Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
J Am Med Inform Assoc. 2022 Jun 14;29(7):1225-1232. doi: 10.1093/jamia/ocac043.
Electric health record (EHR) discontinuity, that is, receiving care outside of a given EHR system, can lead to substantial information bias. We aimed to determine whether a previously described EHR-continuity prediction model can reduce the misclassification of 4 commonly used risk scores in pharmacoepidemiology.
The study cohort consists of patients aged ≥ 65 years identified in 2 US EHR systems linked with Medicare claims data from 2007 to 2017. We calculated 4 risk scores, CHAD2DS2-VASc, HAS-BLED, combined comorbidity score (CCS), claims-based frailty index (CFI) based on information recorded in the 365 days before cohort entry, and assessed their misclassification by comparing score values based on EHR data alone versus the linked EHR-claims data. CHAD2DS2-VASc and HAS-BLED were assessed in atrial fibrillation (AF) patients, whereas CCS and CFI were assessed in the general population.
Our study cohort included 204 014 patients (26 537 with nonvalvular AF) in system 1 and 115 726 patients (15 529 with nonvalvular AF) in system 2. Comparing the low versus high predicted EHR continuity in system 1, the proportion of patients with misclassification of ≥2 categories improved from 55% to 16% for CHAD2DS2-VASc, from 55% to 12% for HAS-BLED, from 37% to 16% for CCS, and from 10% to 2% for CFI. A similar pattern was found in system 2.
Using a previously described prediction model to identify patients with high EHR continuity may significantly reduce misclassification for the commonly used risk scores in EHR-based comparative studies.
电子健康记录（EHR）不连续，即在给定的 EHR 系统之外接受治疗，可能导致大量信息偏倚。我们旨在确定之前描述的 EHR 连续性预测模型是否可以减少 4 种常用于药物流行病学的风险评分的错误分类。
研究队列由 2 个美国 EHR 系统中年龄≥65 岁的患者组成，这些患者与 2007 年至 2017 年期间的医疗保险索赔数据相关联。我们计算了 4 个风险评分，即 CHAD2DS2-VASc、HAS-BLED、综合合并症评分（CCS）和基于索赔的脆弱指数（CFI），这些评分基于队列入组前 365 天记录的信息，并通过比较仅基于 EHR 数据的评分值与关联的 EHR-索赔数据的评分值，评估了这些评分的错误分类。CHAD2DS2-VASc 和 HAS-BLED 用于评估房颤（AF）患者，而 CCS 和 CFI 用于评估一般人群。
我们的研究队列包括系统 1 中的 204 014 名患者（26 537 名非瓣膜性 AF 患者）和系统 2 中的 115 726 名患者（15 529 名非瓣膜性 AF 患者）。在系统 1 中，比较低与高预测 EHR 连续性，CHAD2DS2-VASc 的错误分类比例从 55%改善至 16%，HAS-BLED 从 55%改善至 12%，CCS 从 37%改善至 16%，CFI 从 10%改善至 2%。在系统 2 中也发现了类似的模式。
使用之前描述的预测模型来识别 EHR 连续性高的患者可能会显著减少基于 EHR 的比较研究中常用风险评分的错误分类。
