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Abstract Hearing loss has been associated with individual cardiovascular disease (CVD) risk factors and, to a lesser extent, CVD risk metrics. However, these relationships are understudied in clinical populations. We conducted a retrospective study of electronic health records to evaluate the relationship between hearing loss and CVD risk burden. Hearing loss was defined as puretone average (PTA 0.5,1,2,4 ) > 20 dB hearing level (HL). Optimal CVD risk was defined as nondiabetic, nonsmoking, systolic blood pressure (SBP) < 120 and diastolic (D)BP < 80 mm Hg, and total cholesterol < 180 mg/dL. Major CVD risk factors were diabetes, smoking, hypertension, and total cholesterol ≥ 240 mg/dL or statin use. We identified 6332 patients (mean age = 62.96 years; 45.5% male); 64.0% had hearing loss. Sex-stratified logistic regression adjusted for age, noise exposure, hearing aid use, and body mass index examined associations between hearing loss and CVD risk. For males, diabetes, hypertension, smoking, and ≥ 2 major CVD risk factors were associated with hearing loss. For females, diabetes, smoking, and ≥ 2 major CVD risk factors were significant risk factors. Compared to those with no CVD risk factors, there is a higher likelihood of hearing loss in patients with ≥ 2 major CVD risk factors. Future research to better understand sex dependence in the hearing loss-hypertension relationship is indicated.
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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
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.
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- PAR ID:
- 10404732
- Date Published:
- Journal Name:
- JAMIA Open
- Volume:
- 6
- Issue:
- 1
- ISSN:
- 2574-2531
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/jamiaopen/ooad006
