the internal evaluation, was derived by using all available candidate variables in their original form, permutation-based variable selection, and gradient descent boosting. Compared with the baseline model that uses 11 variables, the best model used 13 variables and achieved a significantly better area under the receiver operating characteristic curve (AUC) of 0.752 (95% confidence interval [CI] 0.749-0.754) versus 0.711 (95% CI 0.708-0.714), a significantly better Brier score of 0.0617 (95% CI 0.0615-0.0618) versus 0.0636 (95% CI 0.0634-0.0638), and a better calibration slope of observed versus predicted rate of 1.008 (95% CI 0.988-1.028) versus 1.036 (95% CI 1.015-1.056). The best model also had a significantly wider predictive range (25.3% versus 21.6%, p < 0.001) and was more accurate in stratifying AKI risk for patients. Evaluated on a more contemporary CathPCI cohort (July 1, 2015-March 31, 2017), the best model consistently achieved significantly better performance than the baseline model in AUC (0.785 versus 0.753), Brier score (0.0610 versus 0.0627), calibration slope (1.003 versus 1.062), and predictive range (29.4% versus 26.2%). The current study does not address implementation for risk calculation at the point of care, and potential challenges include the availability and accessibility of the predictors., Conclusions: Machine learning techniques and data-driven approaches resulted in improved prediction of AKI risk after PCI. The results support the potential of these techniques for improving risk prediction models and identification of patients who may benefit from risk-mitigation strategies., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: SSD is supported by the Department of Veterans Affairs. WLS is a consultant for Hugo, a personal health information platform. CIM is a consultant for Cook, Bard, Medtronic, Abbott and Cardinal Health. FPW is supported by the National Science Foundation grant R01DK113191. JSR is the Chief Innovation Officer for the American College of Cardiology. HMK is a recipient of research agreements from Medtronic and from Johnson & Johnson (Janssen), through Yale University, to develop methods of clinical trial data sharing; was the recipient of a grant from the Food and Drug Administration and Medtronic to develop methods for postmarket surveillance of medical devices; works under contract with the Centers for Medicare and Medicaid Services to develop and maintain performance measures; chairs a cardiac scientific advisory board for UnitedHealth; is a member of the Advisory Board for Element Science and the Physician Advisory Board for Aetna; is a participant/participant representative of the IBM Watson Health Life Sciences Board; and is the founder of Hugo, a personal health information platform. JAS is supported by grants from Gilead, Genentech, Lilly, Amorcyte, and EvaHeart and has a patent for the Seattle Angina Questionnaire with royalties paid. He also owns the copyright to the Seattle Angina Questionnaire. He is the PI of an Analytic Center for the American College of Cardiology Foundation and has an equity interest in Health Outcomes Sciences. FAM has a contract (through his primary institution) for his role as Chief Science Officer of the NCDR. BJM is an associate editor for PLOS ONE, which is involved in this special issue. He has a relationship with the American College of Cardiology in selecting and pursuing innovative research based upon their registry data (unrelated to this paper). He has a pending patent application for an EHR-based prediction tool in Yale New Haven Health, as well as two funded studies, one by the DoD-Advanced Research Projects Agency and one with the NSF to support student travel to conferences in the body sensor networks field. American College of Cardiology may incorporate this work, or future iterations, into its registry. No other organisation named above has a competing interest in relation to this work. The other authors report no potential competing interests.

Identifying temporal variation in hospitalization rates may provide insights about disease patterns and thereby inform research, policy, and clinical care. However, the majority of medical conditions have not been studied for their potential seasonal variation. The objective of this study was to apply a data-driven approach to characterize temporal variation in condition-specific hospitalizations. Using a dataset of 34 million inpatient discharges gathered from hospitals in New York State from 2008-2011, we grouped all discharges into 263 clinical conditions based on the principal discharge diagnosis using Clinical Classification Software in order to mitigate the limitation that administrative claims data reflect clinical conditions to varying specificity. After applying Seasonal-Trend Decomposition by LOESS, we estimated the periodicity of the seasonal component using spectral analysis and applied harmonic regression to calculate the amplitude and phase of the condition's seasonal utilization pattern. We also introduced four new indices of temporal variation: mean oscillation width, seasonal coefficient, trend coefficient, and linearity of the trend. Finally, K-means clustering was used to group conditions across these four indices to identify common temporal variation patterns. Of all 263 clinical conditions considered, 164 demonstrated statistically significant seasonality. Notably, we identified conditions for which seasonal variation has not been previously described such as ovarian cancer, tuberculosis, and schizophrenia. Clustering analysis yielded three distinct groups of conditions based on multiple measures of seasonal variation. Our study was limited to New York State and results may not directly apply to other regions with distinct climates and health burden. A substantial proportion of medical conditions, larger than previously described, exhibit seasonal variation in hospital utilization. Moreover, the application of clustering tools yields groups of clinically heterogeneous conditions with similar seasonal phenotypes. Further investigation is necessary to uncover common etiologies underlying these shared seasonal phenotypes.