基于索赔数据的合并症调整模型的可预测性可以通过使用药物数据来提高。
The predictability of claim-data-based comorbidity-adjusted models could be improved by using medication data.
机构信息
Department of Health Policy and Management, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 110-799, Korea.
出版信息
BMC Med Inform Decis Mak. 2013 Nov 20;13:128. doi: 10.1186/1472-6947-13-128.
BACKGROUND
Recently, claim-data-based comorbidity-adjusted methods such as the Charlson index and the Elixhauser comorbidity measures have been widely used among researchers. At the same time, there have been an increasing number of attempts to improve the predictability of comorbidity-adjusted models. We tried to improve the predictability of models using the Charlson and Elixhauser indices by using medication data; specifically, we used medication data to estimate omitted comorbidities in the claim data.
METHODS
We selected twelve major diseases (other than malignancies) that caused large numbers of in-hospital mortalities during 2008 in hospitals with 700 or more beds in South Korea. Then, we constructed prediction models for in-hospital mortality using the Charlson index and Elixhauser comorbidity measures, respectively. Inferring missed comorbidities using medication data, we built enhanced Charlson and Elixhauser comorbidity-measures-based prediction models, which included comorbidities inferred from medication data. We then compared the c-statistics of each model.
RESULTS
247,712 admission cases were enrolled. 55 generic drugs were used to infer 8 out of 17 Charlson comorbidities, and 106 generic drugs were used to infer 14 out of 31 Elixhauser comorbidities. Before the inclusion of comorbidities inferred from medication data, the c-statistics of models using the Charlson index were 0.633-0.882 and those of the Elixhauser index were 0.699-0.917. After the inclusion of comorbidities inferred from medication data, 9 of 12 models using the Charlson index and all of the models using the Elixhauser comorbidity measures were improved in predictability but, the differences were relatively small.
CONCLUSION
Prediction models using Charlson index or Elixhauser comorbidity measures might be improved by including comorbidities inferred from medication data.
背景
最近，基于索赔数据的合并症调整方法（如 Charlson 指数和 Elixhauser 合并症度量）已被研究人员广泛使用。与此同时，人们越来越多地尝试提高合并症调整模型的预测能力。我们试图通过使用药物数据来改善 Charlson 和 Elixhauser 指数模型的预测能力；具体来说，我们使用药物数据来估计索赔数据中遗漏的合并症。
方法
我们选择了 2008 年在韩国拥有 700 张或更多床位的医院中导致大量住院死亡的 12 种主要疾病（恶性肿瘤除外）。然后，我们分别使用 Charlson 指数和 Elixhauser 合并症度量构建了住院死亡率预测模型。通过使用药物数据推断遗漏的合并症，我们构建了增强的 Charlson 和 Elixhauser 合并症度量预测模型，其中包括从药物数据推断出的合并症。然后，我们比较了每个模型的 c 统计量。
结果
共纳入 247712 例入院病例。使用 55 种通用药物推断了 Charlson 合并症中的 8 种，使用 106 种通用药物推断了 Elixhauser 合并症中的 14 种。在纳入药物数据推断出的合并症之前，使用 Charlson 指数的模型的 c 统计量为 0.633-0.882，使用 Elixhauser 指数的模型的 c 统计量为 0.699-0.917。纳入药物数据推断出的合并症后，使用 Charlson 指数的 12 个模型中的 9 个和使用 Elixhauser 合并症度量的所有模型的预测能力都得到了提高，但差异相对较小。
结论
通过纳入药物数据推断出的合并症，使用 Charlson 指数或 Elixhauser 合并症度量的预测模型可能会得到改善。
Zotero 插件