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https://www.selleckchem.com/products/tiplaxtinin-pai-039.html Hospitalized patients with hyperkalemia are heterogeneous, and cluster approaches may identify specific homogenous groups. This study aimed to cluster patients with hyperkalemia on admission using unsupervised machine learning consensus clustering approach, and to compare characteristics and outcomes among these distinct clusters. Consensus cluster analysis was performed in 5,133 hospitalized adult patients with admission hyperkalemia, based on available clinical and laboratory data. The standardized mean difference was used to identify each cluster's key clinical features. The association of hyperkalemia clusters with hospital and one-year mortality was assessed using logistic and Cox proportional hazard regression. Three distinct clusters of hyperkalemia patients were identified using consensus cluster analysis 1,661 (32%) in cluster 1, 2,455 (48%) in cluster 2, and 1,017 (20%) in cluster 3. Cluster 1 was mainly characterized by older age, higher serum chloride, and acute kidney injury (AKI), but lower estimated glomerular filtration rate (eGFR), serum bicarbonate and hemoglobin. Cluster 2 was mainly characterized by higher eGFR, serum bicarbonate, and hemoglobin, but lower comorbidity burden, serum potassium, and AKI. Cluster 3 was mainly characterized by higher comorbidity burden, particularly diabetes, and end-stage kidney disease, AKI, serum potassium, anion gap, but lower eGFR, serum sodium, chloride, and bicarbonate. Hospital and one-year mortality risk was significantly different among the three identified clusters, with highest mortality in cluster 3, followed by cluster 1, and then cluster 2. In a heterogeneous cohort of hyperkalemia patients, three distinct clusters were identified using unsupervised machine learning. These three clusters had different clinical characteristics and outcomes. In a heterogeneous cohort of hyperkalemia patients, three distinct clusters were identified using unsupe

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