Evidence map›Paper›PMID 28801439›Full record

ArticleBMJ open2017

Validation of asthma recording in the Clinical Practice Research Datalink (CPRD).

Francis Nissen, Daniel R Morales, Hana Mullerova, Liam Smeeth, Ian J Douglas, Jennifer K Quint

Abstract readValidation Study
In one paragraph

Article in BMJ open, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

64 citing papers in PubMed.

  1. Article
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  3. Age-Specific Risk of Herpes Zoster in Adults Aged ≥18 Years With Comorbid Conditions-A Retrospective Cohort Study in the United States.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026
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  14. Adverse respiratory outcomes among patients with resolved asthma: matched retrospective cohort study.The British journal of general practice : the journal of the Royal College of General Practitioners · 2024
    Article
  15. Article
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4 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Francis NissenDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.ORCID http://orcid.org/0000-0002-8747-0982
Daniel R MoralesDivision of Population Health Sciences, University of Dundee, Dundee, UK.
Hana MullerovaRWE & Epidemiology, GSK R&D, Uxbridge, UK.
Liam SmeethDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
Ian J DouglasDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
Jennifer K QuintNational Heart and Lung Institute, Imperial College, London, UK.

Funding

Medical Research Council G0802403Wellcome Trust
6 · The paper itself

Abstract

objectivesThe 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).

methods684 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.

results684 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%)%).

conclusionsPeople 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.

Indexed as

AlgorithmsClinical CodingElectronic Health RecordsPrimary Health CareAdultAgedAnti-Asthmatic AgentsAsthmaDatabases, FactualFemaleHumansMaleMiddle AgedReproducibility of ResultsSpirometrySurveys and QuestionnairesAnti-Asthmatic Agentsasthmaelectronic health recordsepidemiologypositive predictive valuevalidation

Identifiers

PMID28801439
PMCPMC5724126

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.