Talk to the Veterans Crisis Line now
U.S. flag
An official website of the United States government

VA Health Systems Research

Go to the VA ORD website
Go to the QUERI website

HSR&D Citation Abstract

Search | Search by Center | Search by Source | Keywords in Title

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

Stevens VW, Russo EM, Young-Xu Y, Leecaster M, Zhang Y, Zhang C, Yu H, Cai B, Gonzalez EN, Gerding DN, Lawrence J, Samore MH. Identification of patients at risk of Clostridioides difficile infection for enrollment in vaccine clinical trials. Vaccine. 2021 Jan 15; 39(3):536-544.

Dimensions for VA is a web-based tool available to VA staff that enables detailed searches of published research and research projects.

If you have VA-Intranet access, click here for more information vaww.hsrd.research.va.gov/dimensions/

VA staff not currently on the VA network can access Dimensions by registering for an account using their VA email address.
   Search Dimensions for VA for this citation
* Don't have VA-internal network access or a VA email address? Try searching the free-to-the-public version of Dimensions



Abstract:

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.





Questions about the HSR website? Email the Web Team

Any health information on this website is strictly for informational purposes and is not intended as medical advice. It should not be used to diagnose or treat any condition.