Journal
CLINICAL KIDNEY JOURNAL
Volume 15, Issue 2, Pages 328-337Publisher
OXFORD UNIV PRESS
DOI: 10.1093/ckj/sfab207
Keywords
chronic kidney disease; dialysis; iron; machine learning; mortality; phosphate
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This study investigated the prognosis of dialysis patients by clustering them based on phosphate, hemoglobin, and iron-related markers. The use of phosphate binders was found to be associated with lower risk of all-cause death in two clusters, highlighting the potential benefits of oral phosphate binder therapy in certain patient populations.
Background Hyperphosphatemia in patients undergoing dialysis is common and is associated with mortality. Recently, the link between phosphate metabolism and iron dynamics has received increasing attention. However, the association between this relationship and prognosis remains largely unexplored. Methods We conducted an observational study of patients who initiated dialysis in the 17 centers participating in the Aichi Cohort Study of the Prognosis in Patients Newly Initiated into Dialysis. Data were available on sex, age, use of phosphate binder, drug history, medical history and laboratory data. After excluding patients with missing values of phosphate, hemoglobin, ferritin and transferrin saturation, we used the Gaussian mixture model to divide the cohort into clusters based on phosphate, hemoglobin, logarithmic ferritin and transferrin saturation. We investigated the prognosis of patients in these clusters. The primary outcome was all-cause death. In each cluster, the prognostic impact of phosphate binder was also studied. Results The study included 1175 patients with chronic kidney disease who initiated dialysis between October 2011 and September 2013. Among them, 785 were men and 390 were women, with a mean +/- SD age of 67.9 +/- 13.0 years. The patients were divided into three clusters, and mortality was higher in cluster c than in cluster a (P = 0.005). Moreover, the use of phosphate binders was associated with a lower risk of all-cause death in two clusters (a and c) that were characterized by older age and higher prevalence of diabetes mellitus, among other things. Conclusions We used an unsupervised machine learning method to cluster patients, using phosphate, hemoglobin and iron-related markers. In two of the clusters, the oral use of a phosphate binder might improve prognosis.
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