Department of Community Health Sciences, University of Manitoba,Winnipeg, MB, Canada.
BMC Health Serv Res. 2013 Aug 17;13:340. doi: 10.1186/1472-6963-13-340.
The 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.
Performance 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.
In 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.
Cohort 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.
在使用 ICD-9 诊断代码的情况下，已经有多项研究调查了在慢性病人群中预测死亡率的合并症指标的性能，但是对于使用 ICD-10 数据和其他健康结果的预测性能了解较少。本研究调查了五种合并症指标在基于人群的糖尿病队列的行政健康数据中的预测性能。目的是评估以下方面的性能：（a）疾病特异性和总体健康结果，（b）基于 ICD-9 和 ICD-10 诊断的数据，以及（c）不同年龄组。
使用加拿大一个省的住院记录、医生计费索赔和处方药记录，评估了所有年龄和年龄特定队列中的心梗、中风、截肢、肾脏疾病、住院和死亡等健康结果的表现。使用多变量逻辑回归模型进行数据分析，并使用判别、准确性和拟合度量来总结。
在仅在行政数据中记录 ICD-9 诊断的队列 1（n=29058）中，Elixhauser 指数对截肢、肾脏疾病和死亡的预测效果较好或优秀，且表现优于 Charlson 指数。诊断数量是住院的良好预测指标。在队列 2（n=41925）中，记录了 ICD-9 和 ICD-10 诊断，也得到了类似的结果，尽管预测性能有时更高。对于特定年龄组的死亡率模型，Elixhauser 指数在除了最年轻年龄组以外的所有年龄组中均导致预测性能的最大改善。
队列年龄和研究的健康结果，而不是诊断编码系统，可能会影响使用行政健康数据研究糖尿病人群的合并症指标的预测性能。
