Validation of asthma recording in the Clinical Practice Research Datalink (CPRD). Issue 8 (11th August 2017)

• Record Type: Journal Article
• Authors: Nissen, Francis; Morales, Daniel R; Mullerova, Hana; Smeeth, Liam; Douglas, Ian J; Quint, Jennifer K
• Is Part Of: BMJ open. Volume 7:Issue 8(2017)
• Publication Date: 2017-08-11
• Subjects: asthma -- validation -- electronic health records -- positive predictive value -- epidemiology; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2017-017474
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : 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.