Recording of Alcohol Use Disorder in Electronic Health Records: Developing a Recommended Codelist for Research.

IF 3.4 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Clinical Epidemiology Pub Date : 2024-10-04 eCollection Date: 2024-01-01 DOI:10.2147/CLEP.S477778
Sarah Cook, David Osborn, Arti Maini, Ravi Parekh, Shamini Gnani, Thomas Beaney, Ana Luisa Neves, Sonia Saxena, Jennifer K Quint
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引用次数: 0

Abstract

Purpose: Electronic health records (EHR) are valuable resources for health research; however, their use is challenging. A validated alcohol use disorder (AUD) codelist for UK primary care is needed to improve population-based research in this patient group. We aimed to develop an AUD codelist for use in the Clinical Practice Research Datalink (CPRD) Aurum database, a UK EHR primary-care database.

Methods: The CPRD code browser was searched using keywords related to alcohol use using a previously developed search strategy. The resulting codes were categorised as AUD if they were: a) diagnostic of AUD, b) indicated alcohol withdrawal, or c) indicated chronic alcohol-related harm (physical or mental). Codes related to alcohol use but not used to define AUD were also classified into relevant categories (alcohol status, acute harm, and alcohol screening). All codes were categorised independently by at least two reviewers (one person reviewed all codes and five reviewers (all practising GPs) each reviewed a subset of codes (100-200 codes each). Disagreements in categorisation were discussed by at least three coders and a consensus was reached. The reliability of categorisation was assessed using kappa statistics.

Results: In total, 556 potential codes related to alcohol use were identified. The Kappa for reliability between coders was moderate for both AUD (0.72) and across all categories (0.62), with substantial variability between coders (AUD: 0.33-0.97; all categories 0.36-0.74). In the final codelist, 138 codes were included as indicating AUD: 38 codes identified which indicated diagnosis of AUD, 14 indicating withdrawal plus 85 codes indicating chronic alcohol-related harm (41 physical health and 44 mental health).

Conclusion: Many codes are used in primary care to record alcohol use and associated harms, and there is substantial variability in how clinicians categorise them. While future work formally validating the codelist against gold standard clinical reviews and qualitative work with General Practitioners is needed for a deeper understanding of coding processes, we have documented here the process used for the development of an AUD codelist within primary care which can be used as a reference for future research.

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在电子健康记录中记录酒精使用障碍:为研究制定推荐目录。
目的:电子健康记录 (EHR) 是健康研究的宝贵资源,但其使用却具有挑战性。英国基层医疗机构需要一个经过验证的酒精使用障碍(AUD)代码表,以改善对这一患者群体的人群研究。我们的目标是为英国电子病历初级保健数据库临床实践研究数据链(CPRD)Aurum 数据库开发一个酒精使用障碍代码表:方法:利用之前开发的搜索策略,使用与酒精使用相关的关键词搜索 CPRD 代码浏览器。搜索出的代码在以下情况下被归类为 AUD:a) 可诊断为 AUD;b) 表明酒精戒断;或 c) 表明与酒精相关的慢性伤害(身体或精神伤害)。与酒精使用相关但未用于定义 AUD 的代码也被归入相关类别(酒精状态、急性危害和酒精筛查)。所有代码均由至少两名审稿人独立分类(一人审阅所有代码,五名审稿人(均为执业全科医生)每人审阅一组代码(每组 100-200 个代码))。对于分类中出现的分歧,至少由三名编码员进行讨论并达成共识。使用卡帕统计法评估分类的可靠性:结果:总共确定了 556 个与饮酒有关的潜在代码。对于 AUD(0.72)和所有类别(0.62),编码者之间的 Kappa 可信度为中等,编码者之间存在很大差异(AUD:0.33-0.97;所有类别 0.36-0.74)。在最终的代码表中,有 138 个代码表示 AUD:其中 38 个代码表示 AUD 诊断,14 个代码表示戒酒,另外 85 个代码表示与酒精相关的慢性损害(41 个表示身体健康,44 个表示心理健康):结论:初级保健中使用了许多代码来记录酒精使用和相关危害,临床医生对这些代码的分类存在很大差异。为了更深入地了解编码过程,我们需要在今后的工作中根据金标准临床回顾和全科医生的定性工作对编码表进行正式验证,但我们在此记录了在初级医疗中制定 AUD 编码表的过程,可作为今后研究的参考。
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来源期刊
Clinical Epidemiology
Clinical Epidemiology Medicine-Epidemiology
CiteScore
6.30
自引率
5.10%
发文量
169
审稿时长
16 weeks
期刊介绍: Clinical Epidemiology is an international, peer reviewed, open access journal. Clinical Epidemiology focuses on the application of epidemiological principles and questions relating to patients and clinical care in terms of prevention, diagnosis, prognosis, and treatment. Clinical Epidemiology welcomes papers covering these topics in form of original research and systematic reviews. Clinical Epidemiology has a special interest in international electronic medical patient records and other routine health care data, especially as applied to safety of medical interventions, clinical utility of diagnostic procedures, understanding short- and long-term clinical course of diseases, clinical epidemiological and biostatistical methods, and systematic reviews. When considering submission of a paper utilizing publicly-available data, authors should ensure that such studies add significantly to the body of knowledge and that they use appropriate validated methods for identifying health outcomes. The journal has launched special series describing existing data sources for clinical epidemiology, international health care systems and validation studies of algorithms based on databases and registries.
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