We thank Gonzales-Luna and colleagues [1] for their comments. We agree that laboratories must have access to accurate and standardized methods for antimicrobial susceptibility testing (AST) results to be clinically meaningful. The reference method for performing Clostridioides difficile AST is agar dilution according to Clinical and Laboratory Standards Institute (CLSI) guidelines [2]. The CLSI method for performing AST for anaerobic bacteria recommends that 5 μg/mL of hemin be incorporated into agar dilution plates and that the hemin stock solution should be protected from light and stored at 4°C–8°C for no longer than 1 month [2]. The susceptibility testing done by Gargis et al [3] was performed according to these guidelines, and the hemin stock solution was protected from light. | | Nevertheless, we read with interest the research in recent years [4–6] related to heme-dependent metronidazole resistance, including the reported association between isolates characterized as heme dependent and metronidazole resistant and the presence of a T to G mutation (PnimBG) in the −10 promoter region of the nitroimidazole reductase gene, nimB [5]. While Olaitan et al [5] found that not all heme-dependent metronidazole-resistant isolates contained the PnimBG mutation, Olaitan et al [5] indicate that most do; therefore, the presence of PnimBG may be predictive of resistance. We determined that the nimB mutation was present in 20% of our study isolates (116 of 593), of which 99% (115 of 116) belonged to RT027 (Table 1). The remaining isolate was RT014, the only RT014 isolate containing the PnimBG mutation among the 65 evaluated.

Along with the rise in modern chronic diseases, ranging from diabetes to asthma, lay the challenges posed by increasing antibiotic resistance resulting in difficult to treat infections, as well as sepsis. An emerging and unifying theme in the pathogenesis of these diverse public health threats is changes in the microbial communities that inhabit multiple body sites. Although there is great promise in exploring the role of these microbial communities in chronic disease pathogenesis, the shorter timeframe of most infectious disease pathogenesis may allow early translation of our basic scientific understanding of microbial ecology and host-microbiota-pathogen interactions. Likely translation avenues include development of preventive strategies, diagnostics, and therapeutics. For example, as basic research related to microbial pathogenesis continues to progress, Clostridioides difficile infection (CDI) is already being addressed clinically through at least two of these three avenues: targeted antibiotic stewardship and treatment of recurrent disease through fecal microbiota transplantation (FMT).

BACKGROUND: Efforts to develop a Clostridium difficile vaccine are underway; identification of patients at risk for C. difficile infection (CDI) is critical to inform vaccine trials. We identified groups at high risk of CDI≥28 days after hospital discharge. METHODS: Hospital discharge data and pharmacy data from two large academic centers, in New York and Connecticut, were linked to active population-based CDI surveillance data from the Emerging Infections Program (EIP). Adult residents of the EIP surveillance area were included if they had an inpatient stay at a study hospital without prior history of CDI. The primary outcome was CDI by either toxin or molecular assay ≥28 days after an index hospitalization. Important predictors of CDI≥28 days post discharge were initially identified through a Cox proportional hazards model (stepwise backward selection) using a derivation cohort; final model parameters were used to develop a risk score evaluated in the validation cohort. RESULTS: Of the 35,186 index hospitalizations, 288 (0.82%) had CDI≥28 days post discharge. After parameter selection, age, number of hospitalizations in the prior 90 days, admission diagnosis, and the use of 3rd/4th generation cephalosporin, clindamycin or fluoroquinolone antibiotic classes remained in the model. Using the validation cohort, the risk score was predictive (p<0.001) with a c-score of 0.75. Based on the distribution of scores in the derivation cohort, we divided the patients into low and high risk groups. In the high risk group, 1.6% experienced CDI≥28 days post discharge compared to 0.3% among our low risk group. CONCLUSIONS: Our study identified specific parameters for a risk score that can be applied at discharge to identify a patient population whose risk of CDI≥28 days following an acute care hospitalization was 5 times greater than other patients.