ArticleJACC. Advances2026
Artificial Intelligence-Enabled Serial Electrocardiograms for Prediction of All-Cause Mortality in Secondary Care Settings.
Article in JACC. Advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
backgroundPrognostic assessment in secondary care settings remains challenging and may influence clinical decision-making and follow-up. Artificial intelligence-enabled electrocardiography (AI-ECG) is a promising tool, but the value of incorporating serial ECG data remains unclear.
objectivesThis study aimed to develop and validate an AI-ECG model for predicting 1-year all-cause mortality and to assess the incremental value of serial ECGs.
methodsWe conducted a retrospective cohort study of adults aged over 50 years treated in emergency department or inpatient settings between 2014 and 2019. Models were developed using a deep-learning framework applied to either a single index ECG or serial ECGs, including the index and 2 prior recordings. Data were divided into training, validation, and test sets. Performance was assessed using discrimination, calibration, and decision-curve analysis. A classification threshold was selected on the validation set by maximizing the F1 score and applied to the test set.
resultsA total of 13,417 patients were included (median age 69 years [IQR: 57-80]; 45% women), of whom 4,372 (34%) died within 1 year. The serial AI-ECG model achieved an area under the curve of 0.84 (95% CI: 0.824-0.862). At a threshold of 0.24, sensitivity was 70%, specificity was 80%, and positive predictive value was 49%. Calibration showed good agreement between predicted and observed risks (Brier score: 0.123). Decision-curve analysis demonstrated greater net benefit than treat-all and treat-none strategies. Addition of clinical variables beyond age and sex did not improve performance.
conclusionsA serial AI-ECG model provides accurate, clinically interpretable prediction of 1-year mortality and may support risk stratification in secondary care.
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