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Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
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Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia.

Charat Thongprayoon1, Janina Paula T Sy-Go1, Voravech Nissaisorakarn2

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

Diagnostics (Basel, Switzerland)
|November 27, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning identified distinct patient groups with abnormal magnesium levels, revealing varying mortality risks for hypomagnesemia and hypermagnesemia. These findings highlight different phenotypes of dysmagnesemia in hospitalized patients.

Keywords:
artificial intelligenceclusteringconsensus clusteringdysmagnesemiaelectrolyteshypermagnesemiahypomagnesemiaindividualized medicinemachine learningmagnesiummortalitynephrologypersonalized medicineprecision medicine

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Area of Science:

  • Internal Medicine
  • Biostatistics
  • Medical Informatics

Background:

  • Serum magnesium derangement (dysmagnesemia) is common in hospitalized patients.
  • Understanding distinct patient phenotypes and their associated mortality risks is crucial for effective clinical management.
  • Unsupervised machine learning offers a novel approach to identify these phenotypes.

Purpose of the Study:

  • To classify hospitalized patients with hypomagnesemia and hypermagnesemia into distinct clusters using machine learning.
  • To evaluate the differential mortality risks associated with these identified clusters.

Main Methods:

  • Consensus cluster analysis was applied to demographic, diagnostic, comorbidity, and laboratory data.
  • Analysis was conducted separately for hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia (serum magnesium ≥ 2.4 mg/dL) cohorts.
  • Associations between clusters and hospital/one-year mortality were assessed.

Main Results:

  • In hypomagnesemia (n=13,320), three clusters emerged: Cluster 1 (high comorbidity, low magnesium), Cluster 2 (youngest, low comorbidity, high kidney function), and Cluster 3 (oldest, low kidney function). Clusters 1 and 3 showed higher mortality than Cluster 2.
  • In hypermagnesemia (n=4671), two clusters were identified. Cluster 2 exhibited older age, higher comorbidity, more kidney disease admissions, acute kidney injury, and lower kidney function compared to Cluster 1.
  • Cluster 2 in the hypermagnesemia cohort was associated with increased hospital and one-year mortality.

Conclusions:

  • Machine learning-based cluster analysis successfully identified clinically distinct phenotypes of dysmagnesemia.
  • These phenotypes are associated with significantly different mortality risks in hospitalized patients.
  • Further research is warranted to explore the clinical utility of this machine learning approach in managing dysmagnesemia.