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

Research output: Contribution to journalArticle

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 - 2016

Fingerprint

risk adjustment
Risk Adjustment
Inpatients
mortality
Mortality
predictive model
health care
Delivery of Health Care
Social Security
Validation Studies
physiology
comorbidity
social security
Teaching Hospitals
Calibration
research planning
Registries
Comorbidity
Research Design
discrimination

Keywords

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

ASJC Scopus subject areas

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

Cite this

A simple and powerful risk-adjustment tool for 30-day mortality among inpatients. / Tremblay, Douglas; Arnsten, Julia H.; Southern, William N.

In: Quality Management in Health Care, Vol. 25, No. 3, 2016, p. 123-128.

Research output: Contribution to journalArticle

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