Prediction of 30-day cardiac-related-emergency-readmissions using simple administrative hospital data

Abstract 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 ef...

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Published in:	International journal of cardiology Vol. 164; no. 2; pp. 193 - 200
Main Authors:	Wallmann, Reinhard, Llorca, Javier, Gómez-Acebo, Inés, Ortega, Álvaro Castellanos, Roldan, Fernando Rojo, Dierssen-Sotos, Trinidad
Format:	Journal Article
Language:	English
Published:	Shannon Elsevier Ireland Ltd 05-04-2013 Elsevier
Subjects:	Adolescent Adult Aged Aged, 80 and over Biological and medical sciences Cardiac hospitalizations Cardiology. Vascular system Cardiovascular Cardiovascular Diseases - diagnosis Cardiovascular Diseases - epidemiology Cardiovascular Diseases - therapy Child Child, Preschool Cohort Cohort Studies Databases, Factual - trends Female Hospital Administration - trends Hospitalization - trends Humans Infant Infant, Newborn Male Medical sciences Middle Aged Patient Readmission - trends Predictive Value of Tests Readmissions Risk model Screening Time Factors Young Adult Cardiac hospitalizations Risk model Screening Cohort Readmissions Heart Prediction Administrative data Risk Hospitalization Medical screening Cohort study Risk factor Models Circulatory system Hospital Emergency Cardiology Predictive factor Day Public health
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Summary:	Abstract 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 35 531 hospital-admissions. The model included 11 predictors: number of previous emergency admission in 180 days, 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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0167-5273 1874-1754
DOI:	10.1016/j.ijcard.2011.06.119