ArticleCardiovascular diabetology2023
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.
Article in Cardiovascular diabetology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05626413 (Cardiovascular Disease and Diabetes in Silesian Patients), which is not on this map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Cardiovascular Disease and Diabetes in Silesian Patients
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Performance of Machine Learning Algorithms for Predicting Heart Failure in Diabetic Patients: A Systematic Review and Meta-Analysis.Endocrinology, diabetes & metabolism · 2025Pooled it
- Risk-associated and clinically informative biomarkers for cardiovascular risk stratification in metabolic dysfunction-Associated steatotic liver disease.American journal of preventive cardiology · 2026Article
- Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches.World journal of gastroenterology · 2026Review
- The application of artificial intelligence in the intersection of metabolic dysfunction-associated steatotic liver disease and cardiovascular diseases.Frontiers in immunology · 2026Review
- Logistic Regression and Machine Learning Algorithms for the Risk Prediction of Perioperative Adverse Cardiovascular Events in Elderly Patients.Aging medicine (Milton (N.S.W)) · 2025Article
- Estimated glucose disposal rate outperforms other insulin resistance surrogates in predicting incident cardiovascular diseases in cardiovascular-kidney-metabolic syndrome stages 0-3 and the development of a machine learning prediction model: a nationwide prospective cohort study.Cardiovascular diabetology · 2025Article
- Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods.Scientific reports · 2025Article
- Sex differences in clinical profile, left ventricular remodeling and cardiovascular outcomes among diabetic patients with heart failure and reduced ejection fraction: a cardiac-MRI-based study.Cardiovascular diabetology · 2024Article
- Machine Learning Identifies Metabolic Dysfunction-Associated Steatotic Liver Disease in Patients With Diabetes Mellitus.The Journal of clinical endocrinology and metabolism · 2024Article
- Protocol for a Longitudinal Cohort Study to Understand Characteristics and Risk Factors Underlying Vibration-Controlled Transient Elastography-Diagnosed Metabolic Dysfunction-Associated Fatty Liver Disease Children.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundDiabetes mellitus (DM), heart failure (HF) and metabolic dysfunction associated steatotic liver disease (MASLD) are overlapping diseases of increasing prevalence. Because there are still high numbers of patients with HF who are undiagnosed and untreated, there is a need for improving efforts to better identify HF in patients with DM with or without MASLD. This study aims to develop machine learning (ML) models for assessing the risk of the HF occurrence in patients with DM with and without MASLD. RESEARCH DESIGN AND
methodsIn the Silesia Diabetes-Heart Project (NCT05626413), patients with DM with and without MASLD were analyzed to identify the most important HF risk factors with the use of a ML approach. The multiple logistic regression (MLR) classifier exploiting the most discriminative patient's parameters selected by the χ2 test following the Monte Carlo strategy was implemented. The classification capabilities of the ML models were quantified using sensitivity, specificity, and the percentage of correctly classified (CC) high- and low-risk patients.
resultsWe studied 2000 patients with DM (mean age 58.85 ± SD 17.37 years; 48% women). In the feature selection process, we identified 5 parameters: age, type of DM, atrial fibrillation (AF), hyperuricemia and estimated glomerular filtration rate (eGFR). In the case of MASLD( +) patients, the same criterion was met by 3 features: AF, hyperuricemia and eGFR, and for MASLD(-) patients, by 2 features: age and eGFR. Amongst all patients, sensitivity and specificity were 0.81 and 0.70, respectively, with the area under the receiver operating curve (AUC) of 0.84 (95% CI 0.82-0.86).
conclusionA ML approach demonstrated high performance in identifying HF in patients with DM independently of their MASLD status, as well as both in patients with and without MASLD based on easy-to-obtain patient parameters.
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