Liu Yan, Ren Haoxing, Fanous Hanna, Dai Xuming, Wolf Hope M, Wade Tyrone C, Ramm Cassandra J, Stouffer George A
Dell Medical School, The University of Texas at Austin, Austin, Texas.
Division of Cardiology, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Cardiovasc Digit Health J. 2022 Mar 7;3(3):112-117. doi: 10.1016/j.cvdhj.2022.02.002. eCollection 2022 Jun.
Coronary artery disease (CAD) costs healthcare billions of dollars annually and is the leading cause of death despite available noninvasive diagnostic tools.
This study aims to examine the usefulness of machine learning in predicting hemodynamically significant CAD using routine demographics, clinical factors, and laboratory data.
Consecutive patients undergoing cardiac catheterization between March 17, 2015, and July 15, 2016, at UNC Chapel Hill were screened for comorbidities and CAD risk factors. In this pilot, single-center, prospective cohort study, patients were screened and selected for moderate CAD risk (n = 185). Invasive coronary angiography and CAD prediction with machine learning were independently performed. Results were blinded from operators and patients. Outcomes were followed up for up to 90 days for major adverse cardiovascular and renal events (MACREs). Greater than 70% stenosis or a fractional flow reserve less than or equal to 0.8 represented hemodynamically significant coronary disease. A random forest model using demographic, comorbidities, risk factors, and lab data was trained to predict CAD severity. The Random Forest Model predictive accuracy was assessed by area under the receiver operating characteristic curve with comparison to the final diagnoses made from coronary angiography.
Hemodynamically significant CAD was predicted by 18-point clinical data input with a sensitivity of 81% ± 7.8%, and specificity of 61% ± 14.4% by the established model. The best machine learning model predicted a 90-day MACRE with specificity of 44.61% ± 14.39%, and sensitivity of 57.13% ± 18.70%.
Machine learning models based on routine demographics, clinical factors, and lab data can be used to predict hemodynamically significant CAD with accuracy that approximates current noninvasive functional modalities.
冠状动脉疾病（CAD）每年给医疗保健带来数十亿美元的成本，尽管有可用的非侵入性诊断工具，但它仍是主要的死亡原因。
本研究旨在探讨机器学习在利用常规人口统计学、临床因素和实验室数据预测血流动力学显著CAD方面的有用性。
对2015年3月17日至2016年7月15日在北卡罗来纳大学教堂山分校接受心脏导管插入术的连续患者进行合并症和CAD危险因素筛查。在这项单中心前瞻性队列研究试点中，对患者进行筛查并选择具有中度CAD风险者（n = 185）。独立进行有创冠状动脉造影和利用机器学习进行CAD预测。结果对操作者和患者保密。对主要不良心血管和肾脏事件（MACREs）进行长达90天的随访。大于70% 的狭窄或小于或等于0.8的血流储备分数表示血流动力学显著的冠状动脉疾病。使用人口统计学、合并症、危险因素和实验室数据训练随机森林模型以预测CAD严重程度。通过与冠状动脉造影做出的最终诊断进行比较，用受试者操作特征曲线下面积评估随机森林模型的预测准确性。
通过18点临床数据输入预测血流动力学显著CAD，既定模型的敏感性为81% ± 7.8%，特异性为61% ± 14.4%。最佳机器学习模型预测90天MACRE的特异性为44.61% ± 14.39%，敏感性为57.13% ± 18.70%。
基于常规人口统计学、临床因素和实验室数据的机器学习模型可用于准确预测血流动力学显著的CAD，其准确性接近当前的非侵入性功能检查方法。
