Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia

Abstract

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.

Incidence of Dysmagnesemia among Medically Hospitalized Patients and Associated Clinical Characteristics: A Prospective Cohort Study

“…Previous studies reported the incidence of hypomagnesemia among hospitalized patients ranged between 11.0-20.0% [ 6 , 7 ], and it was increased to 65.0% in critically ill patients [ 6 , 9 ]. The observed discrepancies in reported incidences may be attributed to variations in the selection of cutoff values and the diverse clinical characteristics exhibited by the patients.…”

“…The normal reference range for total Mg concentration is between 0.7 and 1.0 mmol/L [ 5 ]. Mg disorders are widespread in hospitalized patients, particularly hypomagnesemia, which occurs in 10–24% of them [ 6 , 7 ]. Hypomagnesemia is more common (65%) in patients admitted to intensive care units [ 8 , 9 ].…”

Identification of Distinct Clinical Phenotypes of Cardiogenic Shock Using Machine Learning Consensus Clustering Approach

“…We determined the clusters' key features using an absolute standardized mean difference (SMD) of > 0.3 in reference to Thongprayoon's studies [22][23][24]. We then compared outcomes among the identi ed clusters.…”

Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering

“…Consensus matrix heat map (k = 10) depicting consensus values on a white to blue color scale of each cluster. References [52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67] are cited in the supplementary materials. Data Availability Statement: Data will be made available by the authors upon reasonable request.…”