Machine Learning Consensus Clustering of Hospitalized Patients with Admission Hyponatremia

Diseases

Pradeep K. Vaitla¹  Pattharawin Pattharanitima²  Panupong Hansrivijit³  Michael A. Mao⁴  Mira T. Keddis⁵  Andrea G. Kattah⁶  Stephen B. Erickson⁶  Charat Thongprayoon⁶  Wisit Cheungpasitporn⁶  John J. Dillon⁶  Vesna D. Garovic⁶  Saraschandra Vallabhajosyula⁷  Tananchai Petnak⁸  Voravech Nissaisorakarn⁹ 

[1] Department of Internal Medicine, Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA;Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 10120, Thailand;Department of Internal Medicine, UPMC Pinnacle, Harrisburg, PA 17105, USA;Department of Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, FL 32224, USA;Department of Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Phoenix, AZ 85054, USA;Department of Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA;Department of Medicine, Section of Cardiovascular Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA;Division of Pulmonary and Pulmonary Critical Care Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand;MetroWest Medical Center, Department of Internal Medicine, Tufts University School of Medicine, Boston, MA 01760, USA;

关键词: artificial intelligence;    hyponatremia;    sodium;    clustering;    machine learning;    mortality;   

DOI  :  10.3390/diseases9030054

来源: DOAJ

【 摘 要 】

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’s 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.

【 授权许可】

Unknown