A simple and powerful risk-adjustment tool for 30-day mortality among inpatients

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)123-128
Number of pages6
JournalQuality management in health care
Volume25
Issue number3
DOIs
StatePublished - Jan 1 2016

Keywords

  • Comorbidity
  • Hospital medicine
  • Inpatients
  • Mortality
  • Predictive model
  • Risk adjustment

ASJC Scopus subject areas

  • Leadership and Management
  • Health(social science)
  • Health Policy
  • Care Planning

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