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Each year in the United States, Clostridioides difficile causes nearly 500,000 gastrointestinal infections that range from mild diarrhea to severe colitis and death. The ability to identify patients at increased risk for severe disease or mortality at the time of diagnosis of C. difficile infection (CDI) would allow clinicians to effectively allocate disease modifying therapies. In this study, we developed models consisting of only a small number of serum biomarkers that are capable of predicting both 30-day all-cause mortality and adverse outcomes of patients at time of CDI diagnosis. We were able to validate these models through experimental mouse infection. This provides evidence that the biomarkers reflect the underlying pathophysiology and that our mouse model of CDI reflects the pathogenesis of human infection. Predictive models can not only assist clinicians in identifying patients at risk for severe CDI but also be utilized for targeted enrollment in clinical trials aimed at reduction of adverse outcomes from severe CDI. Clostridioides difficile infection (CDI) can result in severe disease and death, with no accurate models that allow for early prediction of adverse outcomes. To address this need, we sought to develop serum-based biomarker models to predict CDI outcomes. We prospectively collected sera ≤48 h after diagnosis of CDI in two cohorts. Biomarkers were measured with a custom multiplex bead array assay. Patients were classified using IDSA severity criteria and the development of disease-related complications (DRCs), which were defined as ICU admission, colectomy, and/or death attributed to CDI. Unadjusted and adjusted models were built using logistic and elastic net modeling. The best model for severity included procalcitonin (PCT) and hepatocyte growth factor (HGF) with an area (AUC) under the receiver operating characteristic (ROC) curve of 0.74 (95% confidence interval, 0.67 to 0.81). The best model for 30-day mortality included interleukin-8 (IL-8), PCT, CXCL-5, IP-10, and IL-2Rα with an AUC of 0.89 (0.84 to 0.95). The best model for DRCs included IL-8, procalcitonin, HGF, and IL-2Rα with an AUC of 0.84 (0.73 to 0.94). To validate our models, we employed experimental infection of mice with C. difficile . Antibiotic-treated mice were challenged with C. difficile and a similar panel of serum biomarkers was measured. Applying each model to the mouse cohort of severe and nonsevere CDI revealed AUCs of 0.59 (0.44 to 0.74), 0.96 (0.90 to 1.0), and 0.89 (0.81 to 0.97). In both human and murine CDI, models based on serum biomarkers predicted adverse CDI outcomes. Our results support the use of serum-based biomarker panels to inform Clostridioides difficile infection treatment. IMPORTANCE Each year in the United States, Clostridioides difficile causes nearly 500,000 gastrointestinal infections that range from mild diarrhea to severe colitis and death. The ability to identify patients at increased risk for severe disease or mortality at the time of diagnosis of C. difficile infection (CDI) would allow clinicians to effectively allocate disease modifying therapies. In this study, we developed models consisting of only a small number of serum biomarkers that are capable of predicting both 30-day all-cause mortality and adverse outcomes of patients at time of CDI diagnosis. We were able to validate these models through experimental mouse infection. This provides evidence that the biomarkers reflect the underlying pathophysiology and that our mouse model of CDI reflects the pathogenesis of human infection. Predictive models can not only assist clinicians in identifying patients at risk for severe CDI but also be utilized for targeted enrollment in clinical trials aimed at reduction of adverse outcomes from severe CDI.

Background: Clostridioides difficile infection (CDI) frequently recurs after initial treatment. Predicting recurrent CDI (rCDI) early in the disease course can assist clinicians in their decision making and improve outcomes. However, predictions based on clinical criteria alone are not accurate and/or do not validate other results. Here, we tested the hypothesis that circulating and stool-derived inflammatory mediators predict rCDI. Methods: Consecutive subjects with available specimens at diagnosis were included if they tested positive for toxigenic C. difficile (+enzyme immunoassay [EIA] for glutamate dehydrogenase and toxins A/B, with reflex to PCR for the tcdB gene for discordants). Stool was thawed on ice, diluted 1:1 in PBS with protease inhibitor, centrifuged, and used immediately. A 17-plex panel of inflammatory mediators was run on a Luminex 200 machine using a custom antibody-linked bead array. Prior to analysis, all measurements were normalized and log-transformed. Stool toxin activity levels were quantified using a custom cell-culture assay. Recurrence was defined as a second episode of CDI within 100 days. Ordination characterized variation in the panel between outcomes, tested with a permutational, multivariate ANOVA. Machine learning via elastic net regression with 100 iterations of 5-fold cross validation selected the optimal model and the area under the receiver operator characteristic curve (AuROC) was computed. Sensitivity analyses excluding those that died and/or lived >100 km away were performed. Results: We included 186 subjects, with 95 women (51.1%) and average age of 55.9 years (±20). More patients were diagnosed by PCR than toxin EIA (170 vs 55, respectively). Death, rCDI, and no rCDI occurred in 32 (17.2%), 36 (19.4%), and 118 (63.4%) subjects, respectively. Ordination revealed that the serum panel was associated with rCDI ( P = .007) but the stool panel was not. Serum procalcitonin, IL-8, IL-6, CCL5, and EGF were associated with recurrence. The machine-learning models using the serum panel predicted rCDI with AuROCs between 0.74 and 0.8 (Fig. 1). No stool inflammatory mediators independently predicted rCDI. However, stool IL-8 interacted with toxin activity to predict rCDI (Fig. 2). These results did not change significantly upon sensitivity analysis. Conclusions: A panel of serum inflammatory mediators predicted rCDI with up to 80% accuracy, but the stool panel alone was less successful. Incorporating toxin activity levels alongside inflammatory mediator measurements is a novel, promising approach to studying stool-derived biomarkers of rCDI. This approach revealed that stool IL-8 is a potential biomarker for rCDI. These results need to be confirmed both with a larger dataset and after adjustment for clinical covariates. Funding: None Disclosure: Vincent Young is a consultant for Bio-K+ International, Pantheryx, and Vedanta Biosciences.

Previously, we showed proof-of-concept in a mouse model that oral administration of cholestyramine prevented enrichment of daptomycin-resistant in the gastrointestinal (GI) tract during daptomycin therapy. Cholestyramine binds daptomycin in the gut, which removes daptomycin selection pressure and so prevents the enrichment of resistant clones. Here, we investigated two open questions related to this approach: (i) can cholestyramine prevent the enrichment of diverse daptomycin mutations emerging in the gut? and (ii) how does the timing of cholestyramine administration impact its ability to suppress resistance? Mice with GI were treated with daptomycin with or without cholestyramine, and was cultured from feces to measure changes in daptomycin susceptibility. A subset of clones was sequenced to investigate the genomic basis of daptomycin resistance. Cholestyramine prevented the enrichment of diverse resistance mutations that emerged in daptomycin-treated mice. Whole-genome sequencing revealed that resistance emerged through multiple genetic pathways, with most candidate resistance mutations observed in the gene. In addition, we observed that cholestyramine was most effective when administration started prior to the first dose of daptomycin. However, beginning cholestyramine after the first daptomycin dose reduced the frequency of resistant compared to not using cholestyramine at all. Cholestyramine prevented the enrichment of diverse daptomycin-resistance mutations in intestinal populations during daptomycin treatment, and it is a promising tool for managing the transmission of daptomycin-resistant .

Background Clostridium difficile infection (CDI) is a major public health concern and frequently results in severe disease, including death. Predicting subsequent complications early in the course can help optimize treatments and improve outcomes. However, models based on clinical criteria alone are not accurate and/or do not validate. We hypothesized that inflammatory mediators from the stool would be biomarkers for severity and complications. Methods Subjects were included after testing positive for toxigenic C. difficile by the clinical microbiology laboratory via enzyme immunoassay (EIA) for glutamate dehydrogenase and toxins A/B, with reflex to tcdB gene PCR for discordants. Stool was thawed on ice, diluted 1:1 with PBS and protease inhibitor, centrifuged, and the supernatant was analyzed by a custom antibody-linked bead array with 17 inflammatory mediators. Measurements were normalized and log-transformed. IDSA severity was defined by serum white blood cell count > 15000 cells/µL or creatinine 1.5-fold above baseline. Primary 30-day outcomes were all-cause mortality and attributable disease-related complications (DRC): ICU admission, colectomy, and/or death. Analyses included principal components, permutational multivariate ANOVA (PERMANOVA), and logistic regression ± L1 regularization and 5-fold cross validation. The area under the receiver operator characteristic curve (AuROC) was computed. Results We included 225 subjects, with 124 females (55.1%), average age 58.5 (±17), and more PCR+ than toxin EIA+ (170 vs. 55, respectively). IDSA severity, death, and DRCs occurred in 79 (35.1%), 14 (6.2%), and 12 (5.3%) subjects, respectively. PCA and PERMANOVA showed IDSA severity (P = 0.009) but not death or DRCs associated with the panel (figure). Several inflammatory mediators associated with IDSA severity and death (table). Machine learning models had AuROCs of 0.77 (IDSA severity), 0.84 (death), and 0.7 (DRCs). Conclusion We found that specific inflammatory mediators from the stool of patients with CDI associate with severity and complications. These results are promising, but need replication in a larger dataset and should be incorporated into models that include clinical covariates prior to deployment. Disclosures All authors: No reported disclosures.

Background The optimal diagnostic strategy for Clostridium difficile infection (CDI) is not known, and no test is shown to clearly differentiate colonization from symptomatic infection. We hypothesized that detection and/or quantification of stool toxins would associate with severe disease and adverse outcomes. Methods We conducted a retrospective cohort study among subjects with CDI diagnosed in 2016 at the University of Michigan. The clinical microbiology laboratory tested for glutamate dehydrogenase antigen and toxins A/B by enzyme immunoassay (EIA). Discordant results reflexed to PCR for the tcdB gene. Stool toxin levels were quantified via a modified cell cytotoxicity assay (CCA). C. difficile was isolated by anaerobic culture and ribotyped. Severe CDI was defined by the IDSA criteria: white blood cell count >15,000 cells/µL or a 1.5-fold increase in serum creatinine above baseline. The primary outcomes were all-cause 30-day mortality and a composite of colectomy, ICU admission, and/or death attributable to CDI within 30 days. Analysis included standard bivariable tests and adjusted models via logistic regression. Results From 565 adult patients, we obtained 646 samples; 199 (30.8%) contained toxins by EIA. Toxin positivity associated with IDSA severity (Table 1), but not our primary outcomes on unadjusted analysis. After adjustment for putative confounders, we still did not observe an association between toxin positivity and our primary outcomes. Stool toxin levels by CCA >6.4 ng/mL associated with IDSA severity (Table 1), but not the primary outcomes. Compared with the period from 2010 to 2013, the circulating ribotypes of C. difficile at our institution changed in 2016. Notably ribotype 106 newly emerged, accounting for 10.6% of strains, and ribotype 027 fell to 9.3% (Table 2). The incidence of ribotype 014-027 has remained stable at 18.9%, but this strain was associated with both IDSA severity and 30-day mortality (OR = 3.32; P = 0.001). Conclusion Toxin detection by EIA/CCA associated with IDSA severity, but this study was unable to confirm an association with subsequent adverse outcomes. The molecular epidemiology of C. difficile has shifted, and this may have implications for the optimal diagnostic strategy for CDI. Disclosures All authors: No reported disclosures.

Background Clostridium difficile infection (CDI) can result in severe disease and death. We are currently unable to identify patients at risk for developing adverse outcomes. We previously showed multiple inflammatory mediators were associated with severity and adverse outcomes. Here, we set out to validate these findings in patients and a murine model of CDI. Methods CDI was diagnosed by the clinical microbiology laboratory. Sera were collected ≤48 hours after diagnosis from pilot (October 2010–November 2012) and validation (January–September 2016) cohorts. Inflammatory mediators were measured with a custom multiplex assay. IDSA severity was defined as serum creatinine >1.5-fold above baseline or white blood cell count >15,000 cells/mL. The 30-day outcomes were all-cause mortality and disease-related complications (DRCs): ICU admission, colectomy, or death attributed to CDI. We sought to validate our patient findings in a murine model of CDI: 67 antibiotic-treated mice were infected with 630 g (37 mice), a low virulence strain, or VPI 10463 (30 mice), a highly virulent strain. Host responses were assessed with a murine version of the multiplex panel. Unadjusted and adjusted models were built using logistic and L1 regression, respectively. Results The pilot cohort had 156 CDI cases; 63 (40%) with IDSA severity. The inflammatory response in IDSA severe cases was distinct based on redundancy analysis of all measured analytes (P = 0.01). In unadjusted analysis, IL-2R, IL-6, and procalcitonin associated with severity (P < 0.001, P = 0.003, and P = 0.003, respectively). The same findings were seen in the validation cohort of 272 cases (Figure 1). Unadjusted analyses revealed several predictors of severity and outcomes (Table 1). Adjusted models performed well (Figure 2) with AUCs of 0.74 [0.67–0.81] (IDSA severity), 0.89 [0.83–0.95] (death), and 0.84 [0.74–0.95] (DRCs). Application of each model to the mouse cohort for high vs. low virulence infections revealed AUCs of 0.59 [0.44–0.74], 0.96 [0.90–1.0], and 0.89 [0.81–0.97] (Figure 3). Conclusion In both humans and a murine CDI model, a panel of biomarkers from sera associated with severe CDI and predicted adverse outcomes. Our results support the possibility of a serum-based biomarker panel to inform medical decision-making for patients with CDI. Disclosures All Authors: No reported Disclosures.

Previously, we showed proof-of-concept in a mouse model that oral administration of cholestyramine prevented enrichment of daptomycin-resistant Enterococcus faecium in the gastrointestinal (GI) tract during daptomycin therapy. Cholestyramine binds daptomycin in the gut, which removes daptomycin selection pressure and so prevents the enrichment of resistant clones. Here, we investigated two open questions related to this approach: 1) can cholestyramine prevent the enrichment of diverse daptomycin mutations emerging de novo in the gut? 2) how does the timing of cholestyramine administration impact its ability to suppress resistance? Mice with GI E. faecium were treated with daptomycin with or without cholestyramine, and E. faecium was cultured from feces to measure changes in daptomycin susceptibility. A subset of clones was sequenced to investigate the genomic basis of daptomycin resistance. Cholestyramine prevented the enrichment of diverse resistance mutations that emerged de novo in daptomycin-treated mice. Whole-genome sequencing revealed that resistance emerged through multiple genetic pathways, with most candidate resistance mutations observed in the clsA gene. Additionally, we observed that cholestyramine was most effective when administration started prior to the first dose of daptomycin. However, beginning cholestyramine after the first daptomycin dose reduced the frequency of resistant E. faecium compared to not using cholestyramine at all. Cholestyramine prevented the enrichment of diverse daptomycin-resistance mutations in intestinal E. faecium populations during daptomycin treatment, and it is a promising tool for managing transmission of daptomycin-resistant E. faecium.