应用人工神经网络对心脏外科手术危险因素的识别和死亡率的预测

Objective: The artificial neural network model is a nonlinear technology useful for complex pattern recognition problems. This study aimed to develop a method to select risk variables and predict mortality after cardiac surgery by using artificial neural networks. Methods: Prospectively collected data from 18,362 patients undergoing cardiac surgery at 128 European institutions in 1995(the European System for Cardiac Operative Risk Evaluation database) were used. Models to predict the operative mortality were constructed using artificial neural networks. For calibration a sixfold cross-validation technique was used, and for testing a fourfold cross-testing was performed. Risk variables were ranked and minimized in number by calibrated artificial neural networks. Mortality prediction with 95%confidence limits for each patient was obtained by the bootstrap technique. The area under the receiver operating characteristics curve was used as a quantitative measure of the ability to distinguish between survivors and nonsurvivors. Subgroup analysis of surgical operation categories was performed. The results were compared with those from logistic European System for Cardiac Operative Risk Evaluation analysis. Results: The operative mortality was 4.9%. Artificial neural networks selected 34 of the total 72 risk variables as relevant for mortality prediction. The receiver operating characteristics area for artificial neural networks(0.81) was larger than the logistic European System for Cardiac Operative Risk Evaluation model(0.79; P=.0001). For different surgical operation categories, there were no differences in the discriminatory power for the artificial neural networks(P=.15) but significant differences were found for the logistic European System for Cardiac Operative Risk Evaluation(P=.0072). Conclusions: Risk factors in a ranked order contributing to the mortality prediction were identified. A minimal set of risk variables achieving a superior mortality prediction was defined. The artificial neural network model is applicable independent of the cardiac surgical procedure.

心脏直视手术19种术前风险分层模型的比较

Aims: To compare 19 risk score algorithms with regard to their validity to predict 30-day and 1-year mortality after cardiac surgery. Methods and results: Risk factors for patients undergoing heart surgery between 1996 and 2001 at a single centre were prospectively collected. Receiver operating characteristics(ROC) curves were used to describe the performance and accuracy. Survival at 1 year and cause of death were obtained in all cases. The study included 6222 cardiac surgical procedures. Actual mortality was 2.9% at 30 days and 6.1% at 1 year. Discriminatory power for 30-day and 1-year mortality in cardiac surgery was highest for logistic(0.84 and 0.77) and additive(0.84 and 0.77) European System for Cardiac Operative Risk Evaluation(EuroSCORE) algorithms, followed by Cleveland Clinic(0.82 and 0.76) and Magovern(0.82 and 0.76) scoring systems. None of the other 15 risk algorithms had a significantly better discriminatory power than these four. In coronary artery bypass grafting(CABG)-only surgery, EuroSCORE followed by New York State(NYS) and Cleveland Clinic risk score showed the highest discriminatory power for 30-day and 1-year mortality. Conclusion: EuroSCORE, Cleveland Clinic, and Magovern risk algorithms showed superior performance and accuracy in open-heart surgery, and EuroSCORE, NYS, and Cleveland Clinic in CABG-only surgery. Although the models were originally designed to predict early mortality, the 1-year mortality prediction was also reasonably accurate.