Background

The use of claims data for identifying comorbid conditions in patients for research purposes has been widely explored. Traditional measures of comorbid adjustment included diagnostic data (e.g., ICD-9-CM or ICD-10-CM codes), with the Charlson and Elixhauser methodology being the two most common approaches. Prescription data has also been explored for use in comorbidity adjustment, however early methodologies were disappointing when compared to diagnostic measures.

Objective

The objective of this methodological review is to compare results from newer studies using prescription-based data with more traditional diagnostic measures.

Methods

A review of studies found on PubMed, Medline, Embase or CINAHL published between January 1990 and December 2020 using prescription data for comorbidity adjustment. A total of 50 studies using prescription drug measures for comorbidity adjustment were found.

Conclusions

Newer prescription-based measures show promise fitting models, as measured by predictive ability, for research, especially when the primary outcomes are utilization or drug expenditure rather than diagnostic measures. More traditional diagnostic-based measures still appear most appropriate if the primary outcome is mortality or inpatient readmissions.

中文翻译：

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使用处方药数据库进行合并症调整的叙述性审查：效果较差的补救措施还是改进模型拟合的处方？

背景

出于研究目的，使用索赔数据来识别患者的合并症已得到广泛探索。合并症调整的传统措施包括诊断数据（例如，ICD-9-CM 或 ICD-10-CM 代码），Charlson 和 Elixhauser 方法是两种最常见的方法。还探索了用于调整合并症的处方数据，但是与诊断措施相比，早期的方法令人失望。

客观的

本方法学审查的目的是将使用基于处方的数据的较新研究的结果与更传统的诊断措施进行比较。

方法

使用处方数据对 1990 年 1 月至 2020 年 12 月期间发表的关于 PubMed、Medline、Embase 或 CINAHL 的研究进行回顾，以调整合并症。共发现了 50 项使用处方药措施进行合并症调整的研究。

结论

较新的基于处方的措施显示出适用于研究的预测拟合模型，特别是当主要结果是利用率或药物支出而不是诊断措施时。如果主要结果是死亡率或住院患者再入院，更传统的基于诊断的措施似乎仍然最合适。