The Journal of infectious diseases · 2026
Host Immune Biosignatures Predict Septic Shock and Mortality in Solid Organ Transplant Recipients With Gram-Negative Bacteremia.
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Eichenberger Emily M, Ruffin Felicia, Karadkhele Geeta M, Larsen Christian P, Fowler Vance G
Аннотация
BACKGROUND: The aim of this study was to determine whether the addition of immunologic biosignatures to a predictive model based solely on clinical data could improve the model's ability to predict patient outcome among solid organ transplant (SOT) recipients with gram-negative bacteremia (GNB).
METHODS: Plasma was prospectively collected from SOT recipients with GNB. Twenty-seven cytokines, chemokines, and immunoglobulin levels were quantified. Two logistic regression models were specified: a clinical model including age, transplanted organ, and time since transplant and a clinical-immunologic model that additionally incorporated the first two principal components. Model performance was evaluated using area under the curve (AUC). Internal validation was performed by repeated 10-fold cross-validation. AUCs were compared with a paired t test.
RESULTS: Of 169 SOT recipients with GNB, 45 (26.6%) experienced an adverse outcome (septic shock or death). When compared with the clinical model alone, the clinical-immunologic model demonstrated superior discrimination (AUC, 0.70 vs 0.84; P < .001). Principal component 1, characterized by broad immune dysregulation, was associated with increased risk for adverse outcome (odds ratio, 1.43; 95% CI, 1.24-1.69) and was characterized by strong contributions from innate proinflammatory mediators (TNF-α, IL-1β, IL-6, IL-8, MCP-1, MIP-1β, IL12p70, MIP-1α, and GMCSF), anti-inflammatory mediators (IL-10, IL-4, and IL-13), and IL-2. In contrast, principal component 2 was associated with a favorable outcome (odds ratio, 0.61; 95% CI, .43-.84) and reflected coordinated adaptive immunity, with dominant contributions from Th1, Th17, and humoral pathways (TNFβ, IFNg, IP-10, IL-17A, IgG, IgM).
CONCLUSIONS: Immune biosignatures significantly improved prediction of adverse outcomes beyond clinical factors in SOT recipients with GNB.
METHODS: Plasma was prospectively collected from SOT recipients with GNB. Twenty-seven cytokines, chemokines, and immunoglobulin levels were quantified. Two logistic regression models were specified: a clinical model including age, transplanted organ, and time since transplant and a clinical-immunologic model that additionally incorporated the first two principal components. Model performance was evaluated using area under the curve (AUC). Internal validation was performed by repeated 10-fold cross-validation. AUCs were compared with a paired t test.
RESULTS: Of 169 SOT recipients with GNB, 45 (26.6%) experienced an adverse outcome (septic shock or death). When compared with the clinical model alone, the clinical-immunologic model demonstrated superior discrimination (AUC, 0.70 vs 0.84; P < .001). Principal component 1, characterized by broad immune dysregulation, was associated with increased risk for adverse outcome (odds ratio, 1.43; 95% CI, 1.24-1.69) and was characterized by strong contributions from innate proinflammatory mediators (TNF-α, IL-1β, IL-6, IL-8, MCP-1, MIP-1β, IL12p70, MIP-1α, and GMCSF), anti-inflammatory mediators (IL-10, IL-4, and IL-13), and IL-2. In contrast, principal component 2 was associated with a favorable outcome (odds ratio, 0.61; 95% CI, .43-.84) and reflected coordinated adaptive immunity, with dominant contributions from Th1, Th17, and humoral pathways (TNFβ, IFNg, IP-10, IL-17A, IgG, IgM).
CONCLUSIONS: Immune biosignatures significantly improved prediction of adverse outcomes beyond clinical factors in SOT recipients with GNB.