A prediction model for Clostridium difficile recurrence

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.

Machine Learning-Based Prediction Models for Clostridioides difficile Infection: A Systematic Review

INTRODUCTION

Despite research efforts, predicting Clostridioides difficile incidence and its outcomes remains challenging. The aim of this systematic review was to evaluate the performance of machine learning (ML) models in predicting C. difficile infection (CDI) incidence and complications using clinical data from electronic health records.

METHODS

We conducted a comprehensive search of databases (OVID, Embase, MEDLINE ALL, Web of Science, and Scopus) from inception up to September 2023. Studies employing ML techniques for predicting CDI or its complications were included. The primary outcome was the type and performance of ML models assessed using the area under the receiver operating characteristic curve.

RESULTS

Twelve retrospective studies that evaluated CDI incidence and/or outcomes were included. The most commonly used ML models were random forest and gradient boosting. The area under the receiver operating characteristic curve ranged from 0.60 to 0.81 for predicting CDI incidence, 0.59 to 0.80 for recurrence, and 0.64 to 0.88 for predicting complications. Advanced ML models demonstrated similar performance to traditional logistic regression. However, there was notable heterogeneity in defining CDI and the different outcomes, including incidence, recurrence, and complications, and a lack of external validation in most studies.

DISCUSSION

ML models show promise in predicting CDI incidence and outcomes. However, the observed heterogeneity in CDI definitions and the lack of real-world validation highlight challenges in clinical implementation. Future research should focus on external validation and the use of standardized definitions across studies.

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.

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.

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.

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.

Neighborhood disadvantage and 30-day readmission risk following Clostridioides difficile infection hospitalization

BACKGROUND

Clostridioides difficile infection (CDI) is commonly associated with outcomes like recurrence and readmission. The effect of social determinants of health, such as 'neighborhood' socioeconomic disadvantage, on a CDI patient's health outcomes is unclear. Living in a disadvantaged neighborhood could interfere with a CDI patient's ability to follow post-discharge care recommendations and the success probability of these recommendations, thereby increasing risk of readmission. We hypothesized that neighborhood disadvantage was associated with 30-day readmission risk in Medicare patients with CDI.

METHODS

In this retrospective cohort study, odds of 30-day readmission for CDI patients are evaluated controlling for patient sociodemographics, comorbidities, and hospital and stay-level variables. The cohort was created from a random 20% national sample of Medicare patients during the first 11 months of 2014.

RESULTS

From the cohort of 19,490 patients (39% male; 80% white; 83% 65 years or older), 22% were readmitted within 30 days of an index stay. Unadjusted analyses showed that patients from the most disadvantaged neighborhoods were readmitted at a higher rate than those from less disadvantaged neighborhoods (26% vs. 21% rate: unadjusted OR = 1.32 [1.20, 1.45]). This relationship held in adjusted analyses, in which residence in the most disadvantaged neighborhoods was associated with 16% increased odds of readmission (adjusted OR = 1.16 [1.04, 1.28]).

CONCLUSIONS

Residence in disadvantaged neighborhoods poses a significantly increased risk of readmission in CDI patients. Further research should focus on in-depth assessments of this population to better understand the mechanisms underlying these risks and if these findings apply to other infectious diseases.

Healthcare resource utilization and direct medical costs associated with index and recurrent Clostridioides difficile infection: a real-world data analysis

Aims: This study aimed to evaluate all-cause economic outcomes, healthcare resource utilization (HRU), and costs in patients with Clostridioides difficile infection (CDI) and recurrent CDI (rCDI) using commercial claims from a large database representing various healthcare settings.Materials and methods: A retrospective analysis of commercial claims data from the IQVIA PharMetrics Plus database was conducted for patients aged 18-64 years with CDI episodes requiring inpatient stay with CDI diagnosis code or an outpatient medical claim for CDI plus a CDI treatment. Index CDI episodes occurred between 1 January 2010 and 30 June 2017, including only those where patients were observable 6 months before and 12 months after the index episode. Each CDI episode was followed by a 14-d claim-free period. rCDI was defined as another CDI episode within an 8-week window following the claim-free period. HRU, all-cause direct medical costs and time to rCDI were calculated over 12 months and stratified by number of rCDI episodes.Results: A total of 46,571 patients with index CDI were included. Mean time from one CDI episode to the next was approximately 1 month. In the 12-month follow-up period, those with no recurrence had 1.4 inpatient visits per person and those with 3 or more recurrences had 5.8. Most patients with 3 or more recurrences had 2 or more hospital admissions. The mean annual, total all-cause direct medical costs per patient were $71,980 for those with no recurrence and $207,733 for those with 3 or more recurrences.Limitations: The study included individuals 18-64 years only. A stringent definition of rCDI was used, which may have underestimated the incidence of rCDI.Conclusions: CDI and rCDI are associated with substantial healthcare resource utilization and direct medical costs. Timing of recurrences can be predictable, providing a window of opportunity for interventions. Prevention of multiple rCDI appears essential to reduce healthcare costs.

Predicting hospital-onset Clostridium difficile using patient mobility data: A network approach

OBJECTIVE

To examine the relationship between unit-wide Clostridium difficile infection (CDI) susceptibility and inpatient mobility and to create contagion centrality as a new predictive measure of CDI.

DESIGN

Retrospective cohort study.

METHODS

A mobility network was constructed using 2 years of patient electronic health record data for a 739-bed hospital (n = 72,636 admissions). Network centrality measures were calculated for each hospital unit (node) providing clinical context for each in terms of patient transfers between units (ie, edges). Daily unit-wide CDI susceptibility scores were calculated using logistic regression and were compared to network centrality measures to determine the relationship between unit CDI susceptibility and patient mobility.

RESULTS

Closeness centrality was a statistically significant measure associated with unit susceptibility (P < .05), highlighting the importance of incoming patient mobility in CDI prevention at the unit level. Contagion centrality (CC) was calculated using inpatient transfer rates, unit-wide susceptibility of CDI, and current hospital CDI infections. The contagion centrality measure was statistically significant (P < .05) with our outcome of hospital-onset CDI cases, and it captured the additional opportunities for transmission associated with inpatient transfers. We have used this analysis to create easily interpretable clinical tools showing this relationship as well as the risk of hospital-onset CDI in real time, and these tools can be implemented in hospital EHR systems.

CONCLUSIONS

Quantifying and visualizing the combination of inpatient transfers, unit-wide risk, and current infections help identify hospital units at risk of developing a CDI outbreak and, thus, provide clinicians and infection prevention staff with advanced warning and specific location data to inform prevention efforts.

Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications

BACKGROUND

Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID).

OBJECTIVES

We aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID.

SOURCES

References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019.

CONTENT

We found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n = 24, 40%), ID consultation (n = 15, 25%), medical or surgical wards (n = 13, 20%), emergency department (n = 4, 7%), primary care (n = 3, 5%) and antimicrobial stewardship (n = 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low- and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%).

IMPLICATIONS

Considering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.

Characterization and risk factors for recurrence of Clostridioides (Clostridium) difficile infection in Japan: A nationwide real-world analysis using a large hospital-based administrative dataset

OBJECTIVE

Recurrent Clostridioides (Clostridium) difficile infection (rCDI) is common and increases healthcare resource utilization. In this study, we assessed rCDI risk factors using an up-to-date, Japanese national hospital-based database.

METHODS

C. difficile infection (CDI) episodes, occurring July 2014-June 2017, in patients aged ≥18 years were extracted from the database and a nested case-control analysis was performed. Cases were defined as rCDI episodes which required re-initiation of oral vancomycin or oral/intravenous metronidazole treatment within 8 weeks from the start of initial treatment. Cases were matched to 4 non-rCDI episodes at the timing of rCDI occurrence. Adjusted odds ratios (ORs) were estimated using multivariate conditional logistic regression model.

RESULTS

Of 18,246 initial CDI episodes, 3250 (17.8%) had at least one rCDI. Approximately 90% of episodes occurred in inpatients and 65% were treated with metronidazole. Older age (<75 years vs 75-84 years and vs 85 + years) was associated with higher risk of rCDI (OR = 1.27, 95% confidence interval [1.15, 1.41] and 1.45 [1.30, 1.61], respectively). Use of systemic antibiotics (3.16 [2.90, 3.44]), probiotics (2.53 [2.32, 2.77]), chemotherapy (1.28 [1.08, 1.53]), or proton pump inhibitors (PPIs) (1.17 [1.07, 1.28]), and prior CDI history (1.22 [1.03, 1.43]) were also identified as rCDI risk factors. Vancomycin reduced the risk of rCDI compared with metronidazole treatment (0.83 [0.76, 0.91]).

CONCLUSION

This large, multicenter, nationwide study confirmed that older age, PPIs, antibiotics, probiotics, chemotherapy, and prior CDI history are risk factors for rCDI in Japan. There was a 17% decrease of rCDI risk with vancomycin vs metronidazole treatment.

CLINICAL TRIAL REGISTRATION NUMBER

N/A.

Evaluation of a risk score to predict future Clostridium difficile disease using UK primary care and hospital data in Clinical Practice Research Datalink

We evaluated the applicability of a Clostridium difficile infection (CDI) risk index developed for patients at hospital discharge to identify persons at high-risk of CDI in a primary care population. This retrospective observational study used data from the UK Clinical Practice Research Datalink, linked with Hospital Episodes Statistics. The risk index was based on the following patient characteristics: age, previous hospitalizations, days in hospital, and prior antibiotics use. Individual risk scores were calculated by summing points assigned to pre-defined categories for each characteristic. We assessed the association of risk factors with CDI by multivariate logistic regression. The estimated CDI incidence rate was 4/10,000 and 2/10,000 person-years in 2008 and 2012, respectively. On an index with a maximal risk of 19, a cut-off for high risk of ≥7 had sensitivity, specificity and positive predictive values of 80%, 87% and 12%, respectively. A high-risk person had a ~ 35% higher risk of CDI than a low-risk person. Multivariate risk factor analysis indicated a need to reconsider the relative risk scores. The CDI risk index can be applied to the UK primary care population and help identify study populations for vaccine development studies. Reassessing the relative weights assigned to risk factors could improve the index performance in this setting.

2019 update of the WSES guidelines for management of Clostridioides (Clostridium) difficile infection in surgical patients

In the last three decades, Clostridium difficile infection (CDI) has increased in incidence and severity in many countries worldwide. The increase in CDI incidence has been particularly apparent among surgical patients. Therefore, prevention of CDI and optimization of management in the surgical patient are paramount. An international multidisciplinary panel of experts from the World Society of Emergency Surgery (WSES) updated its guidelines for management of CDI in surgical patients according to the most recent available literature. The update includes recent changes introduced in the management of this infection.

2019 update of the WSES guidelines for management of Clostridioides (Clostridium) difficile infection in surgical patients

In the last three decades, Clostridium difficile infection (CDI) has increased in incidence and severity in many countries worldwide. The increase in CDI incidence has been particularly apparent among surgical patients. Therefore, prevention of CDI and optimization of management in the surgical patient are paramount. An international multidisciplinary panel of experts from the World Society of Emergency Surgery (WSES) updated its guidelines for management of CDI in surgical patients according to the most recent available literature. The update includes recent changes introduced in the management of this infection.

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.

Tackling the recurrence of Clostridium difficile infection

The pathogenesis of recurrent Clostridium difficile infection (CDI) is still poorly understood. The risk of recurrence is approximately 20% after an initial CDI episode and dramatically increases with subsequent CDI recurrences. Several factors may play a role in recurrent CDI (rCDI), including conditions influencing germination, metabolic pathways that influence toxin production of C. difficile, and the microbiota composition offering protection against colonization and disease caused by C. difficile. Paradoxically, the currently recommended treatment for acute symptomatic CDI, i.e. metronidazole or vancomycin, can cause modification of the intestinal flora. Indeed, administration of anti-CDI antibiotics leads to suppression of C. difficile, along with collateral damage of the protective intestinal microbiota and opening of a "window of vulnerability" for recurrence. Host factors also have a prominent role, including innate and acquired humoral immunity, i.e. passive antibodies administration or active vaccination as a prevention strategy. They play a crucial role in the protection against severe and recurrent CDI. The assessment of risk factors of recurrence and modeling prediction scores could help in preventing the troublesome experience of CDI recurrence. Six studies have methodologically assessed prediction scores for rCDI. However, the definition of recurrence was heterogeneous, external validation was often not performed, and immunological factors were often not considered. There is a need for further studies on the pathophysiology of recurrence to design models for prediction that are sound and applicable in clinical practice.

WSES guidelines for management of Clostridium difficile infection in surgical patients

In the last two decades there have been dramatic changes in the epidemiology of Clostridium difficile infection (CDI), with increases in incidence and severity of disease in many countries worldwide. The incidence of CDI has also increased in surgical patients. Optimization of management of C difficile, has therefore become increasingly urgent. An international multidisciplinary panel of experts prepared evidenced-based World Society of Emergency Surgery (WSES) guidelines for management of CDI in surgical patients.