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Review of codelists used to define hypertension in electronic health records and development of a codelist for research

Authors :
Jennifer Kathleen Quint
Christopher Denton
Fasihul Khan
Iain Stewart
Gisli Jenkins
Ali-Reza Mohammadi-Nejad
Dorothee Auer
Karen Piper Hanley
Michael Nation
Harley H Y Kwok
Jane Paxton
Elizabeth Robertson
Anna Duckworth
Chris Scotton
Aloysious Aravinthan
Hilary Longhurst
Mujdat Zeybel
Louise V Wain
Philip W Stone
Richard J Allen
Maria Kaisar
Lisa Chakrabarti
Georgie May Massen
Andrew Thorley
Anthony Harbottle
Armando Mendez Villalon
Daniel Lea
Ebrima Joof
Eleanor Cox
Elizabeth Eves
Emma Blamont
Gina Parcesepe
Gordon W. Moran
Guruprasad P. Aithal
Kate Frost
Leo Casmino
Margot Roeth
Martin Craig
Mohammad Alireza Kisomi
Source :
Open Heart, Vol 11, Iss 1 (2024)
Publication Year :
2024
Publisher :
BMJ Publishing Group, 2024.

Abstract

Background and aims Hypertension is a leading risk factor for cardiovascular disease. Electronic health records (EHRs) are routinely collected throughout a person’s care, recording all aspects of health status, including current and past conditions, prescriptions and test results. EHRs can be used for epidemiological research. However, there are nuances in the way conditions are recorded using clinical coding; it is important to understand the methods which have been applied to define exposures, covariates and outcomes to enable interpretation of study findings. This study aimed to identify codelists used to define hypertension in studies that use EHRs and generate recommended codelists to support reproducibility and consistency.Eligibility criteria Studies included populations with hypertension defined within an EHR between January 2010 and August 2023 and were systematically identified using MEDLINE and Embase. A summary of the most frequently used sources and codes is described. Due to an absence of Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) codelists in the literature, a recommended SNOMED CT codelist was developed to aid consistency and standardisation of hypertension research using EHRs.Findings 375 manuscripts met the study criteria and were eligible for inclusion, and 112 (29.9%) reported codelists. The International Classification of Diseases (ICD) was the most frequently used clinical terminology, 59 manuscripts provided ICD 9 codelists (53%) and 58 included ICD 10 codelists (52%). Informed by commonly used ICD and Read codes, usage recommendations were made. We derived SNOMED CT codelists informed by National Institute for Health and Care Excellence guidelines for hypertension management. It is recommended that these codelists be used to identify hypertension in EHRs using SNOMED CT codes.Conclusions Less than one-third of hypertension studies using EHRs included their codelists. Transparent methodology for codelist creation is essential for replication and will aid interpretation of study findings. We created SNOMED CT codelists to support and standardise hypertension definitions in EHR studies.

Details

Language :
English
ISSN :
20533624
Volume :
11
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Open Heart
Publication Type :
Academic Journal
Accession number :
edsdoj.2f2add5b3f1648c1a56b0060a91009fa
Document Type :
article
Full Text :
https://doi.org/10.1136/openhrt-2024-002640