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

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. Graphic abstract