| Title: | Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study |
| Journal: | The Lancet Digital Health |
| Published: | 1 Nov 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39455192/ |
| DOI: | https://doi.org/10.1016/s2589-7500(24)00172-9 |
| URL: | http://dx.doi.org/10.1016/s2589-7500(24)00172-9 |
Publication 13180
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Abstract
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.</p>
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.</p>
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.</p>
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.</p>
FUNDING: British Heart Foundation, National Institute for Health and Care Research, and Medical Research Council.</p>
13 Keywords
- Adult
- Aged
- Artificial Intelligence
- Brazil
- Cardiovascular Diseases
- Deep Learning
- Electrocardiography
- Female
- Humans
- Male
- Middle Aged
- Risk Assessment
- United Kingdom
22 Authors
- Arunashis Sau
- Libor Pastika
- Ewa Sieliwonczyk
- Konstantinos Patlatzoglou
- Antoônio H Ribeiro
- Kathryn A McGurk
- Boroumand Zeidaabadi
- Henry Zhang
- Krzysztof Macierzanka
- Danilo Mandic
- Ester Sabino
- Luana Giatti
- Sandhi M Barreto
- Lidyane do Valle Camelo
- Ioanna Tzoulaki
- Declan P O'Regan
- Nicholas S Peters
- James S Ware
- Antonio Luiz P Ribeiro
- Daniel B Kramer
- Jonathan W Waks
- Fu Siong Ng
3 Applications
| Application ID | Title |
|---|---|
| 40616 | Machine learning discovery of genotype-phenotype associations in cardiovascular science. |
| 47602 | Understanding the genetic architecture of inherited cardiovascular conditions (ICCs) and cardio-renal disease |
| 48666 | Electrophysiological remodelling in patients at elevated risk of ventricular arrhythmia and sudden cardiac death |
Enabling scientific discoveries that improve human health