00172-9/asset/e456ccd0-792d-4182-a8f4-0d8e3068e29f/main.assets/gr1.jpg "AIモデルは心電図から早期死亡を含む健康リスクを予測できる(AI model can predict health risks, including early death, from ECGs)")
2024-10-24 インペリアル・カレッジ・ロンドン(ICL)
<関連情報>
- https://www.imperial.ac.uk/news/257300/ai-model-predict-health-risks-including/
- https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00172-9/fulltext
死亡率と心血管リスク推定のための人工知能対応心電図:モデル開発と検証研究 Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study
Arunashis Sau, PhD∙ Libor Pastika, MBBS∙ Ewa Sieliwonczyk, PhD∙ Konstantinos Patlatzoglou, PhD∙ Antoônio H Ribeiro, PhD∙ Kathryn A McGurk, PhD∙ et al.
Lancet Digital Health Published: November 2024
DOI:https://doi.org/10.1016/S2589-7500(24)00172-9
00172-9/asset/5b4d3603-9750-4a20-a2f8-1d30fa2c1c34/main.assets/gr1_lrg.jpg "AIモデルは心電図から早期死亡を含む健康リスクを予測できる(AI model can predict health risks, including early death, from ECGs)")
Summary
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.
