Machine Learning Approach to Understand Worsening Renal Function in Acute Heart Failure

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 (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.

Artificial Intelligence in Heart Failure and Acute Kidney Injury: Emerging Concepts and Controversial Dimensions

“…Urban et al [35] analyzed 312 patients hospitalized with ADHF to identify predictors of worsening renal function (WRF) through unsupervised machine learning. The study utilized k-Medoid clustering, an unsupervised machine-learning technique that groups similar patients as cluster centers based on actual patient data.…”

“…Cluster 3 encompassed 158 older males with chronic reduced EF HF and a history of coronary artery disease, showing a moderate WRF incidence of 15%. This study by Urban et al [35] showcases the utility of unsupervised machine learning in uncovering distinct AHF phenotypes, each with unique demographic and clinical profiles.…”

Enhancing Disease Diagnosis: Statistical Analysis of Haematological Parameters in Sickle Cell Patients, Integrating Predictive Analytics

“…The authors in this study [16] conveyed how acute heart failure (AHF) is a common and severe condition often complicated by worsening renal function (WRF), worsening the prognosis. They have used clustering, a machine learning technique, on data from 312 AHF patients with 86 variables and identified three distinct patient clusters with significantly different WRF incidences (p = 0.004).…”

Renal Assessment in Acute Cardiorenal Syndrome

“…Noteworthy, novel, artificial intelligence-based approaches to analyze the nature of WRF in HF have emerged. Clustering, a machine-learning technique that distinguishes smaller subgroups in studied populations, revealed different HF patients phenotypes regarding WRF risk and characteristics [23][24][25].…”