Papez, V;
Moinat, M;
Payralbe, S;
Asselbergs, FW;
Lumbers, RT;
Hemingway, H;
Dobson, R;
(2021)
Transforming and evaluating electronic health record disease phenotyping algorithms using the OMOP common data model: a case study in heart failure.
JAMIA Open
, Article ooab001. 10.1093/jamiaopen/ooab001.
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Abstract
Objective: The aim of the study was to transform a resource of linked electronic health records (EHR) to the OMOP common data model (CDM) and evaluate the process in terms of syntactic and semantic consistency and quality when implementing disease and risk factor phenotyping algorithms. Materials and Methods: Using heart failure (HF) as an exemplar, we represented three national EHR sources (Clinical Practice Research Datalink, Hospital Episode Statistics Admitted Patient Care, Office for National Statistics) into the OMOP CDM 5.2. We compared the original and CDM HF patient population by calculating and presenting descriptive statistics of demographics, related comorbidities, and relevant clinical biomarkers. Results: We identified a cohort of 502 536 patients with the incident and prevalent HF and converted 1 099 195 384 rows of data from 216 581 914 encounters across three EHR sources to the OMOP CDM. The largest percentage (65%) of unmapped events was related to medication prescriptions in primary care. The average coverage of source vocabularies was >98% with the exception of laboratory tests recorded in primary care. The raw and transformed data were similar in terms of demographics and comorbidities with the largest difference observed being 3.78% in the prevalence of chronic obstructive pulmonary disease (COPD). Conclusion: Our study demonstrated that the OMOP CDM can successfully be applied to convert EHR linked across multiple healthcare settings and represent phenotyping algorithms spanning multiple sources. Similar to previous research, challenges mapping primary care prescriptions and laboratory measurements still persist and require further work. The use of OMOP CDM in national UK EHR is a valuable research tool that can enable large-scale reproducible observational research.
| Type: | Article |
|---|---|
| Title: | Transforming and evaluating electronic health record disease phenotyping algorithms using the OMOP common data model: a case study in heart failure |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/jamiaopen/ooab001 |
| Publisher version: | http://dx.doi.org/10.1093/jamiaopen/ooab001 |
| Language: | English |
| Additional information: | VC The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | heart failure, EHR, phenotyping, OMOP, algorithms |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Infectious Disease Informatics |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10121919 |
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