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
; 6(1): ooad006, 2023 Apr.
Article
in En
| MEDLINE
| ID: mdl-36789288
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 andMethods:
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 49â049 participants with diabetes. Among them, 1023 had type 1 diabetes (T1D), and 40â193 had type 2 diabetes (T2D). A total of 23â833 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 andConclusion:
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Etiology_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
JAMIA Open
Year:
2023
Document type:
Article
Affiliation country:
Estados Unidos