Summary
Objective: Using three risk-adjustment methods we evaluated whether co-morbidity derived from
electronic hospital patient data provided significant improvement on age adjustment
when predicting major outcomes following an elective total joint replacement (TJR)
due to osteoarthritis.
Methods: Longitudinal data from 819 elderly men who had had a TJR were integrated
with hospital morbidity data (HMD) and mortality records. For each participant, any
mor bidity or health-related outcome was retrieved from the linked data in the period
1970 through to 2007 and this enabled us to better account for patient co-morbidities.
Co-mor bidities recorded in the HMD in all admissions preceding the index TJR admission
were used to construct three risk-adjustment methods, namely Charlson co-morbidity
index (CCI), Elixhauser’s adjustment method, and number of co-morbidities. Postoperative
outcomes evaluated included length of hospital stay, 90-day readmission, and 1-year
and 2-year mortality. These were modelled using Cox proportional hazards regression
as a function of age for the baseline models, and as a function of age and each of
the risk-adjustment methods. The difference in the statistical performance between
the models that included age alone and those that also included the co-morbidity adjustment
method was as sessed by measuring the difference in the Harrell’s C estimates between
pairs of mod els applied to the same patient data using Bootstrap analysis with 1000
replications.
Results: Number of co-morbidities did not provide any significant improvement in model discrimination
when added to baseline models observed in all outcomes. CCI significantly improved
model discrimination when predicting post-operative mortality but not when length
of stay or readmission was modelled. For every one point increase in CCI, postoperative
1- and 2-year mortality increased by 37% and 30%, respectively. Elixhauser’s method
outperformed the other two providing significant improvement on age adjustment in
all outcomes.
Conclusion: The predictive performance of co-morbidity derived from electronic hospital data
is outcome and risk-adjustment method specific.
Keywords
Hospital morbidity data - model discrimination - age - co-morbidity adjustment method
- length of stay - readmission - mortality
