Chong CF, Li YC, Wang TL, Chang H |
------>authors3_c= ------>paper_class1=1 ------>Impact_Factor=2.612 ------>paper_class3=2 ------>paper_class2=1 ------>vol= ------>confirm_bywho=jack ------>insert_bywho=jack ------>Jurnal_Rank=5.6 ------>authors4_c= ------>comm_author=1 ------>patent_EDate=None ------>authors5_c= ------>publish_day=1 ------>paper_class2Letter=None ------>page2=4 ------>medlineContent= ------>unit=E0700 ------>insert_date=20031120 ------>iam=2 ------>update_date= ------>author=??? ------>change_event=4 ------>ISSN= ------>authors_c= ------>score=500 ------>journal_name=Journal of the American Medical Informatics Association, Supplement ------>paper_name=Stratification of Adverse Outcomes by Preoperative Risk Factors in Coronary Artery Bypass Graft Patients: An Artificial Neural Network Prediction Model ------>confirm_date=20060505 ------>tch_id=084004 ------>pmid=14728154 ------>page1=160 ------>fullAbstract=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. ------>tmu_sno=None ------>sno=8039 ------>authors2= ------>authors3= ------>authors4= ------>authors5= ------>authors6= ------>authors6_c= ------>authors=Chong CF, Li YC, Wang TL, Chang H ------>delete_flag=0 ------>SCI_JNo=None ------>authors2_c= ------>publish_area=0 ------>updateTitle=Stratification of adverse outcomes by preoperative risk factors in coronary artery bypass graft patients: an artificial neural network prediction model. ------>language=2 ------>check_flag= ------>submit_date= ------>country=None ------>no= ------>patent_SDate=None ------>update_bywho= ------>publish_year=2003 ------>submit_flag= ------>publish_month=1 |