使用临床分类软件来解决ICD编码过渡在大型医疗保健数据库中通过高维倾向分数进行分析的临床分类软件
Lai San Hong1, Xabier Garcia-Albeniz2, David Friesen3
1Redsen Limited, Bournemouth, UK.
Pharmacoepidemiology and drug safety
|September 25, 2023
概括
使用临床分类软件 (CCS) 类别进行代码聚合,为分析大型医疗保健索赔数据库提供一致的结果. 这种方法有效地解决了在药物安全性研究中使用高维倾向分数 (hd-PS) 进行混调整时的编码异质性.
科学领域:
- 药物监督 药物监督 药物监督
- 医疗信息学 医疗信息学
- 流行病学 流行病学
背景情况:
- 医疗保健索赔数据对于现实世界的证据研究至关重要,但通常包含编码异质性.
- 编码变化,包括国际疾病分类 (ICD) 更新,可能会影响高维倾向评分 (hd-PS) 模型的准确性.
- 大数据集的手动协调通常是不可行的,需要自动化解决方案.
研究的目的:
- 用临床分类软件 (CCS) 评估代码聚合对大型索赔数据库分析的影响.
- 在药物安全研究中使用CCS类别用于与hd-PS进行混调整时评估结果的一致性.
- 确定CCS分类是否可以减轻大规模药物流行病学研究中的编码异质性所产生的问题.
主要方法:
- 利用IBM® MarketScan®研究数据库识别新使用的患者开始服用 levetiracetam 或比较抗药物.
- 采用临床分类软件 (CCS) 分类来协调不同系统和时间段的编码.
- 针对急性功能衰竭 (ARF) 的计算发病率比率 (IRR) 使用修改的Poisson回归与hd-PS调整,比较CCS类别和没有CCS类别的分析.
主要成果:
- 在ICD更新之前的时期 (2008-2015年),ARF的IRR与 (1.30) 和没有 (1.34) 的CCS类别相似.
- 包括ICD编码变更时期 (2008-2017年),ARF的IRR保持一致,显示1.37与CCS类别相比1.34没有.
- 分析表明,使用单一水平的CCS类别,在 levetiracetam 用户中产生一致的ARF发病率结果.
结论:
- 单级CCS类别有效地克服了大型索赔数据库中的编码差异.
- 通过CCS进行代码聚合提供了一致的结果,支持其在使用hd-PS.的药理流行病学研究中的使用.
- 这种方法提高了来自复杂的医疗保健索赔数据的现实证据的可靠性.
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