The subject of the PhysioNet/Computing in Cardiology Challenge 2020 was the identification of cardiac abnormalities in 12-lead electrocardiogram (ECG) recordings. A total of 66,405 recordings were sourced from hospital systems from four distinct countries and annotated with clinical diagnoses, including 43,101 annotated recordings that were posted publicly.For this Challenge, we asked participants to design working, open-source algorithms for identifying cardiac abnormalities in 12-lead ECG recordings. This Challenge provided several innovations. First, we sourced data from multiple institutions from around the world with different demographics, allowing us to assess the generalizability of the algorithms. Second, we required participants to submit both their trained models and the code for reproducing their trained models from the training data, which aids the generalizability and reproducibility of the algorithms. Third, we proposed a novel evaluation metric that considers different misclassification errors for different cardiac abnormalities, reflecting the clinical reality that some diagnoses have similar outcomes and varying risks.Over 200 teams submitted 850 algorithms (432 of which successfully ran) during the unofficial and official phases of the Challenge, representing a diversity of approaches from both academia and industry for identifying cardiac abnormalities. The official phase of the Challenge is ongoing.

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Objective: Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs for follow-up diagnostic screening and treatment, especially in resource-constrained environments. However, experts are needed to interpret the heart sound recordings, limiting the accessibility of auscultation for cardiac care. The George B. Moody PhysioNet Challenge 2022 invites teams to develop automated approaches for detecting abnormal heart function from multi-location phonocardiogram (PCG) recordings of heart sounds. Approach: For the Challenge, we sourced 5272 PCG recordings from 1568 pediatric patients in rural Brazil. We required the Challenge participants to submit the complete code for training and running their models, improving the transparency, reproducibility, and utility of the diagnostic algorithms. We devised a cost-based evaluation metric that captures the costs of screening, treatment, and diagnostic errors, allowing us to investigate the benefits of algorithmic pre-screening and facilitate the development of more clinically relevant algorithms. Main results: So far, over 80 teams have submitted over 600 algorithms during the course of the Challenge, representing a diversity of approaches in academia and industry. We will update this manuscript to share an analysis of the Challenge after the end of the Challenge. Significance: The use of heart sound recordings for both heart murmur detection and clinical outcome identification allowed us to explore the potential of automated approaches to provide accessible pre-screening of less-resourced populations. The submission of working, open-source algorithms and the use of novel evaluation metrics supported the reproducibility, generalizability, and relevance of the researched conducted during the Challenge.

Background Global electrical heterogeneity (GEH) is associated with sudden cardiac death (SCD) in adults of 45 years and above. However, GEH has not been previously measured in young athletes. The goal of this study was to establish a reference for vectorcardiograpic (VCG) metrics in male and female athletes. Methods Skiers (n = 140; mean age 19.2 ± 3.5 years; 66% male, 94% white; 53% professional athletes) were enrolled in a prospective cohort. Resting 12‐lead ECGs were interpreted per the International ECG criteria. Associations of age, sex, and athletic performance with GEH were studied. Results In age and training level‐adjusted analyses, male sex was associated with a larger T vector [T peak magnitude +186 (95% CI 106–266) µV] and a wider spatial QRS‐T angle [+28.2 (17.3–39.2)°] as compared to women. Spatial QRS‐T angle in the ECG left ventricular hypertrophy (LVH) voltage group (n = 21; 15%) and normal ECG group did not differ (67.7 ± 25.0 vs. 66.8 ± 28.2; p = 0.914), suggesting that ECG LVH voltage in athletes reflects physiological remodeling. In contrast, skiers with right ventricular hypertrophy (RVH) voltage (n = 26, 18.6%) had wider QRS‐T angle (92.7 ± 29.6 vs. 66.8 ± 28.2°; p = 0.001), larger SAI QRST (194.9 ± 30.2 vs. 157.8 ± 42.6 mV × ms; p < 0.0001), but similar peak SVG vector magnitude (1976 ± 548 vs. 1939 ± 395 µV; p = 0.775) as compared to the normal ECG group. Better athletic performance was associated with the narrower QRS‐T angle. Each 10% worsening in an athlete's Federation Internationale de’ Ski downhill ranking percentile was associated with an increase in spatial QRS‐T angle by 2.1 (95% CI 0.3–3.9) degrees (p = 0.013). Conclusion Vectorcardiograpic adds nuances to ECG phenomena in athletes.

Aim —Our goal was to investigate the effect of a global XYZ median beat construction and the heart vector origin point definition on predictive accuracy of ECG biomarkers of sudden cardiac death (SCD). Methods —Atherosclerosis Risk In Community study participants with analyzable digital ECGs were included (n=15,768; 55% female, 73% white, mean age 54.2±5.8 y). We developed an algorithm to automatically detect the heart vector origin point on a median beat. Three different approaches to construct a global XYZ beat and two methods to locate origin point were compared. Global electrical heterogeneity was measured by sum absolute QRST integral (SAI QRST), spatial QRS-T angle, and spatial ventricular gradient (SVG) magnitude, azimuth, and elevation. Adjudicated SCD served as the primary outcome. Results —There was high intra-observer (kappa 0.972) and inter-observer (kappa 0.984) agreement in a heart vector origin definition between an automated algorithm and a human. QRS was wider in a median beat that was constructed using R-peak alignment than in time-coherent beat (88.1±16.7 vs. 83.7±15.9 ms; P<0.0001), and on a median beat constructed using QRS-onset as a zeroed baseline, vs. isoelectric origin point (86.7±15.9 vs. 83.7±15.9 ms; P<0.0001). ROC AUC was significantly larger for QRS, QT, peak QRS-T angle, SVG elevation, and SAI QRST if measured on a time-coherent median beat, and for SAI QRST and SVG magnitude if measured on a median beat using isoelectric origin point. Conclusion —Time-coherent global XYZ median beat with physiologically meaningful definition of the heart vector’s origin point improved predictive accuracy of SCD biomarkers.