Research square · 2024
Molecular Phenotyping of Patients with Sepsis and Kidney Injury and Differential Response to Fluid Resuscitation.
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Kiernan Elizabeth, Zelnick Leila R, Khader Ayesha, Coston Taylor D, Bailey Zoie A, Speckmaier Sarah, Lo Jordan, Sathe Neha, Kestenbaum Bryan R, Himmelfarb Jonathan, Johnson Nicholas, Shapiro Nathan, Douglas Ivor S, Hough Catherine, Bhatraju Pavan
Аннотация
PURPOSE: Previous work has identified two AKI sub-phenotypes (SP1 and SP2) characterized by differences in inflammation and endothelial dysfunction. Here we identify these sub-phenotypes using biospecimens collected in the emergency department and test for differential response to restrictive versus liberal fluid strategy in sepsis-induced hypotension in the CLOVERS trial.
METHODS: We applied a previously validated 3-biomarker model using plasma angiopietin-1 and 2, and soluble tumor necrosis factor receptor-1 to classify sub-phenotypes in patients with kidney dysfunction (AKI or end-stage kidney disease [ESKD]). We also compared a de novo latent class analysis (LCA) to the 3-biomarker based sub-phenotypes. Kaplan-Meier estimates were used to test for differences in outcomes and sub-phenotype by treatment interaction.
RESULTS: Among 1289 patients, 846 had kidney dysfunction on enrollment and the 3-variable prediction model identified 605 as SP1 and 241 as SP2. The optimal LCA model identified two sub-phenotypes with high correlation with the 3-biomarker model (Cohen's Kappa 0.8). The risk of 28 and 90-day mortality was greater in SP2 relative to SP1 independent of AKI stage and SOFA scores. Patients with SP2, characterized by more severe endothelial injury and inflammation, had a reduction in 28-day mortality with a restrictive fluid strategy versus a liberal fluid strategy (26% vs 41%), while patients with SP1 had no difference in 28-day mortality (10% vs 11%) (p-value-for-interaction = 0.03).
CONCLUSION: Sub-phenotypes can be identified in the emergency department that respond differently to fluid strategy in sepsis. Identification of these sub-phenotypes could inform a precision-guided therapeutic approach for patients with sepsis-induced hypotension and kidney injury.
METHODS: We applied a previously validated 3-biomarker model using plasma angiopietin-1 and 2, and soluble tumor necrosis factor receptor-1 to classify sub-phenotypes in patients with kidney dysfunction (AKI or end-stage kidney disease [ESKD]). We also compared a de novo latent class analysis (LCA) to the 3-biomarker based sub-phenotypes. Kaplan-Meier estimates were used to test for differences in outcomes and sub-phenotype by treatment interaction.
RESULTS: Among 1289 patients, 846 had kidney dysfunction on enrollment and the 3-variable prediction model identified 605 as SP1 and 241 as SP2. The optimal LCA model identified two sub-phenotypes with high correlation with the 3-biomarker model (Cohen's Kappa 0.8). The risk of 28 and 90-day mortality was greater in SP2 relative to SP1 independent of AKI stage and SOFA scores. Patients with SP2, characterized by more severe endothelial injury and inflammation, had a reduction in 28-day mortality with a restrictive fluid strategy versus a liberal fluid strategy (26% vs 41%), while patients with SP1 had no difference in 28-day mortality (10% vs 11%) (p-value-for-interaction = 0.03).
CONCLUSION: Sub-phenotypes can be identified in the emergency department that respond differently to fluid strategy in sepsis. Identification of these sub-phenotypes could inform a precision-guided therapeutic approach for patients with sepsis-induced hypotension and kidney injury.