Stratification of adverse outcomes by preoperative risk factors in coronary artery bypass graft patients: an artificial neural network prediction model.

Chee Fah Chong, Yu Chuan Li, Tzong Luen Wang, Hang Chang

研究成果: 雜誌貢獻文章

11 引文 (Scopus)

摘要

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.

原文英語
頁(從 - 到)160-164
頁數5
期刊AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
出版狀態已發佈 - 2003

指紋

Neural Networks (Computer)
Coronary Artery Bypass
Logistic Models
Transplants
Area Under Curve
Coma
Heart Arrest
Artificial Respiration
ROC Curve
Renal Insufficiency
Stroke
Databases
Population

引用此文

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AU - Chang, Hang

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