Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia

Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. Methods: Consensus cluster analysis was performed ba...

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Vydáno v:Diagnostics (Basel) Ročník 11; číslo 11; s. 2119
Hlavní autoři: Thongprayoon, Charat, Sy-Go, Janina Paula T., Nissaisorakarn, Voravech, Dumancas, Carissa Y., Keddis, Mira T., Kattah, Andrea G., Pattharanitima, Pattharawin, Vallabhajosyula, Saraschandra, Mao, Michael A., Qureshi, Fawad, Garovic, Vesna D., Dillon, John J., Erickson, Stephen B., Cheungpasitporn, Wisit
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 15.11.2021
MDPI
Témata:
Age
Algorithms
Artificial intelligence
Cardiovascular disease
Clinical outcomes
Cluster analysis
clustering
Comorbidity
consensus clustering
Diabetes
dysmagnesemia
electrolytes
Hospitalization
Hospitals
Hypertension
hypomagnesemia
Laboratories
Machine learning
Mortality
Patients
Signal transduction
Variables
United States > US
ISSN:2075-4418, 2075-4418
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Shrnutí:Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. Methods: Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster’s key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed. Results: In hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (n = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality. Conclusion: Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia.
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ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics11112119