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 (CT) (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 CT 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 CT, 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 CT 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 CT may be critical to augment underperforming clinical models for C. difficile recurrence.
Keywords: medical outcomes; outcome; prediction model; recurrent Clostridioides difficile infection; risk model.