ArticleCardiovascular diabetology2022
Development and validation of a model to predict cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke in patients with type 2 diabetes mellitus and established atherosclerotic cardiovascular disease.
Article in Cardiovascular diabetology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Blood-Based DNA Methylation Models Improve Short-term Cardiovascular Risk Stratification in Individuals With Type 2 Diabetes.Diabetes care · 2026Article
- Development and validation of a long-term survival prediction model for older adults with asthma.Archives of public health = Archives belges de sante publique · 2026Article
- Cost-efficient and Accurate Risk Assessment Instruments in Type 2 Diabetics with Greatest Risk for Cardiovascular Disease.Cardiology and cardiovascular medicine · 2026Article
- Prognostic value of lesion-specific and proximal coronary segment pericoronary adipose tissue CT Attenuation in ischemic heart disease with angina pectoris.Scientific reports · 2025Article
- Association of baseline and trajectory of triglyceride-glucose index with the incidence of cardiovascular autonomic neuropathy in type 2 diabetes mellitus.Cardiovascular diabetology · 2025Article
- Incremental effect of coronary obstruction on myocardial microvascular dysfunction in type 2 diabetes mellitus patients evaluated by first-pass perfusion CMR study.Cardiovascular diabetology · 2023Article
- CT-derived fractional flow reserve for prediction of major adverse cardiovascular events in diabetic patients.Cardiovascular diabetology · 2023Article
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Authors and funding
9 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAmong individuals with atherosclerotic cardiovascular disease (ASCVD), type 2 diabetes mellitus (T2DM) is common and confers increased risk for morbidity and mortality. Differentiating risk is key to optimize efficiency of treatment selection. Our objective was to develop and validate a model to predict risk of major adverse cardiovascular events (MACE) comprising the first event of cardiovascular death, myocardial infarction (MI), or stroke for individuals with both T2DM and ASCVD.
methodsUsing data from the Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS), we used Cox proportional hazards models to predict MACE among participants with T2DM and ASCVD. All baseline covariates collected in the trial were considered for inclusion, although some were excluded immediately because of large missingness or collinearity. A full model was developed using stepwise selection in each of 25 imputed datasets, and comprised candidate variables selected in 20 of the 25 datasets. A parsimonious model with a maximum of 10 degrees of freedom was created using Cox models with least absolute shrinkage and selection operator (LASSO), where the adjusted R-square was used as criterion for selection. The model was then externally validated among a cohort of participants with similar criteria in the ACCORD (Action to Control Cardiovascular Risk in Diabetes) trial. Discrimination of both models was assessed using Harrell's C-index and model calibration by the Greenwood-Nam-D'Agostino statistic based on 4-year event rates.
resultsOverall, 1491 (10.2%) of 14,671 participants in TECOS and 130 (9.3%) in the ACCORD validation cohort (n = 1404) had MACE over 3 years' median follow-up. The final model included 9 characteristics (prior stroke, age, chronic kidney disease, prior MI, sex, heart failure, insulin use, atrial fibrillation, and microvascular complications). The model had moderate discrimination in both the internal and external validation samples (C-index = 0.65 and 0.61, respectively). The model was well calibrated across the risk spectrum-from a cumulative MACE rate of 6% at 4 years in the lowest risk quintile to 26% in the highest risk quintile.
conclusionAmong patients with T2DM and prevalent ASCVD, this 9-factor risk model can quantify the risk of future ASCVD complications and inform decision making for treatments and intensity.
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