Prediction of significant coronary artery disease in patients undergoing operations for rheumatic mitral valve disease.

This study aims to develop a logistic regression model and a simple score system for the prediction of significant coronary artery disease (CAD) in patients undergoing operations for rheumatic mitral valve disease. A total of 1241 rheumatic patients (mean age 57±6 years), who underwent routine coronary angiography (CAG) before mitral valve operations between 1998 and 2009, was analyzed. To identify low-risk (≤5%) patients, a bootstrap refined logistic regression model on the basis of clinical risk factors was developed, from which an additive model was derived. Receiver operating characteristic (ROC) curves were used to compare discrimination, and precision was quantified by the Hosmer-Lemeshow statistic. Significant coronary atherosclerosis was defined as 50% or more luminal narrowing in one or more major epicardial vessels by means of CAG. One hundred twenty-seven (10.2%) patients had significant coronary atherosclerosis. Independent predictors of significant CAD include age, male sex, hypertension, angina, smoking, and hypercholesterolemia. Five hundred and fifty patients were designated as low risk according to our logistic regression and additive models. Of these patients, only 6 (1.1%) had single-vessel disease, and none had multivessel disease. Our models proved more efficient than established regression models. Our logistic regression model could estimate the risk of significant CAD in rheumatic patients undergoing mitral valve operations, while the additive simple score system could reliably identify the low-risk patients in whom routine preoperative angiography might be safely avoided.

Citation

Shu-Chun Li, Xue-Wen Liao, Li Li, Luo-Man Zhang, Zhi-Yun Xu. Prediction of significant coronary artery disease in patients undergoing operations for rheumatic mitral valve disease. European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery. 2012 Jan;41(1):82-6

Mesh Tags

PMID: 21641813

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