Managing and analysing a large health-care system database for predicting in-hospital mortality among acute myocardial infarction patients

Abstract

There is increasing interest in the identification of predictors of risk for in-hospital mortality due to acute myocardial infarction (AMI). This study identified significant predictors of in-hospital mortality among AMI patients using a patient level clinical database. The study population consisted of 4167 cases admitted between October 1999 and April 2001 with a principal diagnosis of AMI to 36 hospitals in three US states. Of the 182 available variables in the clinical data set, 30 variables were used as candidate predictors, and 19 showed significant univariate association with AMI in-hospital mortality. By applying multiple logistic regression and stepwise selection, a final prediction model for AMI in-hospital mortality was developed. Variables included in the final model were age, arrived from cardiac rehabilitation centre, cardiopulmonary resuscitation (CPR) on arrival, Killip class, AMI with co-morbid conditions, AMI with complications, percutaneous transluminal coronary angioplasty (PTCA) performed, beta-blockers given, angiotensin-converting enzyme (ACE) inhibitors given, Plavix given. A 10-variable in-hospital mortality prediction model for AMI patients, which includes both risk factors and beneficial treatment procedures, was developed. chi(2) goodness of fit test suggested a good fit for the model.

A Discriminant Analysis of the Factors Associated With the Career Plans of String Music Educators

“…Variables included in the final models for analysis were also considered clinically plausible predictors of string music educators' career decisions. This criterion for variable inclusion has been employed by other researchers using discriminant analyses (Bae, Zhang, Rivers, & Singh, 2007). Bivariate associations between each plausible variable and the career decisions of participants were then examined.…”