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Methods Inf Med 2014; 53(02): 115-120
DOI: 10.3414/ME13-01-0095
Original Articles
Schattauer GmbH

Does Co-morbidity Provide Significant Improvement on Age Adjustment when Predicting Medical Outcomes?[*]

G. Mnatzaganian
1   Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, Australia
2   Discipline of Public Health, School of Population Health, The University of Adelaide, Adelaide, South Australia, Australia
,
P. Ryan
2   Discipline of Public Health, School of Population Health, The University of Adelaide, Adelaide, South Australia, Australia
,
J. E. Hiller
1   Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, Australia
2   Discipline of Public Health, School of Population Health, The University of Adelaide, Adelaide, South Australia, Australia
› Author Affiliations
Further Information

Publication History

received: 23 August 2013

accepted: 09 January 2014

Publication Date:
20 January 2018 (online)

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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

* Supplementary material published on our web-site www.methods-online.com


 

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