Evidence map›Paper›PMID 31385428›Full record

ArticlePharmacoepidemiology and drug safety2019

Chronic obstructive pulmonary disease exacerbation episodes derived from electronic health record data validated using clinical trial data.

Matthew Sperrin, David J Webb, Pinal Patel, Kourtney J Davis, Susan Collier, Alexander Pate, David A Leather, Jeanne M Pimenta

Open access · hybridAbstract readValidation Study
In one paragraph

Article in Pharmacoepidemiology and drug safety, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 22% of its field
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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Observational
  4. Article
  5. Article
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  10. Observational
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

8 authors at 3 institutions in 3 countries.

Matthew SperrinSchool of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
David J WebbReal-World Data and Analytics, GlaxoSmithKline plc., Harlow, UK.
Pinal PatelClinical Statistics (Respiratory), GlaxoSmithKline plc., Uxbridge, UK.
Kourtney J DavisReal-World Data and Analytics, GlaxoSmithKline plc., Collegeville, PA, USA.
Susan CollierRespiratory Therapy Area Unit, GlaxoSmithKline plc., Brentford, UK.
Alexander PateSchool of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
David A LeatherRespiratory Therapy Area Unit, GlaxoSmithKline plc., Brentford, UK.
Jeanne M PimentaEpidemiology, Global Medical, GlaxoSmithKline plc., Uxbridge, UK.ORCID 0000-0003-3482-7770
GlaxoSmithKline (United Kingdom) · GBManchester Academic Health Science Centre · GBGlaxoSmithKline (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo validate an algorithm for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) episodes derived in an electronic health record (EHR) database, against AECOPD episodes collected in a randomized clinical trial using an electronic case report form (eCRF).

methodsWe analyzed two data sources from the Salford Lung Study in COPD: trial eCRF and the Salford Integrated Record, a linked primary-secondary routine care EHR database of all patients in Salford. For trial participants, AECOPD episodes reported in eCRF were compared with algorithmically derived moderate/severe AECOPD episodes identified in EHR. Episode characteristics (frequency, duration), sensitivity, and positive predictive value (PPV) were calculated. A match between eCRF and EHR episodes was defined as at least 1-day overlap.

resultsIn the primary effectiveness analysis population (n = 2269), 3791 EHR episodes (mean [SD] length: 15.1 [3.59] days; range: 14-54) and 4403 moderate/severe AECOPD eCRF episodes (mean length: 13.8 [16.20] days; range: 1-372) were identified. eCRF episodes exceeding 28 days were usually broken up into shorter episodes in the EHR. Sensitivity was 63.6% and PPV 71.1%, where concordance was defined as at least 1-day overlap.

conclusionsThe EHR algorithm performance was acceptable, indicating that EHR-derived AECOPD episodes may provide an efficient, valid method of data collection. Comparing EHR-derived AECOPD episodes with those collected by eCRF resulted in slightly fewer episodes, and eCRF episodes of extreme lengths were poorly captured in EHR. Analysis of routinely collected EHR data may be reasonable when relative, rather than absolute, rates of AECOPD are relevant for stakeholders' decision making.

Indexed as

Symptom Flare UpAgedAlgorithmsClinical Trials, Phase III as TopicDatabases, FactualData CollectionElectronic Health RecordsEnglandFemaleHumansMaleMiddle AgedPatient AdmissionPharmacoepidemiologyPulmonary Disease, Chronic ObstructiveRandomized Controlled Trials as Topicalgorithmschronic obstructiveelectronic health recordspharmacoepidemiologypulmonary diseasevalidation

Identifiers

PMID31385428
PMCPMC7028141
OpenAlexW2966139875

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.