Predicting risk-adjusted mortality for trauma patients: Logistic versus multilevel logistic models

Clark DE, Hannan EL, Wu C
J Amer Coll Surg, 211, 224-231

Background: Theoretical advantages of random-intercept multilevel (ML) logistic regression (LR) modeling over standard LR include separating variability due to patient-level and hospital-level predictors, “shrinkage” of estimates for lower-volume hospitals toward the overall mean, and fewer hospitals falsely identified as outliers.
Study Design: We used Nationwide Inpatient Sample (NIS) data from 2002-2004 to construct LR models of hospital mortality after admission with a principal ICD-9-CM injury diagnosis (ICD-9-CM 800-904, 910-929, 940-957, 959). After considering various predictors, we used patient-level indicator variables for age groups, sex, maximum AIS for the head region (3,4,5), maximum AIS for other body regions (3,4,5), and mechanisms (fall, gunshot, motor vehicle). Using standard LR and MLLR, we compared predictions based upon 2002, 2003, and 2004 data to actual mortality observed in the same hospitals in 2004, 2005, and 2006 NIS respectively.
Results: Patient-level fixed effects were similar for the two methods in all years, with mortality associated most strongly with AIS=5 head injury, other AIS=5 injury, or higher age groups. ML models identified fewer hospitals as outliers. Differences between actual and predicted mortality were significantly smaller with MLLR models compared to standard LR models.
Conclusions: ML models may have advantages for the measurement and explanation of interhospital differences in trauma patient outcomes.

Number of levels
Model data structure
Response types
Multivariate response model?
Longitudinal data?
Substantive discipline
Paper submitted by
David Clark, Center for Outcomes Research and Evaluation, Maine Medical Center,
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