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

Autor: Kourtney J. Davis, Alexander Pate, David J. Webb, Jeanne M. Pimenta, Pinal Patel, Susan Collier, Matthew Sperrin, David Leather
Rok vydání: 2019
Předmět:
Male
medicine.medical_specialty
pharmacoepidemiology
Databases
Factual
Exacerbation
Epidemiology
Concordance
Population
algorithms
Sensitivity and Specificity
Severity of Illness Index
030226 pharmacology & pharmacy
law.invention
Pulmonary Disease
Chronic Obstructive
03 medical and health sciences
Patient Admission
0302 clinical medicine
Randomized controlled trial
law
health services administration
Original Reports
medicine
Original Report
Electronic Health Records
Humans
Pharmacology (medical)
030212 general & internal medicine
education
Case report form
health care economics and organizations
pulmonary disease
Aged
Randomized Controlled Trials as Topic
validation
COPD
education.field_of_study
chronic obstructive
business.industry
Data Collection
Middle Aged
Pharmacoepidemiology
Symptom Flare Up
medicine.disease
Clinical trial
Clinical Trials
Phase III as Topic
England
Emergency medicine
Female
business
Zdroj: Sperrin, M, Patel, P, Davis, K J, Collier, S, Pate, A, Leather, D A & Pimenta, J M 2019, ' Chronic obstructive pulmonary disease exacerbation episodes derived from electronic health record data validated using clinical trial data ', Pharmacoepidemiology and Drug Safety . https://doi.org/10.1002/pds.4883
Pharmacoepidemiology and Drug Safety
ISSN: 1099-1557
1053-8569
DOI: 10.1002/pds.4883
Popis: PURPOSE: To 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).METHODS: We 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.RESULTS: In 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.CONCLUSIONS: The 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.
Databáze: OpenAIRE
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