| Chong CF |
------>authors3_c=None ------>paper_class1=2 ------>Impact_Factor=None ------>paper_class3=0 ------>paper_class2=0 ------>vol= ------>confirm_bywho=soulchin ------>insert_bywho=m002183 ------>Jurnal_Rank=None ------>authors4_c=None ------>comm_author= ------>patent_EDate=None ------>authors5_c=None ------>publish_day=None ------>paper_class2Letter=None ------>page2=164 ------>medlineContent= ------>unit=E0110 ------>insert_date=20040419 ------>iam=3 ------>update_date=None ------>author=??? ------>change_event=5 ------>ISSN=None ------>authors_c=None ------>score=462 ------>journal_name=Proc AMIA Symp ------>paper_name=Stratification of Major Outcomes by Preoperative Risk Factors in Coronary Artery Bypass Graft Patients: An Artificial Neural Network Prediction Model. ------>confirm_date=20040419 ------>tch_id=092079 ------>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=8481 ------>authors2=Li YC ------>authors3=Wang TL ------>authors4=Chang H ------>authors5= ------>authors6= ------>authors6_c=None ------>authors=Chong CF ------>delete_flag=0 ------>SCI_JNo=None ------>authors2_c=None ------>publish_area=None ------>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=None ------>submit_date=None ------>country=None ------>no= ------>patent_SDate=None ------>update_bywho=None ------>publish_year=2003 ------>submit_flag=None ------>publish_month=None |