G Mnatzaganian1, P Ryan, J E Hiller. 1. Dr George Mnatzaganian, Faculty of Health Sciences, Australian Catholic University, Room 8.70, Level 8, 250 Victoria Parade, East Melbourne, Victoria, Australia, E-mail: George.Mnatzaganian@acu.edu.au.
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.
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.
Entities:
Keywords:
Hospital morbidity data; age; co-morbidity adjustment method; length of stay; model discrimination; mortality; readmission
Authors: Erik Bülow; Ola Rolfson; Peter Cnudde; Cecilia Rogmark; Göran Garellick; Szilárd Nemes Journal: Acta Orthop Date: 2017-06-28 Impact factor: 3.717
Authors: George Mnatzaganian; George Braitberg; Janet E Hiller; Lisa Kuhn; Rose Chapman Journal: BMC Cardiovasc Disord Date: 2016-05-26 Impact factor: 2.298