Unsupervised Clustering Identifies Sub-Phenotypes and Reveals Pre-Dialysis Hyperlactatemia as a Novel Outcome Predictor in Dialysis-Requiring Sepsis-Associated Acute Kidney Injury

Abstract BackgroundHeterogeneity exists in sepsis-associated acute kidney injury (SA-AKI). This prospective observational cohort study aimed to perform consensus cluster analysis and investigate the clinical relevance of identified sub-phenotypes of critically ill patients with dialysis-requiring SA-AKI.MethodsAll septic patients with dialysis-requiring SA-AKI, defined by the Sepsis-3 and Kidney Disease: Improving Global Outcomes AKI criteria, admitted to an intensive care unit in Taiwan between 2002 and 2018 were included. We employed unsupervised consensus clustering based on 22 clinical variables upon initialising renal replacement therapy. They were observed until death or 90 days after hospital discharge. The outcomes were mortality and being free of dialysis.ResultsIn total, 1,397 patients were enrolled (mean age of 63.8 ± 16.38 years and 69.7% were men). After a median follow-up period of 31 (interquartile range 8-123) days, all-cause mortality occurred in 911 patients (65.12%). Moreover, 133 (9.51%) survivors were dialysis dependent, where 355 (25.38%) survivors were free of dialysis. Unsupervised consensus clustering identified three sub-phenotypes associated with significantly different risks of mortality and being free of dialysis. This strategy led us to reveal that the pre-dialysis hyperlactatemia of ≥ 3.1 mmol/L was an independent predictor of mortality and being free of dialysis according to the competing risk modeling. Our results were validated in an independent multi-center AKI cohort.ConclusionsBy the data-driven clustering analysis, we identified sub-phenotypes in septic patients with dialysis-requiring SA-AKI and revealed pre-dialysis hyperlactatemia as a novel outcome predictor. This result represents a step towards precision medicine for septic patients.