Cytokine measurements add value to clinical variables in predicting outcomes for Staphylococcus aureus bacteremia

Abstract

Background: We demonstrated that an early dysregulated cytokine response [high interleukin-10 to tissue necrosis factor (IL-10/TNF) ratio] predicted poor outcomes in patients with Staphylococcus aureus bacteremia (SAB). However, high interpatient variability in cytokine levels were observed. We grouped cytokine measurements in quartiles and assessed their additive value to clinical variables for predicting bacterial persistence and 30-day mortality in patients with SAB.

Methods: A multicenter observational study was conducted in hospitalized patients with SAB. Medical charts were reviewed for relevant information. Blood samples were obtained for cytokine measurements by ELISA: interferon-gamma (IFNγ), interleukin (IL-1β, IL-6, IL-8, IL-10, IL-17) and tissue necrosis factor (TNF). Cytokine measurements were grouped into quartiles. Significant predictors for bacterial persistence and 30-day mortality were determined by multivariable logistic regression analysis. Area under the ROC curve (AUC) analysis was performed and predictive performance was compared between models with and without cytokine quartiles.

Results: Among 606 patients with SAB, a subset of patients (n = 239) had Day 1 cytokine measurements and clinical data collected; of those, 53 (22%) had persistent bacteremia. Accounting for septic shock, the addition of either IL-10 (AUC 0.708) or TNF (AUC 0.714) quartiles measured on Day 1 improved model performance for predicting bacterial persistence. All patients had Day 4 cytokine measurements; 52 patients (8.5%) died within 30-days of SAB onset. Inclusion of either IL-10 (AUC 0.873) or TNF (AUC 0.879) quartiles, but not both, measured on Day 4 to the significant clinical predictors (coronary artery disease, Pitt bacteremia score ≥ 4, and septic shock) improved model performance for mortality.

Conclusions: IL-10 or TNF levels falling within the range in the upper quartiles, when combined with clinical variables, improved model performance for predicting outcomes, and may potentially be used to support aggressive management and biomarker-guided studies to evaluate the benefit of adjunctive immunotherapy for SAB in the future.

Fig. 1

Distribution of Patients across Cytokine Quartiles at Baseline (Day 1) Comparing Resolving vs Persistent SAB Groups. Overall differences between groups (persistent, red vs resolving, blue) was calculated by chi-square analyses for TNF (n = 53 vs n = 188), IL-10 (n = 52 vs n = 178), IL-6 (n = 37 vs n = 147), IL-8 (n = 38 vs n = 148), IFNγ (n = 25 vs n = 89), IL-1β (n = 25 vs n = 77), IL-17 (n = 50 vs n = 154). Then, Bonferroni correction was made for multiple pairwise comparisons, with a threshold for significance of p < 0.008 (0.05/6). Significant p-values are listed in the figure. Note, P ≤ 0.001 is denoted by ***, and P ≤ 0.0001 is denoted by ****

Fig. 2

Distribution of Patients across Cytokine Quartiles on Day 4 of bacteremia Comparing Survivors vs Non-survivors. Overall differences between groups (non-survivor, red vs survivor, blue) was calculated by chi-square analyses for TNF (n = 52 vs n = 554), IL-10 (n = 51 vs n = 504), IL-6 (n = 43 vs n = 494), IL-8 (n = 44 vs n = 490), IFNγ (n = 33 vs n = 394), IL-1β (n = 34 vs n = 369), IL-17 (n = 21 vs n = 174). Then, Bonferroni correction was made for multiple pairwise comparisons, with a threshold for significance of p < 0.008 (0.05/6). Significant p-values are listed in the figure. Note, P ≤ 0.01 is denoted by **, P ≤ 0.001 is denoted by ***, and P ≤ 0.0001 is denoted by ****