Identification of patients at risk of Clostridioides difficile infection for enrollment in vaccine clinical trials

Background: Clostridioides difficile infection (CDI) is an important cause of diarrheal disease associated with increasing morbidity and mortality. Efforts to develop a preventive vaccine are ongoing. The goal of this study was to develop an algorithm to identify patients at high risk of CDI for enrollment in a vaccine efficacy trial.

Methods: We conducted a 2-stage retrospective study of patients aged ≥ 50 within the US Department of Veterans Affairs Health system between January 1, 2009 and December 31, 2013. Included patients had at least 1 visit in each of the 2 years prior to the study, with no CDI in the past year. We used multivariable logistic regression with elastic net regularization to identify predictors of CDI in months 2-12 (i.e., days 31 - 365) to allow time for antibodies to develop. Performance was measured using the positive predictive value (PPV) and the area under the curve (AUC).

Results: Elements of the predictive algorithm included age, baseline comorbidity score, acute renal failure, recent infections or high-risk antibiotic use, hemodialysis in the last month, race, and measures of recent healthcare utilization. The final algorithm resulted in an AUC of 0.69 and a PPV of 3.4%.

Conclusions: We developed a predictive algorithm to identify a patient population with increased risk of CDI over the next 2-12 months. Our algorithm can be used prospectively with clinical and administrative data to facilitate the feasibility of conducting efficacy studies in a timely manner in an appropriate population.