Browsing by Subject "prediction models"
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Item Predictors of Complicated Staphylococcus Aureus Bacteremia: A Retrospective Validation Study(2008-04-01) Krishnamurthy, Pramod; Fischbach, Lori; Cardarelli, Roberto; Coggin, Claudia S.Krishnamurthy, P., Predictors of Complicated Staphylococcus aureus Bacteremia (SAB): A Retrospective Validation Study. Master of Public Health (Epidemiology), April 2008, 57 pp, 9 tables, 1 illustration, bibliography, 39 titles. SAB often has a complicated clinical course and it is important to identify those at risk for complications to guide management. We conducted a validation study of a clinical prediction tool that uses a scoring system to predict the likelihood of developing complicated SAB. Chapter I is a review of background literature and rationale for our study. Chapter II has sections describing the study design, methods, eligibility criteria, statistical analysis and a summary of the results. We observed significantly higher complications among patients with SAB in our validation study. The prediction tool is not a valid predictor of complicated SAB and we recommend better prediction models to accurately predict complications of SAB.Item Predictors of In-Hospital Mortality Among Acute Myocardial Infarction Patients in a Large Health Care System(2001-07-01) Zhang, Huiling; Karan Singh; Antonio Rene; Sally BlakleyZhang, Huiling. Predictors of In-hospital Mortality Among Acute Myocardial Infarction Patients in a Large Health Care System. Master of Public Health, July 2000, 29 pp., 4 tables, 29 references. Background---There is increasing interest in the identification of risk predictors for in-hospital mortality due to acute myocardial infarction (AMI). To date, there has been no AMI in-hospital mortality prediction models developed using clinical database. Methods and Results---The study population consists of 4,167 AMI cases admitted to 36 hospitals in 3 states. Thirty variables were selected as candidate predictors, and 19 showed significant bivariate association with AMI in-hospital mortality. By applying multiple logistic regression and stepwise selection, 10 variables were selected for inclusion in the final prediction model: age, arrive from cardiac rehabilitation center, CPR on arrival, Killip class, AMI with comorbidities, AMI with complications, PCTA performed, beta-blockers given, ACE inhibitors given, Plavix given. Conclusion---A ten-variable in-hospital mortality prediction model for AMI patients, which includes both risk factors and beneficial treatment procedures, was developed. Chi-square goodness of fit test suggested a very good fit for the model.