Validation of asthma recording in the Clinical Practice Research Datalink (CPRD)
Validation of asthma recording in the Clinical Practice Research Datalink (CPRD)
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DOI：
10.1136/bmjopen-2017-017474
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发表时间：
2017-08-01
期刊：
影响因子：
2.9
通讯作者：
Quint, Jennifer K.
中科院分区：
文献类型：
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作者：
Nissen, Francis;Morales, Daniel R.;Quint, Jennifer K.
目标 在初级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。本研究的目的是使用临床代码和处方数据确定不同算法的阳性预测值 (PPV)，以识别英国临床实践研究数据链 (CPRD) 中的哮喘患者。 方法 根据八种预定义的潜在哮喘识别算法之一，选择 2013 年 12 月 1 日至 2015 年 11 月 30 日期间在全科医生 (GP) 诊所注册的 684 名参与 CPRD 的参与者。我们向全科医生发送了一份调查问卷，以确认哮喘状况并提供支持哮喘诊断的额外信息。两名研究医生独立审查和裁定问卷和附加信息，以形成哮喘诊断的金标准。计算每种算法的 PPV。结果 共发出 684 份问卷，其中回收 494 份（72%），完成并分析 475 份（69%）。所有五种算法（包括指示哮喘的特定读取代码或伴随其他条件的非特定读取代码）均表现良好。仅使用特定哮喘代码进行哮喘诊断的 PPV 为 86.4%（95% CI 77.4% 至 95.4%）。有关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。使用非特定哮喘代码、可逆性测试信息和呼吸药物使用信息的算法得分最高（PPV 90.7%、95% CI（82.8% 至 98.7%），但可识别人群要低得多。基于哮喘症状代码的算法的 PPV 较低（43.1% 至 57.8%）。结论 使用特定读取代码，可以从英国初级保健记录中准确识别哮喘患者。在算法中包含肺活量测定法或哮喘药物并没有明显提高准确性。 道德与传播 这项研究的方案得到了 MHRA 数据库研究独立科学咨询委员会 (ISAC) 的批准（方案编号 15_257），并且批准的方案在同行评审期间提供给期刊和审稿人。卫生研究局研究伦理委员会（东米德兰-德比，REC 参考号 05/MRE04/87）已授予使用 CPRD 观察性研究的通用伦理批准，并获得 ISAC 的批准。结果将提交出版，并将通过研究会议和同行评审期刊传播。
Objectives 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).Methods 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.Results 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%)%).Conclusions 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.Ethics and dissemination 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.