Electrocardiogram-Based Machine Learning Emulator Model for Predicting Novel Echocardiography-Derived Phenogroups for Cardiac Risk-Stratification: A Prospective Multicenter Cohort Study.

Purpose: Electrocardiography (ECG)-derived machine learning models can predict echocardiography (echo)-derived indices of systolic or diastolic function. However, systolic and diastolic dysfunction frequently coexists, which necessitates an integrated assessment for optimal risk-stratification. We explored an ECG-derived model that emulates an echo-derived model that combines multiple parameters for identifying patient phenogroups at risk for major adverse cardiac events (MACE).

High Prevalence of Pericardial Involvement in College Student Athletes Recovering From COVID-19.

Objectives: This study sought to explore the spectrum of cardiac abnormalities in student athletes who returned to university campus in July 2020 with uncomplicated coronavirus disease 2019 (COVID-19).

Background: There is limited information on cardiovascular involvement in young individuals with mild or asymptomatic COVID-19.

Methods: Screening echocardiograms were performed in 54 consecutive student athletes (mean age 19 years; 85% male) who had positive results of reverse transcription polymerase chain reaction nasal swab testing of the upper respiratory tract or immunoglobulin G antibodies against severe acute respiratory syndrome coronavirus type 2.

Prediction of coronary artery calcium scoring from surface electrocardiogram in atherosclerotic cardiovascular disease: a pilot study.

Aims: Coronary artery calcium (CAC) scoring is an established tool for cardiovascular risk stratification. However, the lack of widespread availability and concerns about radiation exposure have limited the universal clinical utilization of CAC. In this study, we sought to explore whether machine learning (ML) approaches can aid cardiovascular risk stratification by predicting guideline recommended CAC score categories from clinical features and surface electrocardiograms.