Nissen Francis, Morales Daniel R, Mullerova Hana, Smeeth Liam, Douglas Ian J, Quint Jennifer K
Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
Division of Population Health Sciences, University of Dundee, Dundee, UK.
BMJ Open. 2017 Aug 11;7(8):e017474. doi: 10.1136/bmjopen-2017-017474.
The optimal method of identifying people with asthma from electronic health records in primary care is not known. The aim of this study is to determine the positive predictive value (PPV) of different algorithms using clinical codes and prescription data to identify people with asthma in the United Kingdom Clinical Practice Research Datalink (CPRD).
684 participants registered with a general practitioner (GP) practice contributing to CPRD between 1 December 2013 and 30 November 2015 were selected according to one of eight predefined potential asthma identification algorithms. A questionnaire was sent to the GPs to confirm asthma status and provide additional information to support an asthma diagnosis. Two study physicians independently reviewed and adjudicated the questionnaires and additional information to form a gold standard for asthma diagnosis. The PPV was calculated for each algorithm.
684 questionnaires were sent, of which 494 (72%) were returned and 475 (69%) were complete and analysed. All five algorithms including a specific Read code indicating asthma or non-specific Read code accompanied by additional conditions performed well. The PPV for asthma diagnosis using only a specific asthma code was 86.4% (95% CI 77.4% to 95.4%). Extra information on asthma medication prescription (PPV 83.3%), evidence of reversibility testing (PPV 86.0%) or a combination of all three selection criteria (PPV 86.4%) did not result in a higher PPV. The algorithm using non-specific asthma codes, information on reversibility testing and respiratory medication use scored highest (PPV 90.7%, 95% CI (82.8% to 98.7%), but had a much lower identifiable population. Algorithms based on asthma symptom codes had low PPVs (43.1% to 57.8%)%).
People with asthma can be accurately identified from UK primary care records using specific Read codes. The inclusion of spirometry or asthma medications in the algorithm did not clearly improve accuracy.
The protocol for this research was approved by the Independent Scientific Advisory Committee (ISAC) for MHRA Database Research (protocol number15_257) and the approved protocol was made available to the journal and reviewers during peer review. Generic ethical approval for observational research using the CPRD with approval from ISAC has been granted by a Health Research Authority Research Ethics Committee (East Midlands-Derby, REC reference number 05/MRE04/87).The results will be submitted for publication and will be disseminated through research conferences and peer-reviewed journals.
在初级医疗保健中，从电子健康记录中识别哮喘患者的最佳方法尚不清楚。本研究旨在确定使用临床编码和处方数据的不同算法在英国临床实践研究数据链（CPRD）中识别哮喘患者的阳性预测值（PPV）。
根据八种预定义的潜在哮喘识别算法之一，选取了2013年12月1日至2015年11月30日期间向CPRD提供数据的684名在全科医生（GP）诊所注册的参与者。向全科医生发送问卷以确认哮喘状态并提供支持哮喘诊断的额外信息。两名研究医生独立审查并评判问卷及额外信息，以形成哮喘诊断的金标准。计算每种算法的PPV。
共发送684份问卷，其中494份（72%）被退回，475份（69%）完整并进行了分析。所有五种算法，包括一个表明哮喘的特定Read编码或伴有其他条件的非特定Read编码，表现良好。仅使用特定哮喘编码进行哮喘诊断的PPV为86.4%（95%可信区间77.4%至95.4%）。哮喘药物处方的额外信息（PPV 83.3%）、可逆性测试证据（PPV 86.0%）或所有三个选择标准的组合（PPV 86.4%）并未导致更高的PPV。使用非特定哮喘编码、可逆性测试信息和呼吸药物使用情况的算法得分最高（PPV 90.7%，95%可信区间（82.8%至98.7%）），但可识别的人群要少得多。基于哮喘症状编码的算法PPV较低（43.1%至57.8%）。
使用特定的Read编码可从英国初级医疗保健记录中准确识别哮喘患者。算法中纳入肺功能测定或哮喘药物并未明显提高准确性。
本研究方案已获得药品和医疗产品监管局数据库研究独立科学咨询委员会（ISAC）的批准（方案编号15_257），且在同行评审期间已将批准的方案提供给该期刊和审稿人。健康研究管理局研究伦理委员会（东米德兰兹 - 德比，REC参考编号05/MRE04/87）已批准使用经ISAC批准的CPRD进行观察性研究的通用伦理许可。研究结果将提交发表，并将通过研究会议和同行评审期刊进行传播。
