Evaluation of a risk assessment model to predict infection with healthcare facility-onset Clostridioides difficile

Diagnostic prediction models to identify patients at risk for healthcare-facility-onset Clostridioides difficile: A systematic review of methodology and reporting

OBJECTIVE

To systematically review the methodology, performance, and generalizability of diagnostic models for predicting the risk of healthcare-facility-onset (HO) Clostridioides difficile infection (CDI) in adult hospital inpatients (aged ≥18 years).

BACKGROUND

CDI is the most common cause of healthcare-associated diarrhea. Prediction models that identify inpatients at risk of HO-CDI have been published; however, the quality and utility of these models remain uncertain.

METHODS

Two independent reviewers evaluated articles describing the development and/or validation of multivariable HO-CDI diagnostic models in an inpatient setting. All publication dates, languages, and study designs were considered. Model details (eg, sample size and source, outcome, and performance) were extracted from the selected studies based on the CHARMS checklist. The risk of bias was further assessed using PROBAST.

RESULTS

Of the 3,030 records evaluated, 11 were eligible for final analysis, which described 12 diagnostic models. Most studies clearly identified the predictors and outcomes but did not report how missing data were handled. The most frequent predictors across all models were advanced age, receipt of high-risk antibiotics, history of hospitalization, and history of CDI. All studies reported the area under the receiver operating characteristic curve (AUROC) as a measure of discriminatory ability. However, only 3 studies reported the model calibration results, and only 2 studies were externally validated. All of the studies had a high risk of bias.

CONCLUSION

The studies varied in their ability to predict the risk of HO-CDI. Future models will benefit from the validation on a prospective external cohort to maximize external validity.

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

INTRODUCTION

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.

METHODS

We conducted a systematic literature review (SLR) of MEDLINE (using PubMed) and EMBASE for relevant articles published between January 1, 2016, and November 11, 2022, covering the US population.

RESULTS

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: 2-3 weeks in patients receiving stem cell transplants, within 3 weeks for patients undergoing surgery, and generally more than 3 weeks following solid organ transplant.

CONCLUSION

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.

Microbial ecology between Clostridioides difficile and gut microbiota

Clostridioides difficile colonizes a polymicrobial environment in the intestine and is a causative agent for antibiotic-associated diarrhea (AAD) and pseudomembranous colitis (PMC). The most important virulence factors of C. difficile are bacterial toxins, and three toxins (toxin A, toxin B, and binary toxin) are produced by toxigenic strains. Other virulence factors include spores, flagella, capsules, biofilms, hydrolytic enzymes and adhesins. C. difficile infection (CDI) is specifically diagnosed by anaerobic culture and toxin detection by either nucleic acid amplification test (NAAT) or enzyme-linked immunosorbent assay (ELISA). For treatment of CDI, metronidazole, vancomycin and fidaxomicin are used based on the severity of CDI. Mutual interaction between C. difficile and gut microbiota is associated with pathogenesis of CDI, and decreased microbial diversity with altered gut microbiome was detected in CDI patients. Restoration of certain gut microbiota is considered to be potentially effective for the prevention and treatment of CDI, and an ideal goal for CDI patients is restoration of the gut microbiota to a healthy state. Fecal microbiota transplantation (FMT) is a highly successful method of microbiome restoration and has been reported to be effective for the prevention of recurrent CDI. In addition, approaches to restoring the gut microbiota by using probioitcs and live biotherapeutic products (LBPs) are currently being studied to examine the effect on CDI. Further microbial ecological research on C. difficile and gut microbiota could lead to a better understanding of the pathogenesis and treatment of CDI.

Can prediction scores be used to identify patients at risk of Clostridioides difficile infection?

PURPOSE OF REVIEW

To describe the current state of literature on modeling risk of incident and recurrent Clostridioides difficile infection (iCDI and rCDI), to underscore limitations, and to propose a path forward for future research.

RECENT FINDINGS

There are many published risk factors and models for both iCDI and rCDI. The approaches include scores with a limited list of variables designed to be used at the bedside, but more recently have also included automated tools that take advantage of the entire electronic health record. Recent attempts to externally validate scores have met with mixed success.

SUMMARY

For iCDI, the performance largely hinges on the incidence, which even for hospitalized patients can be low (often <1%). Most scores fail to achieve high accuracy and/or are not externally validated. A challenge in predicting rCDI is the significant overlap with risk factors for iCDI, reducing the discriminatory ability of models. Automated electronic health record-based tools show promise but portability to other centers is challenging. Future studies should include external validation and consider biomarkers to augment performance.