A semi-supervised approach for rapidly creating clinical biomarker phenotypes in the UK Biobank using different primary care EHR and clinical terminology systems.

Spiros Denaxas,Harry Hemingway,Valerie Kuan,Bilal A Mateen,Anoop D Shah,Ghazaleh Fatemifar,Jennifer K Quint,Natalie Fitzpatrick,Ana Torralbo

doi:10.1093/jamiaopen/ooaa047

Abstract

ObjectivesThe UK Biobank (UKB) is making primary care electronic health records (EHRs) for 500 000 participants available for COVID-19-related research. Data are extracted from four sources, recorded using five clinical terminologies and stored in different schemas. The aims of our research were to: (a) develop a semi-supervised approach for bootstrapping EHR phenotyping algorithms in UKB EHR, and (b) to evaluate our approach by implementing and evaluating phenotypes for 31 common biomarkers.Materials and MethodsWe describe an algorithmic approach to phenotyping biomarkers in primary care EHR involving (a) bootstrapping definitions using existing phenotypes, (b) excluding generic, rare, or semantically distant terms, (c) forward-mapping terminology terms, (d) expert review, and (e) data extraction. We evaluated the phenotypes by assessing the ability to reproduce known epidemiological associations with all-cause mortality using Cox proportional hazards models.ResultsWe created and evaluated phenotyping algorithms for 31 biomarkers many of which are directly related to COVID-19 complications, for example diabetes, cardiovascular disease, respiratory disease. Our algorithm identified 1651 Read v2 and Clinical Terms Version 3 terms and automatically excluded 1228 terms. Clinical review excluded 103 terms and included 44 terms, resulting in 364 terms for data extraction (sensitivity 0.89, specificity 0.92). We extracted 38 190 682 events and identified 220 978 participants with at least one biomarker measured.Discussion and conclusionBootstrapping phenotyping algorithms from similar EHR can potentially address pre-existing methodological concerns that undermine the outputs of biomarker discovery pipelines and provide research-quality phenotyping algorithms.

Highlights

UK Biobank (UKB) is the largest longitudinal research study in the UK ($500 000 participants), and one of the largest globally.[1]
We observed that a decrease in forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) was associated with an increased risk of mortality and an increase in C-reactive protein (CRP) was associated with an increased risk
We have demonstrated the challenges that UK Biobank researchers will face when extracting biomarker values from the primary care electronic health records (EHRs) records of participants

Summary

Introduction

UK Biobank (UKB) is the largest longitudinal research study in the UK ($500 000 participants), and one of the largest globally.[1] To further enrich this cohort’s data, UKB has begun to link the wealth of information already collected from each individual to their primary care electronic health record (EHR).[1] In the UK, the first point of contact with the health service for individuals with a new (nonemergency) medical problem or a chronic condition is their local general practitioner (GP). Researchers with no previous experience working with primary care EHR would need to dedicate a significant amount of time and effort to create phenotyping algorithms for important biomarkers, for example blood pressure or hematological laboratory markers In this manuscript, we use the term “biomarker” to refer to well-established, and measurable, clinical or laboratory parameters that are used in routine clinical care as indicators of a particular disease or other physiological state.

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