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Subtyping Hyperchloremia among Hospitalized Patients by Machine Learning Consensus Clustering.

Charat Thongprayoon1, Voravech Nissaisorakarn2, Pattharawin Pattharanitima3

  • 1Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.

Medicina (Kaunas, Lithuania)
|September 28, 2021
PubMed
Summary

Hyperchloremia in hospitalized patients can be categorized into three distinct clusters. These patient groups exhibit varying mortality risks, highlighting the need for personalized risk assessment in clinical practice.

Keywords:
artificial intelligencechlorideclusteringhospitalizationhyperchloremiamachine learningmortality

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Area of Science:

  • Nephrology
  • Internal Medicine
  • Data Science

Background:

  • Hyperchloremia is linked to adverse outcomes, but mortality risks vary across patient groups.
  • Previous studies have not consistently defined mortality risks associated with hyperchloremia.
  • Understanding distinct patient phenotypes is crucial for accurate risk stratification.

Purpose of the Study:

  • To characterize hospitalized patients with hyperchloremia at admission using unsupervised machine learning.
  • To identify distinct patient clusters based on clinical and laboratory data.
  • To evaluate the association between these hyperchloremia clusters and mortality risk.

Main Methods:

  • Consensus cluster analysis of 11,394 hospitalized adult patients with hyperchloremia (serum chloride >108 mEq/L).
  • Utilized demographic data, principal diagnoses, comorbidities, and laboratory values.
  • Assessed standardized mean differences to define cluster characteristics and evaluated mortality associations.

Main Results:

  • Identified three distinct hyperchloremia clusters (28%, 36%, 36% of patients).
  • Clusters differed significantly in age, comorbidities, serum electrolytes, hemoglobin, albumin, and estimated glomerular filtration rate (eGFR).
  • Clusters 1 and 3 showed significantly higher hospital and one-year mortality odds/hazard ratios compared to Cluster 2.

Conclusions:

  • Machine learning identified three clinically distinct phenotypes of hyperchloremia in hospitalized patients.
  • These phenotypes are associated with significantly different mortality risks.
  • The findings support tailored risk assessment and management strategies for hyperchloremic patients.