Peter Munk Cardiac Centre and Heart and Stroke Richard Lewar Centre, University of Toronto, Toronto, ON, Canada; ICES, Toronto, ON, Canada; Institute of Health Policy Management, and Evaluation, University of Toronto, ON, Canada; Instituto do Coracao (InCor), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, SP, Brazil.
Peter Munk Cardiac Centre and Heart and Stroke Richard Lewar Centre, University of Toronto, Toronto, ON, Canada.
Am Heart J. 2023 Feb;256:117-127. doi: 10.1016/j.ahj.2022.11.004. Epub 2022 Nov 11.
The ISCHEMIA trial showed similar cardiovascular outcomes of an initial conservative strategy as compared with invasive management in patients with stable ischemic heart disease without left main stenosis. We aim to assess the feasibility of predicting significant left main stenosis using extensive clinical, laboratory and non-invasive tests data.
All adult patients who had stress testing prior to undergoing an elective coronary angiography for stable ischemic heart disease in Ontario, Canada, between April 2010 and March 2019, were included. Candidate predictors included comprehensive demographics, comorbidities, laboratory tests, and cardiac stress test data. The outcome was stenosis of 50% or greater in the left main coronary artery. A traditional model (logistic regression) and a machine learning algorithm (boosted trees) were used to build prediction models.
Among 150,423 patients included (mean age: 64.2 ± 10.6 years; 64.1% males), there were 9,225 (6.1%) with left main stenosis. The final logistic regression model included 24 predictors and 3 interactions, had an optimism-adjusted c-statistic of 0.72 and adequate calibration (optimism-adjusted Integrated Calibration Index 0.0044). These results were consistent in subgroups of males and females, diabetes and non-diabetes, and extent of ischemia. The boosted tree algorithm had similar accuracy, also resulting in a c-statistic of 0.72 and adequate calibration (Integrated Calibration Index 0.0054).
In this large population-based study of patients with stable ischemic heart disease using extensive clinical data, only modest prediction of left main coronary artery disease was possible with traditional and machine learning modelling techniques.
ISCHEMIA 试验表明，在无左主干狭窄的稳定型缺血性心脏病患者中，与侵入性治疗相比，初始保守策略的心血管结局相似。我们旨在评估使用广泛的临床、实验室和非侵入性检查数据预测左主干显著狭窄的可行性。
纳入 2010 年 4 月至 2019 年 3 月期间在加拿大安大略省因稳定型缺血性心脏病进行选择性冠状动脉造影之前接受过应激试验的所有成年患者。候选预测因素包括全面的人口统计学、合并症、实验室检查和心脏应激试验数据。结局为左主干冠状动脉狭窄 50%或以上。采用传统模型（逻辑回归）和机器学习算法（增强树）构建预测模型。
在纳入的 150423 例患者中（平均年龄：64.2±10.6 岁；64.1%为男性），有 9225 例（6.1%）存在左主干狭窄。最终的逻辑回归模型纳入了 24 个预测因素和 3 个交互作用，经优化调整后的 C 统计量为 0.72，校准良好（优化调整后的综合校准指数为 0.0044）。这些结果在男性和女性、糖尿病和非糖尿病以及缺血程度的亚组中一致。增强树算法的准确性相似，也得到了 0.72 的 C 统计量和良好的校准（综合校准指数为 0.0054）。
在这项使用广泛临床数据的稳定型缺血性心脏病患者的大型基于人群研究中，传统和机器学习建模技术仅能对左主干冠状动脉疾病进行适度预测。
