A platform for phenotyping disease progression and associated longitudinal risk factors in large-scale EHRs, with application to incident diabetes complications in the UK Biobank

Do Hyun Kim; Aubrey Jensen; Kelly Jones; Sridharan Raghavan; Lawrence S Phillips; Adriana Hung; Yan V Sun; Gang Li; Peter Reaven; Hua Zhou; Jin J Zhou

doi:10.1093/jamiaopen/ooad006

A platform for phenotyping disease progression and associated longitudinal risk factors in large-scale EHRs, with application to incident diabetes complications in the UK Biobank

JAMIA Open. 2023 Feb 9;6(1):ooad006. doi: 10.1093/jamiaopen/ooad006. eCollection 2023 Apr.

Authors

Do Hyun Kim¹, Aubrey Jensen¹, Kelly Jones², Sridharan Raghavan^{3

4}, Lawrence S Phillips^{5

6}, Adriana Hung^{7

8}, Yan V Sun⁹, Gang Li^{1

10}, Peter Reaven¹¹, Hua Zhou^{1

10}, Jin J Zhou^{1

11

12}

Affiliations

¹ Department of Biostatistics, University of California, Los Angeles, California, USA.
² Department of Computer Science, Columbia University, New York, New York, USA.
³ Division of Hospital Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA.
⁴ Rocky Mountain Regional VA Medical Center, Aurora, Colorado, USA.
⁵ Division of Endocrinology, Emory University School of Medicine, Atlanta, Georgia, USA.
⁶ Atlanta VA Medical Center, Decatur, Georgia, USA.
⁷ VA Tennessee Valley Healthcare System, Nashville, Tennessee, USA.
⁸ Vanderbilt University, Nashville, Tennessee, USA.
⁹ Department of Epidemiology, Emory University, Atlanta, Georgia, USA.
¹⁰ Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, USA.
¹¹ Phoenix VA Health Care System, Phoenix, Arizona, USA.
¹² Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, USA.

Abstract

Objective: Modern healthcare data reflect massive multi-level and multi-scale information collected over many years. The majority of the existing phenotyping algorithms use case-control definitions of disease. This paper aims to study the time to disease onset and progression and identify the time-varying risk factors that drive them.

Materials and methods: We developed an algorithmic approach to phenotyping the incidence of diseases by consolidating data sources from the UK Biobank (UKB), including primary care electronic health records (EHRs). We focused on defining events, event dates, and their censoring time, including relevant terms and existing phenotypes, excluding generic, rare, or semantically distant terms, forward-mapping terminology terms, and expert review. We applied our approach to phenotyping diabetes complications, including a composite cardiovascular disease (CVD) outcome, diabetic kidney disease (DKD), and diabetic retinopathy (DR), in the UKB study.

Results: We identified 49 049 participants with diabetes. Among them, 1023 had type 1 diabetes (T1D), and 40 193 had type 2 diabetes (T2D). A total of 23 833 diabetes subjects had linked primary care records. There were 3237, 3113, and 4922 patients with CVD, DKD, and DR events, respectively. The risk prediction performance for each outcome was assessed, and our results are consistent with the prediction area under the ROC (receiver operating characteristic) curve (AUC) of standard risk prediction models using cohort studies.

Discussion and conclusion: Our publicly available pipeline and platform enable streamlined curation of incidence events, identification of time-varying risk factors underlying disease progression, and the definition of a relevant cohort for time-to-event analyses. These important steps need to be considered simultaneously to study disease progression.

Keywords: diabetes; diabetes complications; disease progression; electronic health records; phenotyping; time-to-event.

Abstract

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