School of Public Health & National Heart and Lung Institute, Imperial College London, London, United Kingdom.
Epidemiology, Value Evidence and Outcomes, Global Medical R&D, GSK, London, United Kingdom.
PLoS One. 2023 Nov 1;18(11):e0292876. doi: 10.1371/journal.pone.0292876. eCollection 2023.
Validity of exposure and outcome measures in electronic medical records is vital to ensure robust, comparable study findings however, despite validation studies, definitions of variables used often differ. Using exacerbations of chronic obstructive pulmonary disease (COPD) as an example, we investigated the impact of potential misclassification of different definitions commonly used in publications on study findings.
A retrospective cohort study was performed. English primary care data from the Clinical Practice Research Datalink Aurum database with linked secondary care data were used to define a population of COPD patients ≥40 years old registered at a general practice. Index date was the date eligibility criteria were met and end of follow-up was 30/12/19, death or end of data collection. Exacerbations were defined using 6 algorithms based on definitions commonly used in the literature, including one validated definition. For each algorithm, the proportion of frequent exacerbators (≥2 exacerbations/year) and exacerbation rates were described. Cox proportional hazard regression was used to investigate each algorithm on the association between heart failure and risk of COPD exacerbation.
A total of 315,184 patients were included. Baseline proportion of frequent exacerbators varied from 2.7% to 15.3% depending on the algorithm. Rates of exacerbations over follow-up varied from 19.3 to 66.6 events/100 person-years. The adjusted hazard ratio for the association between heart failure and exacerbation varied from 1.45, 95% confidence intervals 1.42-1.49, to 1.01, 0.98-1.04.
The use of high validity definitions and standardisation of definitions in electronic medical records is crucial to generating high quality, robust evidence.
在电子病历中，暴露和结局测量的有效性对于确保研究结果的稳健性和可比性至关重要。然而，尽管已经进行了验证研究，但由于定义变量的方法不同，文献中使用的变量定义仍存在差异。本研究以慢性阻塞性肺疾病（COPD）加重为例，探讨了不同定义方法在文献中常见的潜在分类错误对研究结果的影响。
本研究为回顾性队列研究。研究使用来自 Clinical Practice Research Datalink Aurum 数据库的英国初级保健数据，以及与该数据库相关联的二级保健数据，定义了一个≥40 岁在普通诊所注册的 COPD 患者人群。索引日期为满足入选标准的日期，随访结束日期为 2019 年 12 月 30 日，死亡或数据收集结束。使用基于文献中常用定义的 6 种算法来定义加重事件，包括一种经过验证的定义。对于每种算法，描述了频繁加重者（≥2 次/年）的比例和加重事件率。使用 Cox 比例风险回归分析来研究每种算法与心力衰竭和 COPD 加重风险之间的关系。
共纳入 315184 名患者。根据算法的不同，基线频繁加重者的比例从 2.7%到 15.3%不等。随访期间的加重事件发生率从 19.3 到 66.6 次/100 人年不等。心力衰竭与加重事件之间的调整后危险比从 1.45（95%置信区间 1.42-1.49）到 1.01（0.98-1.04）不等。
在电子病历中使用高有效性定义和定义标准化对于生成高质量、稳健的证据至关重要。
