Accurately predicting hospital mortality is necessary to measure and compare patient care. External validation of predictive models is required to truly prove their utility. This study assessed the Kaiser Permanente inpatient risk adjustment methodology for hospital mortality in a patient population distinct from that used for its derivation.
Retrospective cohort study at two hospitals in Ottawa, Canada, involving all inpatients admitted between January 1998 and April 2002 (n=188,724). Statistical models for inpatient mortality were derived on a random half of the cohort and validated on the other half.
Inpatient mortality was 3.3%. The model using original parameter estimates had excellent discrimination (c-statistic 89.4, 95% confidence interval [CI] 0.891-0.898) but poor calibration. Using data-based parameter estimates, discrimination was excellent (c-statistic 0.915, 95% CI 0.912-0.918) and remained so when patient comorbidity was expressed in the model using the Elixhauser Index (0.901, 0.898-0.904) or the Charlson Index (0.894, 0.891-0.897). These models accurately predicted the risk of hospital death.
The Kaiser Permanente inpatient risk adjustment methodology is a valid model for predicting hospital mortality risk. It performed equally well regardless of methods used to summarize patient comorbidity.