A Two Biomarker Model Augments Clinical Prediction of Mortality in Melioidosis.

BACKGROUND:Melioidosis, infection caused by the bacterium Burkholderia pseudomallei, is a common cause of sepsis with high associated mortality in Southeast Asia. Identification of patients at high-likelihood of clinical deterioration is important for guiding decisions about resource allocation and management. We sought to develop a biomarker-based model for 28-day mortality prediction in melioidosis. METHODS:In a derivation set (N=113) of prospectively enrolled, hospitalized Thai patients with melioidosis, we measured concentrations of interferon-γ, interleukin-1β, interleukin-6, interleukin-8, interleukin-10, tumor necrosis factor-α, granulocyte-colony stimulating factor and interleukin-17A. We used least absolute shrinkage and selection operator (LASSO) regression to identify a subset of predictive biomarkers and performed logistic regression and receiver operating characteristic curve analysis to evaluate biomarker-based prediction of 28-day mortality compared to clinical variables. We repeated select analyses in an internal validation set (N=78) and in a prospectively enrolled external validation set (N=161) of hospitalized adults with melioidosis in Thailand. RESULTS:All eight cytokines were positively associated with 28-day mortality. Of these, interleukin-6 and interleukin-8 were selected by LASSO regression. A model consisting of interleukin-6, interleukin-8 and clinical variables significantly improved 28-day mortality prediction over a model of only clinical variables (AUC 0.86, 95% confidence interval (CI) 0.79-0.92 vs AUC 0.78, 95% CI 0.69-0.87; P=0.01). In both the internal validation set (AUC 0.91, 95% CI 0.84-0.97) and the external validation set (AUC 0.81, 95% CI 0.74-0.88), the combined model including biomarkers significantly improved 28-day mortality prediction over a model limited to clinical variables. CONCLUSIONS:A two biomarker model augments clinical prediction of 28-day mortality in melioidosis.