TY - JOUR
T1 - A simple and powerful risk-adjustment tool for 30-day mortality among inpatients
AU - Tremblay, Douglas
AU - Arnsten, Julia H.
AU - Southern, William N.
N1 - Publisher Copyright:
© Copyright 2016 Wolters Kluwer Health, Inc. All rights reserved.
PY - 2016
Y1 - 2016
N2 - Background: Risk adjustment formortality is increasingly important in an era when hospitals and health care systems are being compared with respect to health outcomes and quality. A powerful predictive model has been developed to risk-adjust for 30-day mortality among inpatients, but it is complex and not widely used. Objective: To develop and validate a simpler model, with predictive power similar to more complex models. Research Design: This was a retrospective split-validation study. In a derivation cohort, a predictivemodel for 30-daymortality was developed using logistic regression with the Charlson comorbidity score, Laboratory-Based Acute Physiology Score, and age as the predictor variables. In the validation cohort, the performance and calibration of the model to predict 30-day mortality was examined. Subjects: All admissions to themedical service of 2 urban university-based teaching hospitals located in Bronx, New York, between July 1, 2002, and April 30, 2008. Measures: All-cause mortality was taken from the social security death registry. Predictor variables were constructed from demographic characteristics, laboratory and billing data extracted from a clinical data repository. Results: The study sample included 147 991 admissions and overall 30-day mortality was 5.4%. The model had excellent discrimination, with a c-statistics of 0.8585 in the derivation cohort and 0.8484 in the validation cohort. The model accurately predicts 30-day mortality in all risk deciles. Conclusions: This simple and powerful predictive model can be used by hospitals and health care systems as a risk-adjustment tool for quality and research purposes.
AB - Background: Risk adjustment formortality is increasingly important in an era when hospitals and health care systems are being compared with respect to health outcomes and quality. A powerful predictive model has been developed to risk-adjust for 30-day mortality among inpatients, but it is complex and not widely used. Objective: To develop and validate a simpler model, with predictive power similar to more complex models. Research Design: This was a retrospective split-validation study. In a derivation cohort, a predictivemodel for 30-daymortality was developed using logistic regression with the Charlson comorbidity score, Laboratory-Based Acute Physiology Score, and age as the predictor variables. In the validation cohort, the performance and calibration of the model to predict 30-day mortality was examined. Subjects: All admissions to themedical service of 2 urban university-based teaching hospitals located in Bronx, New York, between July 1, 2002, and April 30, 2008. Measures: All-cause mortality was taken from the social security death registry. Predictor variables were constructed from demographic characteristics, laboratory and billing data extracted from a clinical data repository. Results: The study sample included 147 991 admissions and overall 30-day mortality was 5.4%. The model had excellent discrimination, with a c-statistics of 0.8585 in the derivation cohort and 0.8484 in the validation cohort. The model accurately predicts 30-day mortality in all risk deciles. Conclusions: This simple and powerful predictive model can be used by hospitals and health care systems as a risk-adjustment tool for quality and research purposes.
KW - Comorbidity
KW - Hospital medicine
KW - Inpatients
KW - Mortality
KW - Predictive model
KW - Risk adjustment
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U2 - 10.1097/QMH.0000000000000096
DO - 10.1097/QMH.0000000000000096
M3 - Article
C2 - 27367212
AN - SCOPUS:84978975580
SN - 1063-8628
VL - 25
SP - 123
EP - 128
JO - Quality management in health care
JF - Quality management in health care
IS - 3
ER -