Introduction Heterogeneity exists in sepsis-associated acute kidney injury (SA-AKI). This study aimed to perform unsupervised consensus clustering in critically ill patients with dialysis-requiring SA-AKI. Patients and Methods This prospective observational cohort study included all septic patients, defined by the Sepsis-3 criteria, with dialysis-requiring SA-AKI in surgical intensive care units in Taiwan between 2009 and 2018. We employed unsupervised consensus clustering based on 23 clinical variables upon initializing renal replacement therapy. Multivariate-adjusted Cox regression models and Fine–Gray sub-distribution hazard models were built to test associations between cluster memberships with mortality and being free of dialysis at 90 days after hospital discharge, respectively. Results Consensus clustering among 999 enrolled patients identified three sub-phenotypes characterized with distinct clinical manifestations upon renal replacement therapy initiation ( n = 352, 396 and 251 in cluster 1, 2 and 3, respectively). They were followed for a median of 48 (interquartile range 9.5–128.5) days. Phenotypic cluster 1, featured by younger age, lower Charlson Comorbidity Index, higher baseline estimated glomerular filtration rate but with higher severity of acute illness was associated with an increased risk of death (adjusted hazard ratio of 3.05 [95% CI, 2.35–3.97]) and less probability to become free of dialysis (adjusted sub-distribution hazard ratio of 0.55 [95% CI, 0.38–0.8]) than cluster 3. By examining distinct features of the sub-phenotypes, we discovered that pre-dialysis hyperlactatemia ≥3.3 mmol/L was an independent outcome predictor. A clinical model developed to determine high-risk sub-phenotype 1 in this cohort (C-static 0.99) can identify a sub-phenotype with high in-hospital mortality risk (adjusted hazard ratio of 1.48 [95% CI, 1.25–1.74]) in another independent multi-centre SA-AKI cohort. Conclusions Our data-driven approach suggests sub-phenotypes with clinical relevance in dialysis-requiring SA-AKI and serves an outcome predictor. This strategy represents further development toward precision medicine in the definition of high-risk sub-phenotype in patients with SA-AKI. Key messages Unsupervised consensus clustering can identify sub-phenotypes of patients with SA-AKI and provide a risk prediction. Examining the features of patient heterogeneity contributes to the discovery of serum lactate levels ≥ 3.3 mmol/L upon initializing RRT as an independent outcome predictor. This data-driven approach can be useful for prognostication and lead to a better understanding of therapeutic strategies in heterogeneous clinical syndromes.
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.
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