Dataset Information

Cardiovascular disease risk prediction for people with type 2 diabetes in a population-based cohort and in electronic health record data.

ABSTRACT:

Objective

Electronic health records (EHRs) have become a common data source for clinical risk prediction, offering large sample sizes and frequently sampled metrics. There may be notable differences between hospital-based EHR and traditional cohort samples: EHR data often are not population-representative random samples, even for particular diseases, as they tend to be sicker with higher healthcare utilization, while cohort studies often sample healthier subjects who typically are more likely to participate. We investigate heterogeneities between EHR- and cohort-based inferences including incidence rates, risk factor identifications/quantifications, and absolute risks.

Materials and methods

This is a retrospective cohort study of older patients with type 2 diabetes using EHR from New York University Langone Health ambulatory care (NYULH-EHR, years 2009-2017) and from the Health and Retirement Survey (HRS, 1995-2014) to study subsequent cardiovascular disease (CVD) risks. We used the same eligibility criteria, outcome definitions, and demographic covariates/biomarkers in both datasets. We compared subsequent CVD incidence rates, hazard ratios (HRs) of risk factors, and discrimination/calibration performances of CVD risk scores.

Results

The estimated subsequent total CVD incidence rate was 37.5 and 90.6 per 1000 person-years since T2DM onset in HRS and NYULH-EHR respectively. HR estimates were comparable between the datasets for most demographic covariates/biomarkers. Common CVD risk scores underestimated observed total CVD risks in NYULH-EHR.

Discussion and conclusion

EHR-estimated HRs of demographic and major clinical risk factors for CVD were mostly consistent with the estimates from a national cohort, despite high incidences and absolute risks of total CVD outcome in the EHR samples.

SUBMITTER: Szymonifka J

PROVIDER: S-EPMC7886535 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Cardiovascular disease risk prediction for people with type 2 diabetes in a population-based cohort and in electronic health record data.

Szymonifka Jackie J Conderino Sarah S Cigolle Christine C Ha Jinkyung J Kabeto Mohammed M Yu Jaehong J Dodson John A JA Thorpe Lorna L Blaum Caroline C Zhong Judy J

JAMIA open 20201205 4

<h4>Objective</h4>Electronic health records (EHRs) have become a common data source for clinical risk prediction, offering large sample sizes and frequently sampled metrics. There may be notable differences between hospital-based EHR and traditional cohort samples: EHR data often are not population-representative random samples, even for particular diseases, as they tend to be sicker with higher healthcare utilization, while cohort studies often sample healthier subjects who typically are more l ...[more]

PMID: 33623893

Similar Datasets

Project description:BackgroundSjögren's disease (SD) is an autoimmune disease that is difficult to diagnose early due to its wide spectrum of clinical symptoms and overlap with other autoimmune diseases. SD potentially presents through early oral manifestations prior to showing symptoms of clinically significant dry eyes or dry mouth. We examined the feasibility of utilizing a linked electronic dental record (EDR) and electronic health record (EHR) dataset to identify factors that could be used to improve early diagnosis prediction of SD in a matched case-control study population.MethodsEHR data, including demographics, medical diagnoses, medication history, serological test history, and clinical notes, were retrieved from the Indiana Network for Patient Care database and dental procedure data were retrieved from the Indiana University School of Dentistry EDR. We examined EHR and EDR history in the three years prior to SD diagnosis for SD cases and the corresponding period in matched non-SD controls. Two conditional logistic regression (CLR) models were built using Least Absolute Shrinkage and Selection Operator regression. One used only EHR data and the other used both EHR and EDR data. The ability of these models to predict SD diagnosis was assessed using a concordance index designed for CLR.ResultsWe identified a sample population of 129 cases and 371 controls with linked EDR-EHR data. EHR factors associated with an increased risk of SD diagnosis were the usage of lubricating throat drugs with an odds ratio (OR) of 14.97 (2.70-83.06), dry mouth (OR = 6.19, 2.14-17.89), pain in joints (OR = 2.54, 1.34-4.76), tear film insufficiency (OR = 27.04, 5.37-136.), and rheumatoid factor testing (OR = 6.97, 1.94-25.12). The addition of EDR data slightly improved model concordance compared to the EHR only model (0.834 versus 0.811). Surgical dental procedures (OR = 2.33, 1.14-4.78) were found to be associated with an increased risk of SD diagnosis while dental diagnostic procedures (OR = 0.45, 0.20-1.01) were associated with decreased risk.ConclusionUtilizing EDR data alongside EHR data has the potential to improve prediction models for SD. This could improve the early diagnosis of SD, which is beneficial to slowing or preventing complications of SD.

Project description:AimsTo characterise the cardiovascular risk of people with type 2 diabetes without established cardiovascular disease but with risk factors, relative to those with established cardiovascular disease, to provide information on which patients could benefit from early use of glucose-lowering therapies that also reduce cardiovascular risk.MethodsData from people with type 2 diabetes initiating second-line glucose-lowering medication were retrieved from the UK Clinical Practice Research Datalink GOLD database and linked with Hospital Episode Statistics and Office for National Statistics (2001-2016). Cox proportional hazards models were used to estimate relative risks of major adverse cardiovascular events within groups defined by the presence of selected risk factors in people without versus with established cardiovascular disease.ResultsOf 53,182 individuals, 19.4% had established cardiovascular disease (i.e. a prior cardiovascular event). Over 5-7 years' follow-up, the rate of major adverse cardiovascular events was 14.0 and 49.6 events/1000 person-years without and with established cardiovascular disease, respectively (hazard ratio [HR] 0.28, 95% confidence interval [CI] 0.26, 0.29). Compared with a reference HR 1.0 for participants with established cardiovascular disease, estimated glomerular filtration rate <60 mL/min was the single factor associated with the highest risk of major adverse cardiovascular events (HR 0.75, 95% CI 0.70, 0.81) and mortality (HR 1.12, 95% CI 1.07, 1.18) in people with type 2 diabetes without established cardiovascular disease. The combination of chronic kidney disease with older age, smoking and/or dyslipidaemia was associated with a similarly high risk of cardiovascular events in people with type 2 diabetes and without cardiovascular disease compared with those having established cardiovascular disease.ConclusionsThese analyses provide important information to identify people who may benefit from primary prevention of cardiovascular disease. Modifiable cardiovascular risk factors should be controlled early in all individuals with type 2 diabetes (as well as in all individuals with cardiovascular disease).

Project description:BackgroundSuicide risk prediction models have been developed by using information from patients' electronic health records (EHR), but the time elapsed between model development and health system implementation is often substantial. Temporal changes in health systems and EHR coding practices necessitate the evaluation of such models in more contemporary data.ObjectivesA set of published suicide risk prediction models developed by using EHR data from 2009 to 2015 across seven health systems reported c-statistics of 0.85 for suicide attempt and 0.83 to 0.86 for suicide death. Our objective was to evaluate these models' performance with contemporary data (2014-2017) from these systems.MethodsWe evaluated performance using mental health visits (6,832,439 to mental health specialty providers and 3,987,078 to general medical providers) from 2014 to 2017 made by 1,799,765 patients aged 13+ across the health systems. No visits in our evaluation were used in the previous model development. Outcomes were suicide attempt (health system records) and suicide death (state death certificates) within 90 days following a visit. We assessed calibration and computed c-statistics with 95% confidence intervals (CI) and cut-point specific estimates of sensitivity, specificity, and positive/negative predictive value.ResultsModels were well calibrated; 46% of suicide attempts and 35% of suicide deaths in the mental health specialty sample were preceded by a visit (within 90 days) with a risk score in the top 5%. In the general medical sample, 53% of attempts and 35% of deaths were preceded by such a visit. Among these two samples, respectively, c-statistics were 0.862 (95% CI: 0.860-0.864) and 0.864 (95% CI: 0.860-0.869) for suicide attempt, and 0.806 (95% CI: 0.790-0.822) and 0.804 (95% CI: 0.782-0.829) for suicide death.ConclusionPerformance of the risk prediction models in this contemporary sample was similar to historical estimates for suicide attempt but modestly lower for suicide death. These published models can inform clinical practice and patient care today.

Dataset Information

Cardiovascular disease risk prediction for people with type 2 diabetes in a population-based cohort and in electronic health record data.

Objective

Materials and methods

Results

Discussion and conclusion

Publications

Cardiovascular disease risk prediction for people with type 2 diabetes in a population-based cohort and in electronic health record data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets