Prediction of recurrent clostridium difficile infection at the bedside: the GEIH-CDI score

Is advanced age still a risk factor for recurrence of C. difficile infection in the era of new treatments?

BACKGROUND

Advanced age has been widely identified as a risk factor for recurrent Clostridioides difficile infection (CDI), but most related studies were performed before the introduction of novel therapies. The aim of this study was to compare CDI characteristics and outcomes in patients over and under 80 years old with CDI and their outcomes in the era of new treatments.

METHODS

This was a retrospective cohort study of patients diagnosed with CDI from January 2021 to December 2022 in an academic hospital. We compared recurrence and mortality at 12 weeks after the end of treatment. An extension of the Fine and Grey model adjusted for competing events was used to assess the effect of age on recurrence.

RESULTS

Four hundred seventy-six patients were considered to have CDI (320 in patients <80 years and 156 in ≥80 years). CDI in older patients was more frequently healthcare-associated and was more severe. Although the Charlson index was almost identical between populations, comorbidities clearly differed. New treatments (bezlotoxumab, fidaxomicin and faecal microbiota transplantation) were more frequently used in older patients without statistical significance (41.3% vs. 33.4%, P = .053). There were 69 (14.5%) recurrences, with no differences by age group after adjusting for competing events. Mortality was greater in the oldest (35.3%) than in the youngest (13.1%); P < .001.

CONCLUSIONS

No differences in CDI recurrence rates were found between age groups. However, there was a high mortality rate in patients ≥80 years old, which emphasises the urgent need to improve the prevention and treatment of CDI in this group.

Predicting Clostridioides difficile infection outcomes with explainable machine learning

BACKGROUND

Clostridioides difficile infection results in life-threatening short-term outcomes and the potential for subsequent recurrent infection. Predicting these outcomes at diagnosis, when important clinical decisions need to be made, has proven to be a difficult task.

METHODS

52 clinical features from existing models or the literature were collected retrospectively within ±48 h of diagnosis among 1660 inpatient infections. A modified desirability of outcome ranking (DOOR) was designed to encompass clinically-important severe events attributable to the acute infection (intensive care transfer due to sepsis, shock, colectomy/ileostomy, mortality) and/or 60-day recurrence. A deep neural network was constructed and interpreted using SHapley Additive exPlanations (SHAP). High-importance features were used to train a reduced, shallow network and performance was compared to existing conventional models (7 severity, 7 recurrence; after summing DOOR probabilities to align with conventional binary outputs) using area under the ROC curve (AUROC) and DeLong tests.

FINDINGS

The full (52-feature) model achieved an out-of-sample AUROC 0.823 for severity and 0.678 for recurrence. SHAP identified 13 unique, highly-important features (age, hypotension, initial treatment, onset, PCR cycle threshold, number of prior episodes, antibiotic exposure, fever, hypotension, pressors, leukocytosis, creatinine, lactate) that were used to train a reduced model, which performed similarly to the full model (severity AUROC difference P = 0.130; recurrence P = 0.426) and significantly better than the top severity model (reduced model predicting severity 0.837, ATLAS 0.749; P = 0.001). The reduced model also outperformed the top recurrence model, but this was not statistically-significant (reduced model recurrence AUROC 0.653, IDSA Recurrence Risk Criteria 0.595; P = 0.196). The final, reduced model was deployed as a web application with real-time SHAP explanations.

INTERPRETATION

Our final model outperformed existing severity and recurrence models; however, it requires external validation. A DOOR output allows specific clinical questions to be asked with explainable predictions that can be feasibly implemented with limited computing resources.

FUNDING

National Institutes of Health-Institute of Allergy and Infectious Diseases.

Fecal microbiota composition is a better predictor of recurrent Clostridioides difficile infection than clinical factors in a prospective, multicentre cohort study

INTRODUCTION

Clostridioides difficile infection (CDI) is the most common cause of antibiotic-associated diarrhoea. Fidaxomicin and fecal microbiota transplantation (FMT) are effective, but expensive therapies to treat recurrent CDI (reCDI). Our objective was to develop a prediction model for reCDI based on the gut microbiota composition and clinical characteristics, to identify patients who could benefit from early treatment with fidaxomicin or FMT.

METHODS

Multicentre, prospective, observational study in adult patients diagnosed with a primary episode of CDI. Fecal samples and clinical data were collected prior to, and after 5 days of CDI treatment. Follow-up duration was 8 weeks. Microbiota composition was analysed by IS-pro, a bacterial profiling technique based on phylum- and species-specific differences in the 16-23 S interspace regions of ribosomal DNA. Bayesian additive regression trees (BART) and adaptive group-regularized logistic ridge regression (AGRR) were used to construct prediction models for reCDI.

RESULTS

209 patients were included, of which 25% developed reCDI. Variables related to microbiota composition provided better prediction of reCDI and were preferentially selected over clinical factors in joint prediction models. Bacteroidetes abundance and diversity after start of CDI treatment, and the increase in Proteobacteria diversity relative to baseline, were the most robust predictors of reCDI. The sensitivity and specificity of a BART model including these factors were 95% and 78%, but these dropped to 67% and 62% in out-of-sample prediction.

CONCLUSION

Early microbiota response to CDI treatment is a better predictor of reCDI than clinical prognostic factors, but not yet sufficient enough to predict reCDI in daily practice.

Bezlotoxumab during the first episode of Clostridioides difficile infection in patients at high risk of recurrence

PURPOSE

To describe a cohort with a high risk of recurrence who received bezlotoxumab during the first episode of Clostridioides difficile infection (CDI) and to compare this cohort with patients with similar characteristics who did not receive the monoclonal antibody.

METHODS

A prospective and multicentre study of patients with a high risk of recurrence (expected recurrence rate>35%) who were treated with bezlotoxumab during their first episode of CDI was conducted. A propensity score-matched model 1:2 was used to compare both cohorts that were weighed according to basal characteristics (hospital-acquisition, creatinine value, and fidaxomicin as a CDI treatment).

RESULTS

Sixty patients (mean age:72 years) were prospectively treated with bezlotoxumab plus anti-Clostridioides antibiotic therapy. Vancomycin (48 patients) and fidaxomicin (12 patients) were prescribed for CDI treatment, and bezlotoxumab was administered at a mean of 4.2 (SD:2.1) days from the beginning of therapy. Recurrence occurred in nine out of 54 (16.7%) evaluable patients at 8 weeks. Forty bezlotoxumab-treated patients were matched with 69 non-bezlotoxumab-treated patients. Recurrence rates at 12 weeks were 15.0% (6/40) in bezlotoxumab-treated patients vs. 23.2% (16/69) in non-bezlotoxumab-treated patients (OR:0.58 [0.20-1.65]). No adverse effects were observed related to bezlotoxumab infusion. Only one of 9 patients with previous heart failure developed heart failure.

CONCLUSION

We observed that patients treated with bezlotoxumab in a real-world setting during a first episode of CDI having high risk of recurrence, presented low rate of recurrence. However, a significant difference in recurrence could not be proved in comparison to the controls. We did not detect any other safety concerns.

An integrated pipeline for prediction of Clostridioides difficile infection

With the expansion of electronic health records(EHR)-linked genomic data comes the development of machine learning-enable models. There is a pressing need to develop robust pipelines to evaluate the performance of integrated models and minimize systemic bias. We developed a prediction model of symptomatic Clostridioides difficile infection(CDI) by integrating common EHR-based and genetic risk factors(rs2227306/IL8). Our pipeline includes (1) leveraging phenotyping algorithm to minimize temporal bias, (2) performing simulation studies to determine the predictive power in samples without genetic information, (3) propensity score matching to control for the confoundings, (4) selecting machine learning algorithms to capture complex feature interactions, (5) performing oversampling to address data imbalance, and (6) optimizing models and ensuring proper bias-variance trade-off. We evaluate the performance of prediction models of CDI when including common clinical risk factors and the benefit of incorporating genetic feature(s) into the models. We emphasize the importance of building a robust integrated pipeline to avoid systemic bias and thoroughly evaluating genetic features when integrated into the prediction models in the general population and subgroups.

Microbiome profile and calprotectin levels as markers of risk of recurrent Clostridioides difficile infection

Introduction

Clostridioides difficile infection (CDI) is the main cause of nosocomial diarrhoea in developed countries. Recurrent CDI (R-CDI), which affects 20%-30% of patients and significantly increases hospital stay and associated costs, is a key challenge. The main objective of this study was to explore the role of the microbiome and calprotectin levels as predictive biomarkers of R-CDI.

Methods

We prospectively (2019-2021) included patients with a primary episode of CDI. Clinical data and faecal samples were collected. The microbiome was analysed by sequencing the hypervariable V4 region of the 16S rRNA gene on an Illumina Miseq platform.

Results

We enrolled 200 patients with primary CDI, of whom 54 developed R-CDI and 146 did not. We analysed 200 primary samples and found that Fusobacterium increased in abundance, while Collinsella, Senegalimassilia, Prevotella and Ruminococcus decreased in patients with recurrent versus non-recurrent disease. Elevated calprotectin levels correlated significantly with R-CDI (p=0.01). We built a risk index for R-CDI, including as prognostic factors age, sex, immunosuppression, toxin B amplification cycle, creatinine levels and faecal calprotectin levels (overall accuracy of 79%).

Discussion

Calprotectin levels and abundance of microbial genera such as Fusobacterium and Prevotella in primary episodes could be useful as early markers of R-CDI. We propose a readily available model for prediction of R-CDI that can be applied at the initial CDI episode. The use of this tool could help to better tailor treatments according to the risk of R-CDI.

Assessing the clinical value of faecal bile acid profiling to predict recurrence in primary Clostridioides difficile infection

BACKGROUND

Factors influencing recurrence risk in primary Clostridioides difficile infection (CDI) are poorly understood, and tools predicting recurrence are lacking. Perturbations in bile acids (BAs) contribute to CDI pathogenesis and may be relevant to primary disease prognosis.

AIMS

To define stool BA dynamics in patients with primary CDI and to explore signatures predicting recurrence METHODS: Weekly stool samples were collected from patients with primary CDI from the last day of anti-CDI therapy until recurrence or, otherwise, through 8 weeks post-completion. Ultra-high performance liquid chromatography-mass spectrometry was used to profile BAs. Stool bile salt hydrolase (BSH) activity was measured to determine primary BA bacterial deconjugation capacity. Multivariate and univariate models were used to define differential BA trajectories in patients with recurrence versus those without, and to assess faecal BAs as predictive markers for recurrence.

RESULTS

Twenty (36%) of 56 patients (median age: 57, 64% male) had recurrence; 80% of recurrences occurred within the first 9 days post-antibiotic treatment. Principal component analysis of stool BA profiles demonstrated clustering by recurrence status and post-treatment timepoint. Longitudinal faecal BA trajectories showed recovery of secondary BAs and their derivatives only in patients without recurrence. BSH activity increased over time only among non-relapsing patients (β = 0.056; likelihood ratio test p = 0.018). A joint longitudinal-survival model identified five stool BAs with area under the receiver operating characteristic curve >0.73 for predicting recurrence within 9 days post-CDI treatment.

CONCLUSIONS

Gut BA metabolism dynamics differ in primary CDI patients between those developing recurrence and those who do not. Individual BAs show promise as potential novel biomarkers to predict CDI recurrence.

Development of a clinical prediction model for recurrence and mortality outcomes after Clostridioides difficile infection using a machine learning approach

OBJECTIVES

Clostridioides difficile infection (CDI) is associated with a large burden of morbidity and mortality worldwide. Previous studies have developed models for predicting recurrence and mortality following CDI, but no machine learning predictive models have been developed specifically using data from Japanese patients.

METHODS

Using a database of records from acute care hospitals in Japan, we extracted records from January 2012 to September 2016 (plus a 60-day lookback window). A total of 19,159 patients were included. We used a machine learning approach, XGBoost, and compared it to a traditional unregularized logistic regression model. The first 80% of the dataset (by patient index date) was used to optimize model hyperparameters and train the final models, and evaluation was performed on the remaining 20%. We measured model performance by the area under the receiver operator curve and assessed feature importance using Shapley additive explanations.

RESULTS

Performance was similar between the machine learning approach and the classical logistic regression model. Logistic regression performed slightly better than XGBoost for predicting mortality.

CONCLUSION

XGBoost performed slightly better than logistic regression for predicting recurrence, but it was not competitive with existing published models. Despite this, a future machine learning-based application provided in a bedside setting at low cost might be a clinically useful tool.

Gut metabolites predict Clostridioides difficile recurrence

BACKGROUND

Clostridioides difficile infection (CDI) is the most common hospital acquired infection in the USA, with recurrence rates > 15%. Although primary CDI has been extensively linked to gut microbial dysbiosis, less is known about the factors that promote or mitigate recurrence. Moreover, previous studies have not shown that microbial abundances in the gut measured by 16S rRNA amplicon sequencing alone can accurately predict CDI recurrence.

RESULTS

We conducted a prospective, longitudinal study of 53 non-immunocompromised participants with primary CDI. Stool sample collection began pre-CDI antibiotic treatment at the time of diagnosis, and continued up to 8 weeks post-antibiotic treatment, with weekly or twice weekly collections. Samples were analyzed using (1) 16S rRNA amplicon sequencing, (2) liquid chromatography/mass-spectrometry metabolomics measuring 1387 annotated metabolites, and (3) short-chain fatty acid profiling. The amplicon sequencing data showed significantly delayed recovery of microbial diversity in recurrent participants, and depletion of key anaerobic taxa at multiple time-points, including Clostridium cluster XIVa and IV taxa. The metabolomic data also showed delayed recovery in recurrent participants, and moreover mapped to pathways suggesting distinct functional abnormalities in the microbiome or host, such as decreased microbial deconjugation activity, lowered levels of endocannabinoids, and elevated markers of host cell damage. Further, using predictive statistical/machine learning models, we demonstrated that the metabolomic data, but not the other data sources, can accurately predict future recurrence at 1 week (AUC 0.77 [0.71, 0.86; 95% interval]) and 2 weeks (AUC 0.77 [0.69, 0.85; 95% interval]) post-treatment for primary CDI.

CONCLUSIONS

The prospective, longitudinal, and multi-omic nature of our CDI recurrence study allowed us to uncover previously unrecognized dynamics in the microbiome and host presaging recurrence, and, in particular, to elucidate changes in the understudied gut metabolome. Moreover, we demonstrated that a small set of metabolites can accurately predict future recurrence. Our findings have implications for development of diagnostic tests and treatments that could ultimately short-circuit the cycle of CDI recurrence, by providing candidate metabolic biomarkers for diagnostics development, as well as offering insights into the complex microbial and metabolic alterations that are protective or permissive for recurrence. Video Abstract.

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.

European Society of Clinical Microbiology and Infectious Diseases: 2021 update on the treatment guidance document for Clostridioides difficile infection in adults

SCOPE

In 2009, the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) published the first treatment guidance document for Clostridioides difficile infection (CDI). This document was updated in 2014. The growing literature on CDI antimicrobial treatment and novel treatment approaches, such as faecal microbiota transplantation (FMT) and toxin-binding monoclonal antibodies, prompted the ESCMID study group on C. difficile (ESGCD) to update the 2014 treatment guidance document for CDI in adults.

METHODS AND QUESTIONS

Key questions on CDI treatment were formulated by the guideline committee and included: What is the best treatment for initial, severe, severe-complicated, refractory, recurrent and multiple recurrent CDI? What is the best treatment when no oral therapy is possible? Can prognostic factors identify patients at risk for severe and recurrent CDI and is there a place for CDI prophylaxis? Outcome measures for treatment strategy were: clinical cure, recurrence and sustained cure. For studies on surgical interventions and severe-complicated CDI the outcome was mortality. Appraisal of available literature and drafting of recommendations was performed by the guideline drafting group. The total body of evidence for the recommendations on CDI treatment consists of the literature described in the previous guidelines, supplemented with a systematic literature search on randomized clinical trials and observational studies from 2012 and onwards. The Grades of Recommendation Assessment, Development and Evaluation (GRADE) system was used to grade the strength of our recommendations and the quality of the evidence. The guideline committee was invited to comment on the recommendations. The guideline draft was sent to external experts and a patients' representative for review. Full ESCMID endorsement was obtained after a public consultation procedure.

RECOMMENDATIONS

Important changes compared with previous guideline include but are not limited to: metronidazole is no longer recommended for treatment of CDI when fidaxomicin or vancomycin are available, fidaxomicin is the preferred agent for treatment of initial CDI and the first recurrence of CDI when available and feasible, FMT or bezlotoxumab in addition to standard of care antibiotics (SoC) are preferred for treatment of a second or further recurrence of CDI, bezlotoxumab in addition to SoC is recommended for the first recurrence of CDI when fidaxomicin was used to manage the initial CDI episode, and bezlotoxumab is considered as an ancillary treatment to vancomycin for a CDI episode with high risk of recurrence when fidaxomicin is not available. Contrary to the previous guideline, in the current guideline emphasis is placed on risk for recurrence as a factor that determines treatment strategy for the individual patient, rather than the disease severity.

Prognostic factors for severe and recurrent Clostridioides difficile infection: a systematic review

OBJECTIVES

Clostridioides difficile infection (CDI), its subsequent recurrences (rCDIs), and severe CDI (sCDI) provide a significant burden for both patients and the healthcare system. Identifying patients diagnosed with initial CDI who are at increased risk of developing sCDI/rCDI could lead to more cost-effective therapeutic choices. In this systematic review we aimed to identify clinical prognostic factors associated with an increased risk of developing sCDI or rCDI.

METHODS

PubMed, Embase, Emcare, Web of Science and COCHRANE Library databases were searched from database inception through March, 2021. The study eligibility criteria were cohort and case-control studies. Participants were patients ≥18 years old diagnosed with CDI, in which clinical or laboratory factors were analysed to predict sCDI/rCDI. Risk of bias was assessed by using the Quality in Prognostic Research (QUIPS) tool and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool modified for prognostic studies. Study selection was performed by two independent reviewers. Overview tables of prognostic factors were constructed to assess the number of studies and the respective effect direction and statistical significance of an association.

RESULTS

136 studies were included for final analysis. Greater age and the presence of multiple comorbidities were prognostic factors for sCDI. Identified risk factors for rCDI were greater age, healthcare-associated CDI, prior hospitalization, proton pump inhibitors (PPIs) started during or after CDI diagnosis, and previous rCDI.

CONCLUSIONS

Prognostic factors for sCDI and rCDI could aid clinicians to make treatment decisions based on risk stratification. We suggest that future studies use standardized definitions for sCDI/rCDI and systematically collect and report the risk factors assessed in this review, to allow for meaningful meta-analysis of risk factors using data of high-quality trials.

Effect of Endogenous Clostridioides difficile Toxin Antibodies on Recurrence of C. difficile Infection

BACKGROUND

Endogenous antibodies (eAbs) against Clostridioides (Clostridium) difficile toxins may protect against recurrence of C. difficile infection (rCDI). This hypothesis was tested using placebo group data from MODIFY (Monoclonal Antibodies for C. difficile Therapy) I and II (NCT01241552 and NCT01513239, respectively), global, randomized phase 3 trials that assessed the efficacy and safety of the antitoxin monoclonal antibodies bezlotoxumab and actoxumab in participants receiving antibiotic therapy for CDI.

METHODS

A placebo infusion (normal saline) was administered on study day 1. Serum samples were collected on day 1, week 4, and week 12, and eAb-A and eAb-B titers were measured by 2 validated electrochemiluminescence immunoassays. Rates of initial clinical cure and rCDI were summarized by eAb titer category (low, medium, high) at each time point.

RESULTS

Serum eAb titers were available from a total of 773 participants. The proportion of participants with high eAb-A and eAb-B titers increased over time. Rates of initial clinical cure were similar across eAb titer categories. There was no correlation between eAb-A titers and rCDI rate at any time point. However, there was a negative correlation between rCDI and eAb-B titer on day 1 and week 4. rCDI occurred in 22% of participants with high eAb-B titers at baseline compared with 35% with low or medium titers (P = .015).

CONCLUSIONS

Higher eAb titers against toxin B, but not toxin A, were associated with protection against rCDI. These data are consistent with the observed efficacy of bezlotoxumab, and lack of efficacy of actoxumab, in the MODIFY trials.

CLINICAL TRIALS REGISTRATION

NCT01241552 and NCT01513239.

External validation of two prediction tools for patients at risk for recurrent Clostridioides difficile infection

BACKGROUND

One in four patients with primary Clostridioides difficile infection (CDI) develops recurrent CDI (rCDI). With every recurrence, the chance of a subsequent CDI episode increases. Early identification of patients at risk for rCDI might help doctors to guide treatment. The aim of this study was to externally validate published clinical prediction tools for rCDI.

METHODS

The validation cohort consisted of 129 patients, diagnosed with CDI between 2018 and 2020. rCDI risk scores were calculated for each individual patient in the validation cohort using the scoring tools described in the derivation studies. Per score value, we compared the average predicted risk of rCDI with the observed number of rCDI cases. Discrimination was assessed by calculating the area under the receiver operating characteristic curve (AUC).

RESULTS

Two prediction tools were selected for validation (Cobo 2018 and Larrainzar-Coghen 2016). The two derivation studies used different definitions for rCDI. Using Cobo's definition, rCDI occurred in 34 patients (26%) of the validation cohort: using the definition of Larrainzar-Coghen, we observed 19 recurrences (15%). The performance of both prediction tools was poor when applied to our validation cohort. The estimated AUC was 0.43 [95% confidence interval (CI); 0.32-0.54] for Cobo's tool and 0.42 (95% CI; 0.28-0.56) for Larrainzar-Coghen's tool.

CONCLUSION

Performance of both prediction tools was disappointing in the external validation cohort. Currently identified clinical risk factors may not be sufficient for accurate prediction of rCDI.

Real-life experience with fidaxomicin in Clostridioides difficile infection: a multicentre cohort study on 244 episodes

The high cost of fidaxomicin has restricted its use despite the benefit of a lower Clostridioides difficile infection (CDI) recurrence rate at 4 weeks of follow-up. This short follow-up represents the main limitation of pivotal clinical trials of fidaxomicin, and some recent studies question its benefits over vancomycin. Moreover, the main risk factors of recurrence after treatment with fidaxomicin remain unknown. We designed a multicentre retrospective cohort study among four Spanish hospitals to assess the efficacy of fidaxomicin in real life and to investigate risk factors of fidaxomicin failure at weeks 8 and 12. Two-hundred forty-four patients were included. Fidaxomicin was used in 96 patients (39.3%) for a first episode of CDI, in 95 patients (38.9%) for a second episode, and in 53 patients (21.7%) for a third or subsequent episode. Patients treated with fidaxomicin in a first episode were younger (59.9 years vs 73.5 years), but they had more severe episodes (52.1% vs. 32.4%). The recurrence rates for patients treated in the first episode were 6.5% and 9.7% at weeks 8 and 12, respectively. Recurrence rates increased for patients treated at second or ulterior episodes (16.3% and 26.4% at week 8, respectively). Age greater than or equal to 85 years and having had a previous episode of CDI were identified as recurrence risk factors at weeks 8 and 12. We conclude that the outcomes with fidaxomicin in real life are at least as good as those observed in clinical trials despite a more demanding evaluation. Be it 85 years of age or older, and the use after a first episode appears to be independent factors of CDI recurrence after treatment with fidaxomicin.

[Updated review of Clostridium difficile infection in elderly]

Clostridium difficile infection is the most common cause of health care-associated diarrhoea, and its incidence increases with age. Clinical challenges, risk of resistance to treatment, risk of recurrence, and treatment responses are different in elderly. The aim of this review is to discuss the updated epidemiology, pathophysiology, diagnosis, and therapeutic management of C. difficile infection in elderly with the available data.

Clinical Predictors of Recurrence After Primary Clostridioides difficile Infection: A Prospective Cohort Study

BACKGROUND

Recurrent Clostridioides difficile infection (CDI) is a major public health threat. While clinical prediction tools exist, they do not incorporate the newest Infectious Diseases Society of America guidelines.

METHODS

This was a prospective longitudinal study of patients experiencing their first episode of uncomplicated CDI. Patients were followed from diagnosis through 8 weeks post-completion of their anti-CDI therapy to assess recurrence. Stool was collected at diagnosis and weekly for 8 weeks following treatment. Recurrence was defined as diarrhea as well as a positive stool test by toxin EIA (EIA) for C. difficile. Fisher's exact test for binary variables and Student's t test for continuous variables were performed. Cox regression was performed to assess for predictors of CDI recurrence.

RESULTS

Seventy-five patients were enrolled between August 1, 2015, and September 1, 2018. Mean age 58.1 years ± 15.5, 69.3% female, 74.7% were white, 11.3% had baseline irritable bowel syndrome, and 54.7% were actively using PPIs. Over the 8-week follow-up period, 22 patients developed a confirmed CDI recurrence. Univariate predictors of recurrence included treatment with metronidazole (40.9% vs 15.1%, p = 0.03), initially diagnosis by EIA (77.3% vs 43.4%, p = 0.007) and platelet count (206 ± 72.1 vs 270.9 ± 114.8, p = 0.03). A Cox regression model revealed primary diagnosis by EIA (HR 3.39, 95% CI 1.23, 9.31, p = 0.018) and treatment with metronidazole (HR 3.27 95% CI 1.31-8.19, p = 0.01) remain predictors for CDI recurrence.

CONCLUSION

In a large prospective longitudinal cohort of uncomplicated CDI patients, treatment with metronidazole and diagnosis via EIA were the most robust predictors of CDI recurrence.

Recommendations for the diagnosis and treatment of Clostridioides difficile infection: An official clinical practice guideline of the Spanish Society of Chemotherapy (SEQ), Spanish Society of Internal Medicine (SEMI) and the working group of Postoperative Infection of the Spanish Society of Anesthesia and Reanimation (SEDAR)

This document gathers the opinion of a multidisciplinary forum of experts on different aspects of the diagnosis and treatment of Clostridioides difficile infection (CDI) in Spain. It has been structured around a series of questions that the attendees considered relevant and in which a consensus opinion was reached. The main messages were as follows: CDI should be suspected in patients older than 2 years of age in the presence of diarrhea, paralytic ileus and unexplained leukocytosis, even in the absence of classical risk factors. With a few exceptions, a single stool sample is sufficient for diagnosis, which can be sent to the laboratory with or without transportation media for enteropathogenic bacteria. In the absence of diarrhoea, rectal swabs may be valid. The microbiology laboratory should include C. difficile among the pathogens routinely searched in patients with diarrhoea. Laboratory tests in different order and sequence schemes include GDH detection, presence of toxins, molecular tests and toxigenic culture. Immediate determination of sensitivity to drugs such as vancomycin, metronidazole or fidaxomycin is not required. The evolution of toxin persistence is not a suitable test for follow up. Laboratory diagnosis of CDI should be rapid and results reported and interpreted to clinicians immediately. In addition to the basic support of all diarrheic episodes, CDI treatment requires the suppression of antiperistaltic agents, proton pump inhibitors and antibiotics, where possible. Oral vancomycin and fidaxomycin are the antibacterials of choice in treatment, intravenous metronidazole being restricted for patients in whom the presence of the above drugs in the intestinal lumen cannot be assured. Fecal material transplantation is the treatment of choice for patients with multiple recurrences but uncertainties persist regarding its standardization and safety. Bezlotoxumab is a monoclonal antibody to C. difficile toxin B that should be administered to patients at high risk of recurrence. Surgery is becoming less and less necessary and prevention with vaccines is under research. Probiotics have so far not been shown to be therapeutically or preventively effective. The therapeutic strategy should be based, rather than on the number of episodes, on the severity of the episodes and on their potential to recur. Some data point to the efficacy of oral vancomycin prophylaxis in patients who reccur CDI when systemic antibiotics are required again.

Recurrent community-acquired Clostridium(Clostridioides)difficile infection in Serbianchildren

Information on recurrent Clostridium difficile infections (rCDI) in children is rare and limited, especially community acquired (CA-CDI).This study was designed to identify risk factors for rCA-CDI in Serbian pediatric population. The study group included 71 children (aged from 1 to 14 years) with a first episode of CDI. Data were collected from 56 (78.87%) children with only one episode of CA-CDI and from 15 (21.13%) children with rCA-CDI were mutually compared. The following parameters were found to be statistically significantly more frequent in the children with rCA-CDI group (p < 0.05); leukemia as underlying disease, treatment with immunosuppressive and-or cytostatic drugs, and treatment with antibiotics. Similarly, previously visits to outpatient facilities, daycare hospitals and hospitals were also associated with rCDI. Analysis of clinical symptoms and laboratory parameters, revealed a statistically significant association of the severity of the first episode of CDI (determined by an increase in body temperature, higher maximum WBC and higher CRP) with development of a rCDI. Ribotype (RT) 027 was more common in children with rCA-CDI (66.7%, p = 0.006). During the seven-year research period, we found a rate of rCA-CDI rate in children of 21.13%. Our study identified several parameters statistically significantly more frequently in children with rCA-CDI. The obtained results will serve as a basis for future larger studies, but new prospective, studies are necessary to build a prediction model of rCDI in children that can be used to guide the treatment to prevent rCDI.

Follow-on RifAximin for the Prevention of recurrence following standard treatment of Infection with Clostridium Difficile (RAPID): a randomised placebo controlled trial

BACKGROUND

Clostridium difficile infection (CDI) recurs after initial treatment in approximately one in four patients. A single-centre pilot study suggested that this could be reduced using 'follow-on' rifaximin treatment. We aimed to assess the efficacy of rifaximin treatment in preventing recurrence.

METHODS

A multisite, parallel group, randomised, placebo controlled trial recruiting patients aged ≥18 years immediately after resolution of CDI through treatment with metronidazole or vancomycin. Participants received either rifaximin 400 mg three times a day for 2 weeks, reduced to 200 mg three times a day for a further 2 weeks or identical placebo. The primary endpoint was recurrence of CDI within 12 weeks of trial entry.

RESULTS

Between December 2012 and March 2016, 151 participants were randomised to either rifaximin or placebo. Primary outcome data were available on 130. Mean age was 71.9 years (SD 15.3). Recurrence within 12 weeks was 29.5% (18/61) among participants allocated to placebo compared with 15.9% (11/69) among those allocated to rifaximin, a difference between groups of 13.7% (95% CI -28.1% to 0.7%, p=0.06). The risk ratio was 0.54 (95% CI 0.28 to 1.05, p=0.07). During 6-month safety follow-up, nine participants died in each group (12%). Adverse event rates were similar between groups.

CONCLUSION

While 'follow-on' rifaximin after CDI appeared to halve recurrence rate, we failed to reach our recruitment target in this group of frail elderly patients, so the estimated effect of rifaximin lacks precision. A meta-analysis including a previous trial suggests that rifaximin may be effective; however, further, larger confirmatory studies are needed.

Using Machine Learning and the Electronic Health Record to Predict Complicated Clostridium difficile Infection

Background

Clostridium (Clostridioides) difficile infection (CDI) is a health care–associated infection that can lead to serious complications. Potential complications include intensive care unit (ICU) admission, development of toxic megacolon, need for colectomy, and death. However, identifying the patients most likely to develop complicated CDI is challenging. To this end, we explored the utility of a machine learning (ML) approach for patient risk stratification for complications using electronic health record (EHR) data.

Methods

We considered adult patients diagnosed with CDI between October 2010 and January 2013 at the University of Michigan hospitals. Cases were labeled complicated if the infection resulted in ICU admission, colectomy, or 30-day mortality. Leveraging EHR data, we trained a model to predict subsequent complications on each of the 3 days after diagnosis. We compared our EHR-based model to one based on a small set of manually curated features. We evaluated model performance using a held-out data set in terms of the area under the receiver operating characteristic curve (AUROC).

Results

Of 1118 cases of CDI, 8% became complicated. On the day of diagnosis, the model achieved an AUROC of 0.69 (95% confidence interval [CI], 0.55–0.83). Using data extracted 2 days after CDI diagnosis, performance increased (AUROC, 0.90; 95% CI, 0.83–0.95), outperforming a model based on a curated set of features (AUROC, 0.84; 95% CI, 0.75–0.91).

Conclusions

Using EHR data, we can accurately stratify CDI cases according to their risk of developing complications. Such an approach could be used to guide future clinical studies investigating interventions that could prevent or mitigate complicated CDI.

High delayed mortality after the first episode of Clostridium difficile infection

Clostridium difficile infection (CDI) is characterized by a high delayed and unrelated mortality. Predicting delayed mortality in CDI patients could allow the implementation of interventions that could reduce these events. A prospective multicentric study was carried out to investigate prognostic factors associated with mortality. It was based on a cohort (July 2015 to February 2016) of 295 patients presenting with CDI. Logistic regression was used and the model was calibrated using the Hosmer-Lemeshow test. The mortality rate at 75 days in our series was 18%. Age (>65 years), comorbidity (defined by heart failure, diabetes mellitus with any organ lesion, renal failure, active neoplasia or immunosuppression) and fecal incontinence at clinical presentation were associated with delayed (75-day) mortality. When present, each of the aforementioned variables added one point to the score. Mortalities with 0, 1, 2 and 3 points were 0%, 9.4%, 18.5% and 38.2%, respectively. The area under the ROC curve was 0.743, and the Hosmer-Lemeshow goodness-of-fit test p value was 0.875. Therefore, the prediction of high delayed mortality in CDI patients by our scoring system could promote measures for increasing survival in suitable cases.

Toxin B PCR Amplification Cycle Threshold Adds Little to Clinical Variables for Predicting Outcomes in Clostridium difficile Infection: a Retrospective Cohort Study

The objective of the present study was to evaluate the value of the PCR cycle threshold (C_T ) for predicting the recurrence/severity of infection compared to that of toxin detection plus clinical variables. First episodes of Clostridium difficile infection (CDI) diagnosed during 2015 at our institution were included. Samples were tested for glutamate dehydrogenase (GDH) and toxin A/B by use of a single enzyme immunoassay (EIA). The Xpert C. difficile PCR assay was performed on GDH-positive samples. Medical data were reviewed by investigators blinded to diagnostic results for comparison of patients with and without recurrence or a poor outcome (severe/severe-complicated CDI episodes and all-cause death). We generated two sets of predictive models by incorporating the presence of a positive toxin EIA ("EIA-including model") or the optimal PCR C_T cutoff value ("PCR-including model") into the clinical variables. Among 227 episodes of CDI included in the study, the rates of recurrence and poor outcome were 15.8% and 30.8%, respectively. The mean PCR C_T was lower for episodes with recurrence (24.00 ± 3.28 versus 26.02 ± 4.54; P = 0.002) or a poor outcome (24.9 ± 4.24 versus 26.05 ± 4.47; P = 0.07). The optimal cutoff value for recurrence was 25.65 (sensitivity, 77.8% [95% confidence interval {CI}, 60.9 to 89.9]; and specificity, 46.6% [95% CI, 39.4 to 53.9]). The area under the receiver operator characteristics curve (auROC) for the "PCR-including model" was similar to that for the "EIA-including model" (0.785 versus 0.775, respectively). The optimal PCR C_T value for poor outcome was 27.55 (sensitivity, 78.6% [95% CI, 67.1 to 87.5]; and specificity, 35.7% [95% CI, 28.2 to 43.7]). The auROC of the "PCR-including model" was again similar to that of the "EIA-including model" (0.804 versus 0.801). Despite the inverse correlation between PCR C_T and the risk of CDI recurrence/severity, this determination does not meaningfully increase the predictive value of clinical variables plus toxin EIA.

Clinical review of Clostridium difficile infection: an update on treatment and prevention

INTRODUCTION

Clostridium difficile infection (CDI) has become a significant healthcare-associated infection and is strongly associated with antibiotic use. Practice guidelines have recently been revised incorporating updated recommendations for diagnosis, treatment, and prevention.

AREAS COVERED

This review discusses updated aspects of CDI management. New and emerging pharmacologic options for treatment and prevention are reviewed.

EXPERT OPINION

Metronidazole is associated with lower rates of treatment success compared to vancomycin and should no longer be used as primary therapy for the first episode of CDI or recurrent disease. Vancomycin or fidaxomicin are now recommended for first-line therapy for most cases of CDI. Fecal microbiota transplant is effective and safe for the treatment of recurrent CDI. Evidence supports the use of fidaxomicin and bezlotoxumab for prevention of recurrent CDI; however, the costs associated with these therapies may limit their use. Validated risk prediction tools are needed to identify patients most likely to benefit from these treatments. Future advancements in microbiota targeting treatments will emerge as promising alternatives to standard CDI treatments. Antibiotic stewardship and infection control measures will remain essential components for CDI management.

A comprehensive approach for the patient with Clostridium difficile infection

During the last decade there have been many changes and advances in the research on Clostridium difficile infection (CDI). We have improved diagnostic and therapeutic tools and, at the same time, we have learned that the CDI implies, especially in the most vulnerable patients, an important morbidity. CDI has traditionally been undervalued and it is widely dispersed in hospitals. Surely, there is inertness in its management and there are also broad areas of improvement. If we add to this the high cost of the new drugs and the practical difficulties to implement the faecal microbiota transplant, we realize that we may not be taking full advantage of all the opportunities to improve patient's outcomes. The implementation of policies that favour the supervision of all CDI cases by an expert in infectious diseases will contribute to a better global management of this important disease.

Assessment of urinary 3-indoxyl sulfate as a marker for gut microbiota diversity and abundance of Clostridiales

OBJECTIVES

After allogeneic hematopoietic stem cell transplantation (allo-HCT), urinary levels of 3-indoxyl sulfate (3-IS) correlate with the relative abundance of bacteria from the class Clostridia (RAC), and antibiotic treatment is considered the major determinant of this outcome. A high RAC has been associated with favorable outcome after allo-HCT and protection from Clostridium difficile infection (CDI). We assessed correlations between alpha diversity, RAC and urinary 3-IS levels in a non-allo-HCT clinical cohort of antibiotic treated patients to further explore 3-IS as a biomarker of reduced diversity and predisposition to CDI.

METHODS

Fecal and urinary specimens were analyzed from 40 non-allo-HCT hospitalized patients before and 9 ± 2 days after initiation of intravenous antibiotic treatment. Fecal microbiota were analyzed by 16s RNA sequencing and urinary 3-IS was analyzed by liquid chromatography-tandem mass spectrometry. Receiver operating characteristic (ROC) analysis was performed to assess the predictive value of 3-IS.

RESULTS

At a RAC cutoff of <30%, the binary logarithm of 3-IS (medium 3-IS: ≤2.5; high 3-IS: >2.5) was predictive with an accuracy of 82% (negative predictive value: 87%, positive predictive value 67%). Accuracy was improved by combing antibiotic history with 3-IS levels (accuracy 89%, npv 88%, ppv 92%).

CONCLUSION

In conjunction with patient antibiotic history, 3-IS is a candidate marker to predict RAC.

An Observational Cohort Study of Clostridium difficile Ribotype 027 and Recurrent Infection

CDI is a major public health issue, with over 400,000 cases per year in the United States alone. Recurrent CDI is common, occurring in approximately one in five individuals after a primary episode. Although interventions exist that could reduce the risk of recurrence, deployment in all patients is limited by cost, invasiveness, and/or an undetermined long-term safety profile. Thus, clinicians need risk stratification tools to properly allocate treatments. Because prior research on clinical predictors has failed to yield a reliable, reproducible, and effective predictive model to assist treatment decisions, accurate biomarkers of recurrence would be of great value. This study tested whether PCR ribotype independently predicted rCDI, and the data build upon prior research in showing that ribotype 027 is associated with rCDI.

Recurrent Clostridium difficile infection (rCDI) frequently complicates recovery from CDI. Accurately predicting rCDI would allow judicious allocation of limited resources, but published models have met with limited success. Thus, biomarkers predictive of recurrence have been sought. This study tested whether PCR ribotype independently predicted rCDI. Stool samples from nonpregnant inpatients ≥18 years of age with diarrhea were included from October 2010 to January 2013 after the patients tested positive for C. difficile in the clinical microbiology laboratory. Per guidelines, the rCDI was defined as a positive test for C. difficile at >2 weeks but ≤8 weeks from the index episode. For each sample, a single colony of C. difficile was isolated by anaerobic culture, confirmed to be toxigenic by PCR, and ribotyped. Simple logistic regression and multiple logistic regression were used to model the primary outcome of rCDI, incorporating a wide range of clinical parameters. In total, 927 patients with 968 index episodes of CDI were included, with 110 (11.4%) developing rCDI. Age and use of proton pump inhibitors or concurrent antibiotics did not increase the risk of rCDI. Low serum bilirubin levels and ribotype 027 were associated with increased risk of rCDI on unadjusted analysis, with health care-associated CDI being inversely associated. In the final multivariable model, ribotype 027 was the strongest independent predictor of rCDI (odds ratio, 2.17; 95% confidence interval, 1.33 to 3.56; P = 0.002). Ribotype 027 is an independent predictor of rCDI.

IMPORTANCE CDI is a major public health issue, with over 400,000 cases per year in the United States alone. Recurrent CDI is common, occurring in approximately one in five individuals after a primary episode. Although interventions exist that could reduce the risk of recurrence, deployment in all patients is limited by cost, invasiveness, and/or an undetermined long-term safety profile. Thus, clinicians need risk stratification tools to properly allocate treatments. Because prior research on clinical predictors has failed to yield a reliable, reproducible, and effective predictive model to assist treatment decisions, accurate biomarkers of recurrence would be of great value. This study tested whether PCR ribotype independently predicted rCDI, and the data build upon prior research in showing that ribotype 027 is associated with rCDI.