Predicting incident heart failure among patients with type 2 diabetes mellitus: The DM-CURE risk score.

Pubmed ID: 35801340

Pubmed Central ID: PMC10201412

Journal: Diabetes, obesity & metabolism

Publication Date: Nov. 1, 2022

MeSH Terms: Humans, Risk Factors, Risk Assessment, Heart Failure, Creatinine, Diabetes Mellitus, Type 2, Child, Preschool, Lipoproteins, HDL, Glycated Hemoglobin, Albumins

Grants: U54 GM104940

Authors: Fonseca V, Lin Y, Shao H, Shi L, Anderson AH

Cite As: Lin Y, Shao H, Shi L, Anderson AH, Fonseca V. Predicting incident heart failure among patients with type 2 diabetes mellitus: The DM-CURE risk score. Diabetes Obes Metab 2022 Nov;24(11):2203-2211. Epub 2022 Aug 8.

Studies:

Abstract

AIM: Early 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. METHODS: A 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. RESULTS: In 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). CONCLUSIONS: The 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.