Distinct Subtyping of Successful Weaning from Acute Kidney Injury Requiring Renal Replacement Therapy by Consensus Clustering in Critically Ill Patients

Abstract

Background: Clinical decisions regarding the appropriate timing of weaning off renal replacement therapy (RRT) in critically ill patients are complex and multifactorial. The aim of the current study was to identify which critical patients with acute kidney injury (AKI) may be more likely to be successfully weaned off RRT using consensus cluster analysis. Methods: In this study, critically ill patients who received RRT at three multicenter referral hospitals at several timepoints from August 2016 to July 2018 were enrolled. An unsupervised consensus clustering algorithm was used to identify distinct phenotypes. The outcomes of interest were the ability to wean off RTT and 90-day mortality. Results: A total of 124 patients with AKI requiring RRT (AKI-RRT) were enrolled. The 90-day mortality rate was 30.7% (38/124), and 49.2% (61/124) of the patients were successfully weaned off RRT for over 90 days. The consensus clustering algorithm identified three clusters from a total of 45 features. The three clusters had distinct features and could be separated according to the combination of urinary neutrophil gelatinase-associated lipocalin to creatinine ratio (uNGAL/Cr), Sequential Organ Failure Assessment (SOFA) score, and estimated glomerular filtration rate at the time of weaning off RRT. uNGAL/Cr (hazard ratio [HR] 2.43, 95% confidence interval [CI]: 1.36–4.33) and clustering phenotype (cluster 1 vs. 3, HR 2.7, 95% CI: 1.11–6.57; cluster 2 vs. 3, HR 44.5, 95% CI: 11.92–166.39) could predict 90-day mortality or re-dialysis. Conclusions: Almost half of the critical patients with AKI-RRT could wean off dialysis for over 90 days. Urinary NGAL/Cr and distinct clustering phenotypes could predict 90-day mortality or re-dialysis.

Long-Term Outcome of Leptospirosis Infection with Acute Kidney Injury

“…More recently, the impact of Leptospira on CKD was extended to the understanding of the cause of CKD of unknown etiology. The asymptomatic carrier, or debris from Leptospira retained in infected kidneys, might be one of the mechanisms for chronic inflammation that lead to CKD [ 31 , 32 , 33 , 34 ].…”

A new approach to predicting mortality in dialysis patients using sociodemographic features based on artificial intelligence

“…Identifying and characterizing high-risk clusters in a heterogeneous ICU population [13] MCA, Hierarchical clustering and K-Means Identifying lupus patient profiles [14] K-Medoids clustering and Kaplan-Meier survival analysis with Cox regression Effects on urticaria remission [15] Consensus clustering analysis, Kaplan-Meier curves and Cox proportional hazard models Identifying novel chronic kidney disease subgroups that best represent the data pattern [16] K-Means, Kaplan-Meier and log-rank test Mortality in patients with sepsis [17] K-means clustering and Bootstrapping Phenotyping of very old patients on admission to ICU [18] Unsupervised consensus clustering Identifying distinct phenotypes of patients with acute kidney injury requiring renal replacement therapy…”

“…Some examples of clustering technique applications in the context of survival analysis are listed below, in Table 1. In [18], the authors use the unsupervised consensus clustering algorithm to identify distinct phenotypes in a sample of 124 patients with acute kidney injury (AKI) who required RRT. The outcomes of interest were the ability to be weaned off RTT and 90-day mortality.…”