Subtyping hospitalized patients with hypokalemia by machine learning consensus clustering and associated mortality risks

Charat Thongprayoon, Michael A. Mao, Andrea G. Kattah, Mira T. Keddis, Pattharawin Pattharanitima, Stephen B. Erickson, John J. Dillon, Vesna D. Garovic, Wisit Cheungpasitporn

Research output: Contribution to journalArticlepeer-review


Background: Hospitalized patients with hypokalemia are heterogeneous and cluster analysis, an unsupervised machine learning methodology, may discover more precise and specific homogeneous groups within this population of interest. Our study aimed to cluster patients with hypokalemia at hospital admission using an unsupervised machine learning approach and assess the mortality risk among these distinct clusters. Methods: We performed consensus clustering analysis based on demographic information, principal diagnoses, comorbidities and laboratory data among 4763 hospitalized adult patients with admission serum potassium ≤3.5 mEq/L. We calculated the standardized mean difference of each variable and used the cutoff of ±0.3 to identify each cluster's key features. We assessed the association of the hypokalemia cluster with hospital and 1-year mortality. Results: Consensus cluster analysis identified three distinct clusters that best represented patients' baseline characteristics. Cluster 1 had 1150 (32%) patients, cluster 2 had 1344 (28%) patients and cluster 3 had 1909 (40%) patients. Based on the standardized difference, patients in cluster 1 were younger, had less comorbidity burden but higher estimated glomerular filtration rate (eGFR) and higher hemoglobin; patients in cluster 2 were older, more likely to be admitted for cardiovascular disease and had higher serum sodium and chloride levels but lower eGFR, serum bicarbonate, strong ion difference (SID) and hemoglobin, while patients in cluster 3 were older, had a greater comorbidity burden, higher serum bicarbonate and SID but lower serum sodium, chloride and eGFR. Compared with cluster 1, cluster 2 had both higher hospital and 1-year mortality, whereas cluster 3 had higher 1-year mortality but comparable hospital mortality. Conclusion: Our study demonstrated the use of consensus clustering analysis in the heterogeneous cohort of hospitalized hypokalemic patients to characterize their patterns of baseline clinical and laboratory data into three clinically distinct clusters with different mortality risks.

Original languageEnglish (US)
Pages (from-to)253-261
Number of pages9
JournalClinical Kidney Journal
Issue number2
StatePublished - Feb 1 2022


  • artificial intelligence
  • clustering
  • electrolytes
  • hypokalemia
  • machine learning
  • potassium

ASJC Scopus subject areas

  • Nephrology
  • Transplantation


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