Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering

Medical Sciences

Pattharawin Pattharanitima¹  Voravech Nissaisorakarn²  Michael A. Mao³  Mira T. Keddis⁴  Fawad Qureshi⁵  Kianoush B. Kashani⁵  Andrea G. Kattah⁵  Charat Thongprayoon⁵  Wisit Cheungpasitporn⁵  John J. Dillon⁵  Jose L. Zabala Genovez⁵  Vesna D. Garovic⁵  Pradeep Vaitla⁶  Saraschandra Vallabhajosyula⁷ 

[1] Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12121, Thailand;Department of Internal Medicine, MetroWest Medical Center, Tufts University School of Medicine, Boston, MA 01702, USA;Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA;Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Phoenix, AZ 85054, USA;Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA;Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA;Section of Cardiovascular Medicine, Department of Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA;

关键词: acute kidney injury;    AKI;    clustering;    machine learning;    nephrology;    artificial intelligence;   

DOI  :  10.3390/medsci9040060

来源: DOAJ

【 摘 要 】

Background: We aimed to cluster patients with acute kidney injury at hospital admission into clinically distinct subtypes using an unsupervised machine learning approach and assess the mortality risk among the distinct clusters. Methods: We performed consensus clustering analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 4289 hospitalized adult patients with acute kidney injury at admission. The standardized difference of each variable was calculated to identify each cluster’s key features. We assessed the association of each acute kidney injury cluster with hospital and one-year mortality. Results: Consensus clustering analysis identified four distinct clusters. There were 1201 (28%) patients in cluster 1, 1396 (33%) patients in cluster 2, 1191 (28%) patients in cluster 3, and 501 (12%) patients in cluster 4. Cluster 1 patients were the youngest and had the least comorbidities. Cluster 2 and cluster 3 patients were older and had lower baseline kidney function. Cluster 2 patients had lower serum bicarbonate, strong ion difference, and hemoglobin, but higher serum chloride, whereas cluster 3 patients had lower serum chloride but higher serum bicarbonate and strong ion difference. Cluster 4 patients were younger and more likely to be admitted for genitourinary disease and infectious disease but less likely to be admitted for cardiovascular disease. Cluster 4 patients also had more severe acute kidney injury, lower serum sodium, serum chloride, and serum bicarbonate, but higher serum potassium and anion gap. Cluster 2, 3, and 4 patients had significantly higher hospital and one-year mortality than cluster 1 patients (p < 0.001). Conclusion: Our study demonstrated using machine learning consensus clustering analysis to characterize a heterogeneous cohort of patients with acute kidney injury on hospital admission into four clinically distinct clusters with different associated mortality risks.

【 授权许可】

Unknown