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Identifying COPD in routinely collected electronic health records: a systematic scoping review / Shanya Sivakumaran, Mohammad A. Al Sallakh, Ronan Lyons, Jennifer K. Quint, Gwyneth Davies
ERJ Open Research, Volume: 7, Issue: 3, Pages: 00167 - 2021
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Although routinely collected electronic health records (EHRs) are widely used to examine outcomes related to COPD, consensus regarding the identification of cases from electronic healthcare databases is lacking. We systematically examine and summarise approaches from the recent literature. MEDLINE &...
|Published in:||ERJ Open Research|
European Respiratory Society (ERS)
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Although routinely collected electronic health records (EHRs) are widely used to examine outcomes related to COPD, consensus regarding the identification of cases from electronic healthcare databases is lacking. We systematically examine and summarise approaches from the recent literature. MEDLINE <i>via</i> EBSCOhost was searched for COPD-related studies using EHRs published from January 1, 2018 to November 30, 2019. Data were extracted relating to the case definition of COPD and determination of COPD severity and phenotypes. From 185 eligible studies, we found widespread variation in the definitions used to identify people with COPD in terms of code sets used (with 20 different code sets in use based on the ICD-10 classification alone) and requirement of additional criteria (relating to age (n=139), medication (n=31), multiplicity of events (n=21), spirometry (n=19) and smoking status (n=9)). Only seven studies used a case definition which had been validated against a reference standard in the same dataset. Various proxies of disease severity were used since spirometry results and patient-reported outcomes were not often available. To enable the research community to draw reliable insights from EHRs and aid comparability between studies, clear reporting and greater consistency of the definitions used to identify COPD and related outcome measures is key.
Swansea University Medical School
Swansea University Medical School Grant: n/a BREATHE - The Health Data Research Hub for Respiratory Health Grant: MC_PC_19004