Arunashis Sau PhD , Libor Pastika MBBS , Ewa Sieliwonczyk PhD , Konstantinos Patlatzoglou PhD , Antônio H Ribeiro PhD , Kathryn A McGurk PhD , Boroumand Zeidaabadi BSc , Henry Zhang BSc , Krzysztof Macierzanka BSc , Prof Danilo Mandic PhD , Prof Ester Sabino MD , Luana Giatti PhD , Prof Sandhi M Barreto PhD , Lidyane do Valle Camelo PhD , Prof Ioanna Tzoulaki PhD , Prof Declan P O'Regan PhD , Prof Nicholas S Peters MD , Prof James S Ware PhD , Prof Antonio Luiz P Ribeiro PhD , Daniel B Kramer MD , Fu Siong Ng PhD
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Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study
Background
Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions do not have actionability at an individual patient level, explainability, or biological plausibi. We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE) platform.
Methods
The AIRE platform was developed in a secondary care dataset (Beth Israel Deaconess Medical Center [BIDMC]) of 1 163 401 ECGs from 189 539 patients with deep learning and a discrete-time survival model to create a patient-specific survival curve with a single ECG. Therefore, AIRE predicts not only risk of mortality, but also time-to-mortality. AIRE was validated in five diverse, transnational cohorts from the USA, Brazil, and the UK (UK Biobank [UKB]), including volunteers, primary care patients, and secondary care patients.
Findings
AIRE accurately predicts risk of all-cause mortality (BIDMC C-index 0·775, 95% CI 0·773–0·776; C-indices on external validation datasets 0·638–0·773), future ventricular arrhythmia (BIDMC C-index 0·760, 95% CI 0·756–0·763; UKB C-index 0·719, 95% CI 0·635–0·803), future atherosclerotic cardiovascular disease (0·696, 0·694–0·698; 0·643, 0·624–0·662), and future heart failure (0·787, 0·785–0·789; 0·768, 0·733–0·802). Through phenome-wide and genome-wide association studies, we identified candidate biological pathways for the prediction of increased risk, including changes in cardiac structure and function, and genes associated with cardiac structure, biological ageing, and metabolic syndrome.
Interpretation
AIRE is an actionable, explainable, and biologically plausible AI-ECG risk estimation platform that has the potential for use worldwide across a wide range of clinical contexts for short-term and long-term risk estimation.
Funding
British Heart Foundation, National Institute for Health and Care Research, and Medical Research Council.
期刊介绍：
The Lancet Digital Health publishes important, innovative, and practice-changing research on any topic connected with digital technology in clinical medicine, public health, and global health.
The journal’s open access content crosses subject boundaries, building bridges between health professionals and researchers.By bringing together the most important advances in this multidisciplinary field,The Lancet Digital Health is the most prominent publishing venue in digital health.
We publish a range of content types including Articles,Review, Comment, and Correspondence, contributing to promoting digital technologies in health practice worldwide.
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