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Hypernatremia subgroups among hospitalized patients by machine learning consensus clustering with different patient survival

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Abstract

Background

The objective of this study was to characterize hypernatremia patients at hospital admission into clusters using an unsupervised machine learning approach and to evaluate the mortality risk among these distinct clusters.

Methods

We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 922 hospitalized adult patients with admission serum sodium of > 145 mEq/L. We calculated the standardized difference of each variable to identify each cluster’s key features. We assessed the association of each hypernatremia cluster with hospital and 1-year mortality.

Results

There were three distinct clusters of patients with hypernatremia on admission: 318 (34%) patients in cluster 1, 339 (37%) patients in cluster 2, and 265 (29%) patients in cluster 3. Cluster 1 consisted of more critically ill patients with more severe hypernatremia and hypokalemic hyperchloremic metabolic acidosis. Cluster 2 consisted of older patients with more comorbidity burden, body mass index, and metabolic alkalosis. Cluster 3 consisted of younger patients with less comorbidity burden, higher baseline eGFR, hemoglobin, and serum albumin. Compared to cluster 3, odds ratios for hospital mortality were 15.74 (95% CI 3.75–66.18) for cluster 1, and 6.51 (95% CI 1.48–28.59) for cluster 2, whereas hazard ratios for 1-year mortality were 6.25 (95% CI 3.69–11.46) for cluster 1 and 4.66 (95% CI 2.73–8.59) for cluster 2.

Conclusion

Our cluster analysis identified three clinically distinct phenotypes with differing mortality risk in patients hospitalized with hypernatremia.

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Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

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

    Charat Thongprayoon, Andrea G. Kattah, Grace Y. Chong, Arvind K. Garg, Stephen B. Erickson, John J. Dillon, Vesna D. Garovic & Wisit Cheungpasitporn

  2. Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Jacksonville, FL, USA

    Michael A. Mao

  3. Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Phoenix, AZ, USA

    Mira T. Keddis

  4. Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani, Thailand

    Pattharawin Pattharanitima

  5. Department of Internal Medicine, MetroWest Medical Center, Tufts University School of Medicine, Boston, MA, USA

    Voravech Nissaisorakarn

Authors
  1. Charat Thongprayoon
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  2. Michael A. Mao
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  3. Mira T. Keddis
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  4. Andrea G. Kattah
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  7. Voravech Nissaisorakarn
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  8. Arvind K. Garg
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  9. Stephen B. Erickson
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  10. John J. Dillon
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  11. Vesna D. Garovic
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  12. Wisit Cheungpasitporn
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All authors have read and agreed to the published version of the manuscript.

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Correspondence to Wisit Cheungpasitporn.

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The study was approved by the Institutional Review Board of Mayo Clinic (IRB number 21-003088; date of approval: 3/30/2021).

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Thongprayoon, C., Mao, M.A., Keddis, M.T. et al. Hypernatremia subgroups among hospitalized patients by machine learning consensus clustering with different patient survival. J Nephrol 35, 921–929 (2022). https://doi.org/10.1007/s40620-021-01163-2

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  • DOI: https://doi.org/10.1007/s40620-021-01163-2

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