A Systematic Literature Review on Risk Factors for and Timing of Clostridioides difficile Infection in the United States.

Abstract

Clostridioides difficile infection (CDI) is a major public health threat. Up to 40% of patients with CDI experience recurrent CDI (rCDI), which is associated with increased morbidity. This study aimed to define an at-risk population by obtaining a detailed understanding of the different factors leading to CDI, rCDI, and CDI-related morbidity and of time to CDI. We conducted a systematic literature review (SLR) of MEDLINE (using PubMed) and EMBASE for relevant articles published between January1, 2016, and November11, 2022, covering the US population. Of the 1324 articles identified, 151 met prespecified inclusion criteria. Advanced patient age was a likely risk factor for primary CDI within a general population, with significant risk estimates identified in nine of 10 studies. Older age was less important in specific populations with comorbidities usually diagnosed at earlier age, such as bowel disease and cancer. In terms of comorbidities, the established factors of infection, kidney disease, liver disease, cardiovascular disease, and bowel disease along with several new factors (including anemia, fluid and electrolyte disorders, and coagulation disorders) were likely risk factors for primary CDI. Data on diabetes, cancer, and obesity were mixed. Other primary CDI risk factors were antibiotics, proton pump inhibitors, female sex, prior hospitalization, and the length of stay in hospital. Similar factors were identified for rCDI, but evidence was limited. Older age was a likely risk factor for mortality. Timing of primary CDI varied depending on the population: 2-3weeks in patients receiving stem cell transplants, within 3weeks for patients undergoing surgery, and generally more than 3weeks following solid organ transplant. This SLR uses recent evidence to define the most important factors associated with CDI, confirming those that are well established and highlighting new ones that could help to identify patient populations at high risk.