Department of Computer Science and Software Engineering, The University of Western Australia, Australia; Medical School, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia; Harry Perkins Institute of Medical Research, Perth, Australia.
Medical School, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia; Harry Perkins Institute of Medical Research, Perth, Australia.
Comput Methods Programs Biomed. 2023 Oct;240:107717. doi: 10.1016/j.cmpb.2023.107717. Epub 2023 Jul 9.
Cardiac exercise stress testing (EST) offers a non-invasive way in the management of patients with suspected coronary artery disease (CAD). However, up to 30% EST results are either inconclusive or non-diagnostic, which results in significant resource wastage. Our aim was to build machine learning (ML) based models, using patients demographic (age, sex) and pre-test clinical information (reason for performing test, medications, blood pressure, heart rate, and resting electrocardiogram), capable of predicting EST results beforehand including those with inconclusive or non-diagnostic results.
A total of 30,710 patients (mean age 54.0 years, 69% male) were included in the study with 25% randomly sampled in the test set, and the remaining samples were split into a train and validation set with a ratio of 9:1. We constructed different ML models from pre-test variables and compared their discriminant power using the area under the receiver operating characteristic curve (AUC).
A network of Oblivious Decision Trees provided the best discriminant power (AUC=0.83, sensitivity=69%, specificity=0.78%) for predicting inconclusive EST results. A total of 2010 inconclusive ESTs were correctly identified in the testing set.
Our ML model, developed using demographic and pre-test clinical information, can accurately predict EST results and could be used to identify patients with inconclusive or non-diagnostic results beforehand. Our system could thus be used as a personalised decision support tool by clinicians for optimizing the diagnostic test selection strategy for CAD patients and to reduce healthcare expenditure by reducing nondiagnostic or inconclusive ESTs.
心脏运动压力测试（EST）为疑似冠心病（CAD）患者的管理提供了一种非侵入性的方法。然而，高达 30%的 EST 结果要么不确定，要么无法诊断，这导致了大量资源的浪费。我们的目的是构建基于机器学习（ML）的模型，使用患者的人口统计学（年龄、性别）和测试前的临床信息（进行测试的原因、药物、血压、心率和静息心电图），能够提前预测 EST 结果，包括那些不确定或无法诊断的结果。
共纳入 30710 名患者（平均年龄 54.0 岁，69%为男性），其中 25%随机抽样入测试集，其余样本分为训练集和验证集，比例为 9:1。我们从测试前的变量构建了不同的 ML 模型，并使用接收者操作特征曲线下的面积（AUC）比较它们的判别能力。
一个遗忘决策树网络提供了预测不确定 EST 结果的最佳判别能力（AUC=0.83，敏感性=69%，特异性=0.78%）。在测试集中，共有 2010 个不确定的 EST 被正确识别。
我们使用人口统计学和测试前临床信息开发的 ML 模型可以准确预测 EST 结果，并可以用于提前识别不确定或无法诊断的结果。因此，我们的系统可以作为临床医生个性化的决策支持工具，用于优化 CAD 患者的诊断测试选择策略，并通过减少非诊断或不确定的 EST 来降低医疗保健支出。
