BACKGROUND: Control and reduction of cardiovascular-disease-related readmissions is clinically, logistically and politically challenging. Recent strategies focus on 30-day readmissions. A screening tool for the detection of potential cases is necessary to make further case management more efficient. METHODS: Cohort study. Hospital administrative data were analyzed in order to obtain information about cardiac-related hospitalizations from 2003 to 2009 at a Spanish academic tertiary care center. Predictor-variables of admissions that presented or did not present 30-day cardiac-related readmission were compared. A prediction model was constructed and tested on a validation sample. Model performance was assessed for all cardiac diseases and for 24 main-cardiac-disease-sets. RESULTS: The study sample was 35531 hospital-admissions. The model included 11 predictors: number of previous emergency admission in 180days, residence out of area, no procedure applied during hospitalization, major or minor therapeutic procedure applied during hospitalization, anemia, hypertensive disease, acute coronary syndrome, congestive heart failure, diabetes and renal disease. The performance indicators applied on all cardiac diseases were: C-statistic=0.75, Sensitivity=0.66, Specificity=0.70, Positive predictive value=0.10, Negative predictive value=0.98, Positive likelihood ratio=2.21 and Negative likelihood ratio=0.48. Diseases for discriminative prediction are: stenting, circulatory disorders, acute myocardial infarction and defibrillator and pacemaker implantation. CONCLUSIONS: This study provides a prediction model for 30-day cardiac-related diseases based on available administrative data ready to be integrated as a screening tool. It has reasonable validity and can be used to increase the efficiency of case management.
BACKGROUND: Control and reduction of cardiovascular-disease-related readmissions is clinically, logistically and politically challenging. Recent strategies focus on 30-day readmissions. A screening tool for the detection of potential cases is necessary to make further case management more efficient. METHODS: Cohort study. Hospital administrative data were analyzed in order to obtain information about cardiac-related hospitalizations from 2003 to 2009 at a Spanish academic tertiary care center. Predictor-variables of admissions that presented or did not present 30-day cardiac-related readmission were compared. A prediction model was constructed and tested on a validation sample. Model performance was assessed for all cardiac diseases and for 24 main-cardiac-disease-sets. RESULTS: The study sample was 35531 hospital-admissions. The model included 11 predictors: number of previous emergency admission in 180days, residence out of area, no procedure applied during hospitalization, major or minor therapeutic procedure applied during hospitalization, anemia, hypertensive disease, acute coronary syndrome, congestive heart failure, diabetes and renal disease. The performance indicators applied on all cardiac diseases were: C-statistic=0.75, Sensitivity=0.66, Specificity=0.70, Positive predictive value=0.10, Negative predictive value=0.98, Positive likelihood ratio=2.21 and Negative likelihood ratio=0.48. Diseases for discriminative prediction are: stenting, circulatory disorders, acute myocardial infarction and defibrillator and pacemaker implantation. CONCLUSIONS: This study provides a prediction model for 30-day cardiac-related diseases based on available administrative data ready to be integrated as a screening tool. It has reasonable validity and can be used to increase the efficiency of case management.
Authors: Claudia Fischer; Hester F Lingsma; Perla J Marang-van de Mheen; Dionne S Kringos; Niek S Klazinga; Ewout W Steyerberg Journal: PLoS One Date: 2014-11-07 Impact factor: 3.240
Authors: Hao Wang; Carol Johnson; Richard D Robinson; Vicki A Nejtek; Chet D Schrader; JoAnna Leuck; Johnbosco Umejiego; Allison Trop; Kathleen A Delaney; Nestor R Zenarosa Journal: BMC Health Serv Res Date: 2016-10-10 Impact factor: 2.655