| Title: | 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 |
| Journal: | JAMIA Open |
| Published: | 6 Jan 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36789288/ |
| DOI: | https://doi.org/10.1093/jamiaopen/ooad006 |
| URL: | https://academic.oup.com/jamiaopen/article-pdf/6/1/ooad006/49138927/ooad006.pdf |
| Citations: | 3 (3 in last 2 years) as of 8 Aug 2024 |
Publication 10936
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
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.</p>
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.</p>
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.</p>
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.</p>
11 Authors
- 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
1 Application
| Application ID | Title |
|---|---|
| 48152 | Developing statistical methods and computational algorithms for identifying biomarkers at Biobank-data scale for cardio-metabolic traits |
Enabling scientific discoveries that improve human health