美国纽约州西奈山伊坎医学院Ron Do团队研究了基于机器学习的冠状动脉疾病标志物。2022年12月20日出版的《柳叶刀》杂志发表了这项成果。
冠状动脉疾病的二元诊断不能描述疾病的复杂性，也不能量化其严重程度或与死亡相关的风险；因此，冠状动脉疾病的定量标志物是有必要的。研究组评估了从机器学习模型概率得出的冠状动脉疾病的定量标记。
在这项队列研究中，研究组使用95935份电子健康记录开发并验证了一个冠状动脉疾病预测机器学习模型，并评估了两个纵向生物库队列中参与者的冠状动脉疾病计算机评分（ISCAD；范围0[最低概率]至1[最高概率]）的概率。他们测量了ISCAD与临床预后的相关性，即冠状动脉狭窄、阻塞性冠状动脉疾病、多支冠状动脉疾病、全因死亡和冠状动脉疾病后遗症。
95935名参与者中，35749名来自BioMe生物银行（平均年龄61岁；14599人[41%]为男性，21150人[59%]为女性；5130人[14%]确诊冠状动脉疾病），60186人来自英国生物银行（中位年龄62岁；25031人[42%]为男性，35155人[58%]为女性，8128人[14%]确诊冠心病）。
该模型预测的冠状动脉疾病的受试者操作特征曲线下面积分别为0.95（敏感性为0.94，特异性为0.82）和0.93（敏感性为0.99，特异性为0.98），英国生物银行外部测试集的灵敏度为0.84，特异性为0.83。ISCAD从已知的风险因素、合并队列方程和多基因风险评分中获取冠状动脉疾病风险。冠状动脉狭窄随着ISCAD四分位数的增加而定量增加（每四分位数增加12个百分点），包括阻塞性冠状动脉疾病、多支冠状动脉疾病和主要冠状动脉狭窄的风险。
危险比（HRs）和全因死亡的患病率在《国际标准分类法》十分位数中逐步增加（十分位数1：HR 1.0，患病率为0.2%；十分位数6：11，发病率为3.1%；十分位数10：56，11%）。复发性心肌梗死也有类似的趋势。根据2014年美国心脏病学会/美国心脏协会工作组指南，12名（46%）未确诊的高ISCAD（≥0.9）患者有冠状动脉疾病的临床证据。
研究结果表明，基于电子健康记录的机器学习可用于生成冠状动脉疾病的电子标记，该标记可以在连续频谱上无创地量化动脉粥样硬化和死亡风险，并识别诊断不足的个体。
附：英文原文
Title: Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts
Author: Iain S Forrest, Ben O Petrazzini, áine Duffy, Joshua K Park, Carla Marquez-Luna, Daniel M Jordan, Ghislain Rocheleau, Judy H Cho, Robert S Rosenson, Jagat Narula, Girish N Nadkarni, Ron Do
Issue&Volume: 2022-12-20
Abstract:
Background
Binary diagnosis of coronary artery disease does not preserve the complexity of disease or quantify its severity or its associated risk with death; hence, a quantitative marker of coronary artery disease is warranted. We evaluated a quantitative marker of coronary artery disease derived from probabilities of a machine learning model.
Methods
In this cohort study, we developed and validated a coronary artery disease-predictive machine learning model using 95935 electronic health records and assessed its probabilities as in-silico scores for coronary artery disease (ISCAD; range 0 [lowest probability] to 1 [highest probability]) in participants in two longitudinal biobank cohorts. We measured the association of ISCAD with clinical outcomes—namely, coronary artery stenosis, obstructive coronary artery disease, multivessel coronary artery disease, all-cause death, and coronary artery disease sequelae.
Findings
Among 95935 participants, 35749 were from the BioMe Biobank (median age 61 years [IQR 18]; 14599 [41%] were male and 21150 [59%] were female; 5130 [14%] were with diagnosed coronary artery disease) and 60186 were from the UK Biobank (median age 62 [15] years; 25031 [42%] male and 35155 [58%] female; 8128 [14%] with diagnosed coronary artery disease). The model predicted coronary artery disease with an area under the receiver operating characteristic curve of 0·95 (95% CI 0·94–0·95; sensitivity of 0·94 [0·94–0·95] and specificity of 0·82 [0·81–0·83]) and 0·93 (0·92–0·93; sensitivity of 0·90 [0·89–0·90] and specificity of 0·88 [0·87–0·88]) in the BioMe validation and holdout sets, respectively, and 0·91 (0·91–0·91; sensitivity of 0·84 [0·83–0·84] and specificity of 0·83 [0·82–0·83]) in the UK Biobank external test set. ISCAD captured coronary artery disease risk from known risk factors, pooled cohort equations, and polygenic risk scores. Coronary artery stenosis increased quantitatively with ascending ISCAD quartiles (increase per quartile of 12 percentage points), including risk of obstructive coronary artery disease, multivessel coronary artery disease, and stenosis of major coronary arteries. Hazard ratios (HRs) and prevalence of all-cause death increased stepwise over ISCAD deciles (decile 1: HR 1·0 [95% CI 1·0–1·0], 0·2% prevalence; decile 6: 11 [3·9–31], 3·1% prevalence; and decile 10: 56 [20–158], 11% prevalence). A similar trend was observed for recurrent myocardial infarction. 12 (46%) undiagnosed individuals with high ISCAD (≥0·9) had clinical evidence of coronary artery disease according to the 2014 American College of Cardiology/American Heart Association Task Force guidelines.
Interpretation
Electronic health record-based machine learning was used to generate an in-silico marker for coronary artery disease that can non-invasively quantify atherosclerosis and risk of death on a continuous spectrum, and identify underdiagnosed individuals.
DOI: 10.1016/S0140-6736(22)02079-7
Source: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(22)02079-7/fulltext
LANCET：《柳叶刀》，创刊于1823年。隶属于爱思唯尔出版社，最新IF：202.731
官方网址：http://www.thelancet.com/
投稿链接：http://ees.elsevier.com/thelancet
本期文章：《柳叶刀》：Online/在线发表
