ArticleBMC health services research2013
Predictive performance of comorbidity measures in administrative databases for diabetes cohorts.
Article in BMC health services research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 30 citations in OpenAlex.
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- Comparative Performance of Comorbidity Measures in Predicting Health Outcomes in Patients with Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2020Article
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- Modification of claims-based measures improves identification of comorbidities in non-elderly women undergoing mastectomy for breast cancer: a retrospective cohort study.BMC health services research · 2016Article
- Estimating the prevalence of comorbid conditions and their effect on health care costs in patients with diabetes mellitus in Switzerland.Diabetes, metabolic syndrome and obesity : targets and therapy · 2014Article
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Authors and funding
4 authors at 3 institutions in 2 countries.
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Abstract
backgroundThe performance of comorbidity measures for predicting mortality in chronic disease populations and using ICD-9 diagnosis codes in administrative health data has been investigated in several studies, but less is known about predictive performance with ICD-10 data and for other health outcomes. This study investigated predictive performance of five comorbidity measures for population-based diabetes cohorts in administrative data. The objectives were to evaluate performance for: (a) disease-specific and general health outcomes, (b) data based on the ICD-9 and ICD-10 diagnoses, and (c) different age groups.
methodsPerformance was investigated for heart attack, stroke, amputation, renal disease, hospitalization, and death in all-age and age-specific cohorts. Hospital records, physician billing claims, and prescription drug records from one Canadian province were used to identify diabetes cohorts and measure comorbidity. The data were analysed using multiple logistic regression models and summarized using measures of discrimination, accuracy, and fit.
resultsIn Cohort 1 (n = 29,058), for which only ICD-9 diagnoses were recorded in administrative data, the Elixhauser index showed good or excellent prediction for amputation, renal disease, and death and performed better than the Charlson index. Number of diagnoses was a good predictor of hospitalization. Similar results were obtained for Cohort 2 (n = 41,925), in which both ICD-9 and ICD-10 diagnoses were recorded in administrative data, although predictive performance was sometimes higher. For age-specific models of mortality, the Elixhauser index resulted in the largest improvement in predictive performance in all but the youngest age group.
conclusionsCohort age and the health outcome under investigation, but not the diagnosis coding system, may influence the predictive performance of comorbidity measure for studies about diabetes populations using administrative health data.
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