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|Scopus||Web of Science®||Altmetric|
|Title:||Does co-morbidity provide significant improvement on age adjustment when predicting medical outcomes?|
|Citation:||Methods of Information in Medicine, 2014; 53(2):115-120|
|G. Mnatzaganian, P. Ryan, J. E. Hiller|
|Abstract:||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 morbidity 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-morbidities 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 assessed by measuring the difference in the Harrell's C estimates between pairs of models 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 discrimina - tion; age; co-morbidity adjustment method; length of stay; readmission; mortality|
|Rights:||© Schattauer 2014|
|Appears in Collections:||Public Health publications|
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