Schwalm J D, Di Shuang, Sheth Tej, Natarajan Madhu K, O'Brien Erin, McCready Tara, Petch Jeremy
Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Canada.
Department of Medicine, Division of Cardiology, McMaster University, Hamilton, Canada.
Cardiovasc Digit Health J. 2021 Dec 24;3(1):21-30. doi: 10.1016/j.cvdhj.2021.12.001. eCollection 2022 Feb.
Conventional clinical risk scores and diagnostic algorithms are proving to be suboptimal in the prediction of obstructive coronary artery disease, contributing to the low diagnostic yield of invasive angiography. Machine learning could help better predict which patients would benefit from invasive angiography vs other noninvasive diagnostic modalities.
To reduce patient risk and cost to the healthcare system by improving the diagnostic yield of invasive coronary angiography through optimized outpatient selection.
Retrospective analysis of 12 years of referral data from a provincial cardiac registry, including all patients referred for invasive angiography of more than 1.4 million individuals in Ontario, Canada. Stable outpatients undergoing coronary angiography during the study period were included in the analysis. The training set (80% random sample, n = 23,750) was used to develop 8 prediction models in Python using grid-search cross-validation. The test set (20% random sample, n = 5938), evaluated the discrimination performance of each model.
The machine-learning model achieved a substantially better performance (area under the receiver operating characteristics curve: 0.81) than existing models for predicting obstructive coronary artery disease in patients referred for invasive angiography. It significantly outperformed both the reference model and current clinical practice with a net reclassification index of 27.8% (95% confidence interval [CI]: [24.9%-30.8%], value <.01) and 44.7% (95% CI: [42.4%-47.0%], value <.01), respectively.
This prediction model, when coupled with a point-of-care, online decision support tool to be used by referring physicians, could improve the diagnostic yield of invasive coronary angiography in stable, elective outpatients, thus improving patient safety and reducing healthcare costs.
传统的临床风险评分和诊断算法在预测阻塞性冠状动脉疾病方面表现欠佳，导致侵入性血管造影的诊断率较低。机器学习有助于更好地预测哪些患者将从侵入性血管造影与其他非侵入性诊断方式中获益。
通过优化门诊患者选择来提高侵入性冠状动脉造影的诊断率，从而降低患者风险和医疗系统成本。
对来自省级心脏登记处的12年转诊数据进行回顾性分析，包括加拿大安大略省140多万例接受侵入性血管造影转诊的所有患者。分析纳入研究期间接受冠状动脉造影的稳定门诊患者。训练集（80%随机样本，n = 23750）用于在Python中使用网格搜索交叉验证开发8个预测模型。测试集（20%随机样本，n = 5938）评估每个模型的鉴别性能。
在预测接受侵入性血管造影患者的阻塞性冠状动脉疾病方面，机器学习模型的表现（受试者操作特征曲线下面积：0.81）明显优于现有模型。其净重新分类指数分别为27.8%（95%置信区间[CI]：[24.9%-30.8%]）和44.7%（95%CI：[42.4%-47.0%]），显著优于参考模型和当前临床实践（P值均<0.01）。
该预测模型与供转诊医生使用的即时在线决策支持工具相结合，可提高稳定择期门诊患者侵入性冠状动脉造影的诊断率，从而提高患者安全性并降低医疗成本。
