{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:14:12Z","timestamp":1760235252618,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T00:00:00Z","timestamp":1627776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diseases"],"abstract":"<jats:p>Background: The objective of this study was to characterize patients with hyponatremia at hospital admission into clusters using an unsupervised machine learning approach, and to evaluate the short- and long-term mortality risk among these distinct clusters. Methods: We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 11,099 hospitalized adult hyponatremia patients with an admission serum sodium below 135 mEq\/L. The standardized mean difference was utilized to identify each cluster\u2019s key features. We assessed the association of each hyponatremia cluster with hospital and one-year mortality using logistic and Cox proportional hazard analysis, respectively. Results: There were three distinct clusters of hyponatremia patients: 2033 (18%) in cluster 1, 3064 (28%) in cluster 2, and 6002 (54%) in cluster 3. Among these three distinct clusters, clusters 3 patients were the youngest, had lowest comorbidity burden, and highest kidney function. Cluster 1 patients were more likely to be admitted for genitourinary disease, and have diabetes and end-stage kidney disease. Cluster 1 patients had the lowest kidney function, serum bicarbonate, and hemoglobin, but highest serum potassium and prevalence of acute kidney injury. In contrast, cluster 2 patients were the oldest and were more likely to be admitted for respiratory disease, have coronary artery disease, congestive heart failure, stroke, and chronic obstructive pulmonary disease. Cluster 2 patients had lowest serum sodium and serum chloride, but highest serum bicarbonate. Cluster 1 patients had the highest hospital mortality and one-year mortality, followed by cluster 2 and cluster 3, respectively. Conclusion: We identified three clinically distinct phenotypes with differing mortality risks in a heterogeneous cohort of hospitalized hyponatremic patients using an unsupervised machine learning approach.<\/jats:p>","DOI":"10.3390\/diseases9030054","type":"journal-article","created":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T21:46:44Z","timestamp":1627854404000},"page":"54","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Machine Learning Consensus Clustering of Hospitalized Patients with Admission Hyponatremia"],"prefix":"10.3390","volume":"9","author":[{"given":"Charat","family":"Thongprayoon","sequence":"first","affiliation":[{"name":"Department of Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5041-4290","authenticated-orcid":false,"given":"Panupong","family":"Hansrivijit","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, UPMC Pinnacle, Harrisburg, PA 17105, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1814-7003","authenticated-orcid":false,"given":"Michael A.","family":"Mao","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5234-6722","authenticated-orcid":false,"given":"Pradeep K.","family":"Vaitla","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea G.","family":"Kattah","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6010-0033","authenticated-orcid":false,"given":"Pattharawin","family":"Pattharanitima","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 10120, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1631-8238","authenticated-orcid":false,"given":"Saraschandra","family":"Vallabhajosyula","sequence":"additional","affiliation":[{"name":"Department of Medicine, Section of Cardiovascular Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, 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Hypertension, Mayo Clinic, Phoenix, AZ 85054, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen B.","family":"Erickson","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John J.","family":"Dillon","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vesna D.","family":"Garovic","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, 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