| Title: | Prediction of Incident Heart Failure in Men and Women with a History of Myocardial Infarction |
| Journal: | ESC Heart Failure |
| Published: | 30 Jun 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42376861/ |
| DOI: | https://doi.org/10.1093/eschf/xvag187 |
Publication 20494
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Abstract
AIMS: Individuals with prior myocardial infarction (MI) show increased risk of heart failure (HF), yet current risk prediction models leave room for improvement. We aimed to develop and validate a clinically viable prediction model for incident HF in persons with prevalent MI in a general population, and explore sex- and MI subtype-specific differences.</p>
METHODS: We analysed UK Biobank participants with prior MI but absence of HF at baseline. Sociodemographic factors, clinical variables, and 59 blood biomarkers were included. The primary endpoint was the first in-hospital HF diagnosis. Backward selected Cox proportional hazards models were used to identify independent predictors of incident HF. Model performance was assessed via discrimination (C-index), using internal- and hold-out validation, and calibration.</p>
RESULTS: A total of 4,743 participants with prevalent MI; 81.4% male, median (P25-P75) age 62(58-66) years, were included. During a median follow-up of 12.1(11.1-13.0) years, 767(16.2%) developed HF. Sixteen independent predictors were identified and eleven remained significant (P<0.05) after treating all-cause mortality as a competing risk. These included several well-established predictors (age, body mass index, smoking, atrial fibrillation, diabetes) as well as haemoglobin (HR per 1 standard deviation (SD) increase: 0.88(95%CI:0.79-0.96)), mean reticulocyte volume (HR: (1.13(95%CI:1.03-1.23)), and neutrophil percentage (HR:1.12(95%CI:1.03-1.21)), monocyte count (HR:1.11(95%CI,1.06-1.16)). A trend towards an interaction(P<0.1) between sex and MI subtype was present. Model discrimination was modest (C-index=0.67) and calibration was adequate.</p>
CONCLUSION: Our study identifies several clinically accessible blood biomarkers as important risk factors for HF in post-MI individuals, and suggests interactions between sex and MI subtype. Further model refinement and external validation are needed.</p>
7 Authors
- Phoebe Chan
- Thomas F Kok
- Robert M A van der Boon
- Navin Suthahar
- Rudolf A de Boer
- Eric Boersma
- Isabella Kardys
1 Application
| Application ID | Title |
|---|---|
| 88185 | Prediction of incident heart failure using machine learning techniques in men and women with- and without a history of myocardial infarction. |
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