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 (p = 0.004) different incidences of WRF. Inside the AHF population, we successfully discovered that three groups varied in renal prognosis. Our results provide novel insight into the AHF and WRF interplay and can be valuable for future trial construction and more tailored treatment.

Department of Information and Knowledge Engineering Prague University of Economics and Business W Churchill Sq 1938 4 130 67 Prague Czech Republic

Department of Physiology and Patophysiology Wroclaw Medical University 50 368 Wroclaw Poland

Faculty of Electrical Engineering Warsaw University of Technology 00 614 Warszawa Poland

Institute of Heart Diseases Student Scientific Organization Wroclaw Medical University 50 368 Wroclaw Poland

Institute of Heart Diseases Wroclaw Medical University 50 556 Wroclaw Poland

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