ArticleBiomolecules2022
Machine Learning Approach to Understand Worsening Renal Function in Acute Heart Failure.
Article in Biomolecules, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Novel blood biomarkers for heart failure mortality: mechanistic pathways, aging biology, and risk stratification.Frontiers in cardiovascular medicine · 2026Review
- Dynamic functional connectivity and transcriptomic signatures reveal stage-dependent brain network dysfunction in Alzheimer's disease spectrum.Alzheimer's research & therapy · 2025Article
- Facilities in Molecular Biomarkers in Cardiology.Biomolecules · 2024Article
- Renal Assessment in Acute Cardiorenal Syndrome.Biomolecules · 2023Review
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute heart failure (AHF) is a common and severe condition with a poor prognosis. Its course is often complicated by worsening renal function (WRF), exacerbating the outcome. The population of AHF patients experiencing WRF is heterogenous, and some novel possibilities for its analysis have recently emerged. Clustering is a machine learning (ML) technique that divides the population into distinct subgroups based on the similarity of cases (patients). Given that, we decided to use clustering to find subgroups inside the AHF population that differ in terms of WRF occurrence. We evaluated data from the three hundred and twelve AHF patients hospitalized in our institution who had creatinine assessed four times during hospitalization. Eighty-six variables evaluated at admission were included in the analysis. The k-medoids algorithm was used for clustering, and the quality of the procedure was judged by the Davies-Bouldin index. Three clinically and prognostically different clusters were distinguished. The groups had significantly (
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