ArticleDiabetes, obesity & metabolism2022
Predicting incident heart failure among patients with type 2 diabetes mellitus: The DM-CURE risk score.
Article in Diabetes, obesity & metabolism, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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8 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Overcoming Missing Data: Accurately Predicting Cardiovascular Risk in Type 2 Diabetes, A Systematic Review.Journal of diabetes · 2025Pooled it
- Natural Language Processing of Unstructured Healthcare Data for Predicting Heart Failure in Individuals with Type 2 Diabetes.Journal of clinical medicine · 2026Article
- Assessing the risk of heart failure in type 2 diabetes: a prediction algorithm to sustain the evaluation of NT-proBNP in primary care.Endocrine · 2025Article
- Prognostic models for cardiovascular and kidney outcomes in people with type 2 diabetes: living systematic review and meta-analysis of observational studies.BMJ medicine · 2025Article
- Correlation between glycemic control and left ventricular diastolic dysfunction in type 2 diabetes.Bioinformation · 2025Article
- Leveraging calcium score CT radiomics for heart failure risk prediction.Scientific reports · 2024Article
- Article
- Machine learning identification of risk factors for heart failure in patients with diabetes mellitus with metabolic dysfunction associated steatotic liver disease (MASLD): the Silesia Diabetes-Heart Project.Cardiovascular diabetology · 2023Article
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5 authors at 2 institutions in 1 country.
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Abstract
aimEarly identification and prediction of incident heart failure (HF) is important because of severe morbidity and mortality. This study aimed to predict onset of HF among patients with diabetes.
methodsA time-varying Cox model was derived from ACCORD clinical trial to predict the risk of incident HF, defined by hospitalization for HF (HHF). External validation was performed on patient-level data from the Harmony Outcome trial and Chronic Renal Insufficiency Cohort (CRIC) study. The model was transformed into an integer-based scoring algorithm for 10-year risk evaluation. A stepwise algorithm identified and selected predictors from demographic characteristics, physical examination, laboratory results, medical history, medication and health care utilization, to develop a risk prediction model. The main outcome was incident HF, defined by HHF. The C statistic and Brier score were used to assess model performance.
resultsIn total, 9649 patients with diabetes free of HF were used, with median follow-up of 4 years and 299 incident hospitalization of HF events. The model identified several predictors for the 10-year HF incidence risk score 'DM-CURE': socio-Demographic [education, age at type 2 diabetes (T2DM) diagnosis], Metabolic (glycated haemoglobin, systolic blood pressure, body mass index, high-density lipoproteins), diabetes-related Complications (myocardial infarction, revascularization, cardiovascular medications, neuropathy, hypertension duration, albuminuria, urine albumin-to-creatinine ratio, End Stage Kidney Disease), and health care Utilization (all-cause hospitalization, emergency room visits) for Risk Evaluation. Among them, the strongest impact factors for future HF were age at T2DM diagnosis, health care utilization and cardiovascular disease-related variables. The model showed good discrimination (C statistic: 0.838, 95% CI: 0.821-0.855) and calibration (Brier score: 0.006, 95% CI: 0.006-0.007) in the ACCORD data and good performance in the validation data (Harmony: C statistic: 0.881, 95% CI: 0.863-0.899; CRIC: C statistic: 0.813, 95% CI: 0.794-0.833). The 10-year risk of incident HF increased in a graded fashion, from ≤1% in quintile 1 (score ≤14), 1%-5% in quintile 2 (score 15-23), 5%-10% in quintile 3 (score 24-27), 10%-20% in quintile 4 (score 28-33) and ≥20% in quintile 5 (score >33).
conclusionsThe DM-CURE model and score were useful for population risk stratification of incident HHF among patients with T2DM and can be easily applied in clinical practice.
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