Evidence map›Paper›PMID 42203235›Full record

SynthesisEuropean respiratory review : an official journal of the European Respiratory Society2026

Validating exacerbations of asthma in electronic health records: a systematic review.

Elizabeth Moore, Zakariah Gassasse, Ian Sinha, Daniel B Hawcutt, Jennifer K Quint

Abstract readSystematic Review
In one paragraph

Synthesis in European respiratory review : an official journal of the European Respiratory Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

5 authors.

Elizabeth MooreSchool of Public Health, Imperial College London, London, UK liz.moore@imperial.ac.uk.
Zakariah GassasseSchool of Public Health, Imperial College London, London, UK.ORCID https://orcid.org/0000-0003-3079-1563
Ian SinhaAlder Hey Children's Hospital, Liverpool, UK.ORCID https://orcid.org/0000-0002-7342-5523
Daniel B HawcuttInstitute of Life Course and Medical Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK.ORCID https://orcid.org/0000-0002-8120-6507
Jennifer K QuintSchool of Public Health, Imperial College London, London, UK.ORCID https://orcid.org/0000-0003-0149-4869

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrevious studies have shown that the algorithms and code lists used to define asthma exacerbations vary across different sources of data, if reported at all. Defining and validating asthma exacerbations in electronic health records (EHR) would help to improve future research on asthma using EHR by leading to more consistent and comparable evidence.

methodsWe systematically reviewed the literature to evaluate studies that define exacerbations of asthma in EHR and report which algorithms have the highest validity. An adapted version of the QUADAS-2 designed for this review was used to assess risk of bias.

resultsOf the studies yielded by the search, only five met the inclusion criteria. Eligible studies used algorithms that contained codes from versions or modifications of either the 9th or 10th revisions of the International Statistical Classification of Diseases and Related Health (ICD-9 or ICD-10), and validity scores varied. Using the ICD-9 code 493 within algorithms to detect asthma exacerbations, sensitivity scores varied from 44.8% to 91.28% and specificity was >85%. Using the ICD-9 code 493.xx as the principal and secondary diagnosis in claims data, validity measures were all >85%. Using the ICD-10 code J45, scores for sensitivity, specificity and negative predictive value were also all >85%.

conclusionsAlgorithms have been used to identify asthma exacerbations in EHR with varying degrees of validity. Algorithms including the ICD-9 code 493.xx or the ICD-10 code J45 to detect asthma exacerbations had high validity scores. However, there was a risk of bias in these studies and urgent work is needed using robust methods to validate definitions for future research using EHR.

Indexed as

AsthmaElectronic Health RecordsAlgorithmsDisease ProgressionHumansInternational Classification of DiseasesPredictive Value of TestsReproducibility of Results

Identifiers

PMID42203235
PMCPMC13213462

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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.