Novel Biomarkers, Including tcdB PCR Cycle Threshold, for Predicting Recurrent Clostridioides difficile Infection

Traditional clinical models for predicting recurrent Clostridioides difficile infection do not perform well, likely owing to the complex host-pathogen interactions involved. Accurate risk stratification using novel biomarkers could help prevent recurrence by improving underutilization of effective therapies (i.e., fecal transplant, fidaxomicin, bezlotoxumab). We used a biorepository of 257 hospitalized patients with 24 features collected at diagnosis, including 17 plasma cytokines, total/neutralizing anti-toxin B IgG, stool toxins, and PCR cycle threshold (C) (a proxy for stool organism burden). The best set of predictors for recurrent infection was selected by Bayesian model averaging for inclusion in a final Bayesian logistic regression model. We then used a large PCR-only data set to confirm the finding that PCR C predicts recurrence-free survival using Cox proportional hazards regression. The top model-averaged features were (probabilities of >0.05, greatest to least): interleukin 6 (IL-6), PCR C, endothelial growth factor, IL-8, eotaxin, IL-10, hepatocyte growth factor, and IL-4. The accuracy of the final model was 0.88. Among 1,660 cases with PCR-only data, cycle threshold was significantly associated with recurrence-free survival (hazard ratio, 0.95; P < 0.005). Certain biomarkers associated with C. difficile infection severity were especially important for predicting recurrence; PCR C and markers of type 2 immunity (endothelial growth factor [EGF], eotaxin) emerged as positive predictors of recurrence, while type 17 immune markers (IL-6, IL-8) were negative predictors. In addition to novel serum biomarkers (particularly, IL-6, EGF, and IL-8), the readily available PCR C may be critical to augment underperforming clinical models for C. difficile recurrence.