Title
Stratification of adverse outcomes by preoperative risk factors in coronary artery bypass graft patients: an artificial neural network prediction model.
Abstract
We constructed and internally validated an artificial neural network (ANN) model for prediction of in-hospital major adverse outcomes (defined as death, cardiac arrest, coma, renal failure, cerebrovascular accident, reinfarction, or prolonged mechanical ventilation) in patients who received "on-pump" coronary artery bypass grafting (CABG) surgery. We retrospectively analyzed a 5-year CABG surgery database with a final study population of 563 patients. Predictive variables were limited to information available before the procedure, and outcome variables were represented only by events that occurred postoperatively. The ANN's ability to discriminate outcomes was assessed using receiver-operating characteristic (ROC) analysis and the results were compared with a multivariate logistic regression (LR) model and the QMMI risk score (RS) model. A major adverse outcome occurred in 12.3% of all patients and 18 predictive variables were identified by the ANN model. Pairwise comparison showed that the ANN model significantly outperformed the RS model (AUC = 0.886 vs.0.752, p = 0.043). However, the other two pairs, ANN vs. LR models (AUC = 0.886 vs. 0.807, p = 0.076) and LR vs. RS models (AUC = 0.807 vs. 0.752, p = 0.453) performed similarly well. ANNs tend to outperform regression models and might be a useful screening tool to stratify CABG candidates preoperatively into high-risk and low-risk groups.
Year
Venue
Keywords
2003
AMIA
risk factors,regression model,stratification,risk score,receiver operator characteristic,artificial neural network,prediction model,logistic regression,roc analysis,cerebrovascular accident
Field
DocType
ISSN
Framingham Risk Score,Internal medicine,Regression analysis,Cardiology,Stroke,Coma,Mechanical ventilation,Retrospective cohort study,Surgery,Logistic regression,Medicine,Cross-sectional study
Conference
1942-597X
Citations 
PageRank 
References 
2
0.49
3
Authors
4
Name
Order
Citations
PageRank
Chee-Fah Chong1111.47
Yu-Chuan Li220934.88
Tzong-Luen Wang330.90
Hang Chang460.99