人工智能心电图用于死亡率和心血管风险评估：一项模型开发和验证研究。

Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study.

机构信息

National Heart and Lung Institute, Imperial College London, London, UK; Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK.

National Heart and Lung Institute, Imperial College London, London, UK.

出版信息

Lancet Digit Health. 2024 Nov;6(11):e791-e802. doi: 10.1016/S2589-7500(24)00172-9.

DOI:10.1016/S2589-7500(24)00172-9

PMID:39455192

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.

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.

摘要

背景

人工智能 (AI) 心电图 (ECG) 可用于预测未来疾病和死亡风险，但尚未在临床实践中采用。现有的模型预测在个体患者层面上没有可操作性、可解释性或生物学合理性。我们试图通过开发人工智能心电图风险评估器 (AIRE) 平台来解决以前 AI-ECG 方法的这些局限性。

方法

AIRE 平台是在贝斯以色列女执事医疗中心 (BIDMC) 的二级护理数据集和一个离散时间生存模型中开发的，该数据集包含来自 189539 名患者的 1163401 份心电图，用于创建具有单个心电图的患者特异性生存曲线。因此，AIRE 不仅预测死亡率，还预测死亡率时间。AIRE 在来自美国、巴西和英国的五个不同的跨国队列（英国生物库 [UKB]）中进行了验证，包括志愿者、初级保健患者和二级保健患者。

结果

AIRE 准确预测全因死亡率风险（BIDMC C 指数 0.775，95%CI 0.773-0.776；外部验证数据集的 C 指数 0.638-0.773）、未来室性心律失常（BIDMC C 指数 0.760，95%CI 0.756-0.763；UKB C 指数 0.719，95%CI 0.635-0.803）、未来动脉粥样硬化性心血管疾病（0.696，0.694-0.698；0.643，0.624-0.662）和未来心力衰竭（0.787，0.785-0.789；0.768，0.733-0.802）。通过表型和基因组范围的关联研究，我们确定了预测风险增加的候选生物学途径，包括心脏结构和功能的变化，以及与心脏结构、生物衰老和代谢综合征相关的基因。