Annemarie L Woldhek1, Saskia Rijkenberg1, Rob J Bosman1, Peter H J van der Voort2. 1. Dept of intensive care, OLVG hospital, Amsterdam, The Netherlands. 2. Dept of intensive care, OLVG hospital, Amsterdam, The Netherlands; TIAS School for Business and Society, Tilburg University, Tilburg, The Netherlands. Electronic address: P.H.J.vanderVoort@olvg.nl.
Abstract
PURPOSE: Readmission rate is frequently proposed as a quality indicator because it is related to both patient outcome and organizational efficiency. Currently available studies are not clear about modifiable factors as tools to reduce readmission rate. MATERIAL AND METHODS: In a 14year retrospective cohort study of 19,750 ICU admissions we identified 1378 readmissions (7%). A multivariate logistic regression analysis for determinants of readmission within 24h, 48h, 72h and any time during hospital admission was performed with adjustment for patients' characteristics and initial admission severity scores. RESULTS: In all models with different time points, patients with older age, a medical and emergency surgery initial admission and patients with higher SOFA score have a higher risk of readmission. Immunodeficiency was a predictor only in the at any time model. Confirmed infection was predicted in all models except the 24h model. Last day noradrenaline treatment was predicted in the 24 and 48h model. Mechanical ventilation on admission independently protected for readmission, which can be explained by the large number of cardiac surgery patients. All multivariate models had a moderate performance with the highest AUC of 0.70. CONCLUSIONS: Readmission can be predicted with moderate precision and independent variables associated with readmission are age, severity of disease, type of admission, infection, immunodeficiency and last day noradrenaline use. The latter factor is the only one that can be modified and therefore readmission rate does not meet the criteria to be used as a useful quality indicator.
PURPOSE: Readmission rate is frequently proposed as a quality indicator because it is related to both patient outcome and organizational efficiency. Currently available studies are not clear about modifiable factors as tools to reduce readmission rate. MATERIAL AND METHODS: In a 14year retrospective cohort study of 19,750 ICU admissions we identified 1378 readmissions (7%). A multivariate logistic regression analysis for determinants of readmission within 24h, 48h, 72h and any time during hospital admission was performed with adjustment for patients' characteristics and initial admission severity scores. RESULTS: In all models with different time points, patients with older age, a medical and emergency surgery initial admission and patients with higher SOFA score have a higher risk of readmission. Immunodeficiency was a predictor only in the at any time model. Confirmed infection was predicted in all models except the 24h model. Last day noradrenaline treatment was predicted in the 24 and 48h model. Mechanical ventilation on admission independently protected for readmission, which can be explained by the large number of cardiac surgery patients. All multivariate models had a moderate performance with the highest AUC of 0.70. CONCLUSIONS: Readmission can be predicted with moderate precision and independent variables associated with readmission are age, severity of disease, type of admission, infection, immunodeficiency and last day noradrenaline use. The latter factor is the only one that can be modified and therefore readmission rate does not meet the criteria to be used as a useful quality indicator.