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
