Derivation and validation of a simple, accurate and robust prediction rule for risk of mortality in patients with Clostridium difficile infection

The Gut Bacterial Community Potentiates Clostridioides difficile Infection Severity

The severity of Clostridioides difficile infections (CDI) has increased over the last few decades. Patient age, white blood cell count, and creatinine levels as well as C. difficile ribotype and toxin genes have been associated with disease severity. However, it is unclear whether specific members of the gut microbiota are associated with variations in disease severity. The gut microbiota is known to interact with C. difficile during infection. Perturbations to the gut microbiota are necessary for C. difficile to colonize the gut. The gut microbiota can inhibit C. difficile colonization through bile acid metabolism, nutrient consumption, and bacteriocin production. Here, we sought to demonstrate that members of the gut bacterial communities can also contribute to disease severity. We derived diverse gut communities by colonizing germfree mice with different human fecal communities. The mice were then infected with a single C. difficile ribotype 027 clinical isolate, which resulted in moribundity and histopathologic differences. The variation in severity was associated with the human fecal community that the mice received. Generally, bacterial populations with pathogenic potential, such as Enterococcus, Helicobacter, and Klebsiella, were associated with more-severe outcomes. Bacterial groups associated with fiber degradation and bile acid metabolism, such as Anaerotignum, Blautia, Lactonifactor, and Monoglobus, were associated with less-severe outcomes. These data indicate that, in addition to the host and C. difficile subtype, populations of gut bacteria can influence CDI disease severity.

Validation of Clinical Risk Models for Clostridioides difficile-Attributable Outcomes

Clostridioides difficile is the leading health care-associated pathogen, leading to substantial morbidity and mortality; however, there is no widely accepted model to predict C. difficile infection severity. Most currently available models perform poorly or were calibrated to predict outcomes that are not clinically relevant. We sought to validate six of the leading risk models (Age Treatment Leukocyte Albumin Serum Creatinine (ATLAS), C. difficile Disease (CDD), Zar, Hensgens, Shivashankar, and C. difficile Severity Score (CDSS)), guideline severity criteria, and PCR cycle threshold for predicting C. difficile-attributable severe outcomes (inpatient mortality, colectomy/ileostomy, or intensive care due to sepsis). Models were calculated using electronic data available within ±48 h of diagnosis (unavailable laboratory measurements assigned zero points), calibrated using a large retrospective cohort of 3,327 inpatient infections spanning 10 years, and compared using receiver operating characteristic (ROC) and precision-recall curves. ATLAS achieved the highest area under the ROC curve (AuROC) of 0.781, significantly better than the next best performing model (Zar 0.745; 95% confidence interval of AuROC difference 0.0094–0.6222; P = 0.008), and highest area under the precision-recall curve of 0.232. Current IDSA/SHEA severity criteria demonstrated moderate performance (AuROC 0.738) and PCR cycle threshold performed the worst (0.531). The overall predictive value for all models was low, with a maximum positive predictive value of 37.9% (ATLAS cutoff ≥9). No clinical model performed well on external validation, but ATLAS did outperform other models for predicting clinically relevant C. difficile-attributable outcomes at diagnosis. Novel markers should be pursued to augment or replace underperforming clinical-only models.

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 and Comparison of Clostridioides difficile Severity Scoring Systems

BACKGROUND

Many models have been developed to predict severe outcomes from Clostridioides difficile infection. These models are usually developed at a single institution and largely are not externally validated. This aim of this study was to validate previously published risk scores in a multicenter cohort of patients with CDI.

METHODS

Retrospective study on four separate inpatient cohorts with CDI from three distinct sites: The Universities of Michigan (2010-2012 and 2016), Chicago (2012), and Wisconsin (2012). The primary composite outcome was admission to an intensive care unit, colectomy, and/or death attributed to CDI within 30 days of positive testing. Both within each cohort and combined across all cohorts, published CDI severity scores were assessed and compared to each other and the IDSA guideline definitions of severe and fulminant CDI.

RESULTS

A total of 3,646 patients were included for analysis. Including the two IDSA guideline definitions, fourteen scores were assessed. Performance of scores varied within each cohort and in the combined set (mean area under the receiver operator characteristic curve(AUC 0.61, range 0.53-0.66). Only half of the scores had performance at or better than IDSA severe and fulminant definitions (AUCs 0.64 and 0.63, respectively). Most of the scoring systems had more false than true positives in the combined set (mean: 81.5%, range:0-91.5%).

CONCLUSIONS

No published CDI severity score showed stable, good predictive ability for adverse outcomes across multiple cohorts/institutions or in a combined multicenter cohort.

ACG Clinical Guidelines: Prevention, Diagnosis, and Treatment of Clostridioides difficile Infections

Clostridioides difficile infection occurs when the bacterium produces toxin that causes diarrhea and inflammation of the colon. These guidelines indicate the preferred approach to the management of adults with C. difficile infection and represent the official practice recommendations of the American College of Gastroenterology. The scientific evidence for these guidelines was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation process. In instances where the evidence was not appropriate for Grading of Recommendations Assessment, Development, and Evaluation but there was consensus of significant clinical merit, key concept statements were developed using expert consensus. These guidelines are meant to be broadly applicable and should be viewed as the preferred, but not the only, approach to clinical scenarios.

Laboratory markers predictive of fulminant Clostridioides difficile infection refractory to fluid resuscitation

Background

Old age, leucocytosis, hypoalbuminemia, and elevated creatinine have been identified as risk factors for fulminant Clostridioides difficile infection (CDI). High ATLAS scores have also been linked to fatal disease. The affiliated studies, however, involved patients prescribed metronidazole - a regimen no longer standard of care. The variables were thus reassessed in patients prescribed optimal therapy.

Methods

Adults hospitalized with CDI at University of Kentucky Medical Center were retrospectively reviewed. Enrolled subjects were separated according to disease classification i.e. non-severe/severe versus fulminant CDI. Fulminant patients were further subdivided into hypotensive persons responsive to fluid resuscitation, and those with sequent shock, ileus, or megacolon. Following partition, the cohorts underwent correlation analysis.

Findings

Forty-five subjects had non-severe/severe disease. Thirteen fulminant CDI patients responded to fluid resuscitation. Seventeen fulminant CDI patients developed shock, ileus, or megacolon. Median WBC counts, albumin values, and ATLAS scores varied among the cohorts. Although WBC counts were similar among the fulminant subsets, declining albumin values and increasing ATLAS scores mirrored disease worsening. Logistic regression revealed albumin values < 20 g/L (odds ratio [OR] 3.91) and ATLAS scores ≥ 6 (OR 5.03) to predict critical illness in hypotensive persons.

Conclusion

Median WBC counts, albumin values, and ATLAS scores differed in patients separated by CDI severity. A notable variance in albumin values and ATLAS scores between fluid responsive fulminant disease and critical illness was moreover seen. The finding suggests hypoalbuminemia and high ATLAS scores in hypotensive CDI patients may herald shock, ileus, or megacolon.

Validation of a Clinical Risk-scoring Algorithm for Scrub Typhus Severity in South India

Background

A clinical risk-scoring algorithm (CRSA) to forecast the scrub typhus severity was developed from two general hospitals in Thailand where patients were classified into three groups-nonsevere, severe, and fatal. In this study, an attempt was made to validate the risk-scoring algorithm for prognostication of scrub typhus severity in India.

Materials and methods

This prospective study was conducted at a hospital in South India between November 2017 and March 2019. Patients of scrub typhus were categorized into nonsevere, severe, and fatal according to the CRSA. The patients were also grouped into severe and nonsevere according to the definition of severe scrub typhus which was used as a gold standard. The obtained CRSA score was validated against the classification based on the definition of severe scrub typhus. Receiver operating characteristics (ROC) curve for the scores was plotted and the Youden's index for optimal cutoff was used.

Results

A total of 198 confirmed cases of scrub typhus were included in the study. According to the ROC curve, at a severity score ≥7, an optimal combination of sensitivity of 75.9% and specificity of 77.5% was achieved. It correctly predicted 76.77% (152 of 198) of patients as severe, with an underestimation of 10.61% (21 patients) and an overestimation of 12.63% (25 patients).

Conclusion

In the present study setting, a cutoff of ≥7 for severity prediction provides an optimum combination of sensitivity and specificity. These findings need to be validated in further studies.

How to cite this article

Gulati S, Chunduru K, Madiyal M, Setia MS, Saravu K. Validation of a Clinical Risk-scoring Algorithm for Scrub Typhus Severity in South India. Indian J Crit Care Med 2021;25(5):551-556.

Systematic review on the definition and predictors of severe Clostridiodes difficile infection

Clostridiodes difficile infection (CDI) is one of the most common hospital-acquired infections with high mortality rates. Optimal management of CDI depends on early recognition of severity. However, currently, there is no acceptable standard of prediction. We reviewed severe CDI predictors in published literature and its definition according to clinical guidelines. We systematically reviewed studies describing clinical predictors for severe CDI in medical databases (Cochrane, EMBASE, Global Health Library, and MEDLINE/PubMed). They were independently evaluated by two reviewers. Six hundred thirty-three titles and abstracts were screened, and 31 studies were included. We excluded studies that were restricted to a specific patient population. There were 16 articles that examined mortality in CDI, as compared with 15 articles investigating non-mortality outcomes of CDI. The commonest risk factors identified were comorbidities, white blood cell count, serum albumin level, age, serum creatinine level and intensive care unit admission. Generally, the studies had small patient populations, were retrospective in nature, and mostly from Western centers. The commonest severe CDI criteria in clinical guidelines were raised white blood cell count, followed by low serum albumin and raised serum creatinine levels. There was no commonly agreed upon definition of severe CDI severity in the literature. Current clinical guidelines' definitions for severe CDI are heterogeneous. Hence, there is a need for prospective multi-center studies using standardized protocol for biospecimen investigation collection and shared data on outcomes of patients in order to devise a universally accepted definition for severe CDI.

Bezlotoxumab for Preventing Recurrent Clostridioides difficile Infection: A Narrative Review from Pathophysiology to Clinical Studies

Clostridioides difficile infection (CDI) and recurrent CDI (rCDI) remain associated with a reduction in the patients’ quality of life and with increased healthcare costs. Bezlotoxumab is a monoclonal antibody against toxin B of C. difficile, approved for prevention of rCDI. In this narrative review, we briefly discuss the pathophysiology of CDI and the mechanism of action of bezlotoxumab, as well as the available evidence from investigational and observational studies in terms of efficacy, effectiveness, and safety of bezlotoxumab for the prevention of rCDI. Overall, bezlotoxumab has proved efficacious in reducing the burden of rCDI, thereby providing clinicians with an important novel strategy to achieve sustained cure. Nonetheless, experiences outside randomized controlled trials (RCTs) remain scant, and mostly represented by case series without a control group. Along with the conduction of RCTs to directly compare bezlotoxumab with faecal microbiota transplantation (or to precisely evaluate the role of their combined use), further widening our post-marketing experience remains paramount to firmly guide the use of bezlotoxumab outside RCTs, and to clearly identify those real-life settings where its preventive benefits can be exploited most.

Systemic Inflammatory Mediators Are Effective Biomarkers for Predicting Adverse Outcomes in Clostridioides difficile Infection

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.

Nonalcoholic fatty liver disease as a risk factor for Clostridium difficile-associated diarrhea

AIMS

Clostridium difficile is the most common cause of infectious nosocomial diarrhea among adults in developed countries. Nonalcoholic fatty liver disease (NAFLD) is considered the most common chronic liver disease and it is associated with bacterial infections. Our goal was to assess whether NAFLD considered a risk factor for C. difficile-associated diarrhea (CDAD).

METHODS

We conducted a retrospective study of patients admitted with CDAD at Baruch Padeh Medical Center, Poria, Israel during a period of four years. Data on demographic characteristics, clinical signs, underlying conditions, presence of fatty liver based on computed tomography/ultrasonography imaging and several risk factors for CDI were collected. The control group included patients with diarrhea who were negative for CDT and had been hospitalized during the same period. The controls were matched for age (±5 years) and gender.

RESULTS

Totally, 115/164 patients with CDAD met the inclusion criteria. The control group was consisted of 115 hospitalized patients with non-CDAD. The mean age of all the participants (230) was 69.57 ± 18 years. NAFLD was found in 76/115 (66%) patients with CDAD vs. 35/115 (30.4%) in the control group, P < 0.001. Moreover, we found significant associations between CDAD group and metabolic syndrome, prior use of antibiotic in the last 3 months, NAFLD and high serum levels of C-reactive protein. Multivariate analysis showed that NAFLD, odds ratio 1.51, 95% confidence interval 1.2-1.95, P = 0.05 was significantly associated with CDAD.

CONCLUSIONS

This retrospective study showed that NAFLD is a risk factor for CDAD. Moreover, metabolic syndrome and high serum levels of C-reactive protein were significantly associated with the risk of CDAD.

External validation of clinical prediction rules for complications and mortality following Clostridioides difficile infection

Background

Several clinical prediction rules (CPRs) for complications and mortality of Clostridioides difficile infection (CDI) have been developed but only a few have gone through external validation, and none is widely recommended in clinical practice.

Methods

CPRs were identified through a systematic review. We included studies that predicted severe or complicated CDI (cCDI) and mortality, reported at least an internal validation step, and for which data were available with minimal modifications. Data from a multicenter prospective cohort of 1380 adults with confirmed CDI were used for external validation. In this cohort, cCDI occurred in 8% of the patients and 30-day all-cause mortality occurred in 12%. The performance of each tool was assessed using individual outcomes, with the same cut-offs and standard parameters.

Results

Seven CPRs were assessed. Three predictive scores for cCDI showed low sensitivity (25–61%) and positive predictive value (PPV; 9–31%), but moderate specificity (54–90%) and negative predictive value (NPV; 82–95%). One model [using age, white blood cell count (WBC), narcotic use, antacids use, and creatinine ratio > 1.5× the normal level as covariates] showed a probability of 25% of cCDI at the optimal cut-off point with 36% sensitivity and 84% specificity. Two scores for mortality had low sensitivity (4–55%) and PPV (25–31%), and moderate specificity (71–78%) and NPV (87–92%). One predictive model for 30-day all-cause mortality [Charlson comorbidity index, WBC, blood urea nitrogen (BUN), diagnosis in ICU, and delirium] showed an AUC-ROC of 0.74. All other CPRs showed lower AUC values (0.63–0.69). Errors in calibration ranged from 12%- 27%.

Conclusions

Included CPRs showed moderate performance for clinical use in a large validation cohort with a majority of patients infected with ribotype 027 strains and a low rate of cCDI and mortality. These data show that better CPRs need to be developed and validated.

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.

Clostridium difficile infection in Johannesburg, South Africa

OBJECTIVES

The incidence of Clostridium difficile infection is increasing, resulting in significant in-patient morbidity and mortality. We describe the characteristics of Clostridium difficile infection in patients admitted to a tertiary Academic Hospital in Johannesburg, South Africa.

METHODS

This was a retrospective record review of 154 adult in-patients with confirmed Clostridium difficile infection undertaken between 1 January 2013 and 30 June 2014.

RESULTS

Overall, 83 (53.9%) patients were female and the median age was 39 years. The most common symptoms were watery diarrhea (92.9%) and fever (27.3%). Overall, 145 (97.9%) patients had been exposed to antibiotics in the last 30 days, 54 (35.1%) to immunosuppressives, 48 (31.2%) to gastric acid suppressants, and 65 (42.2%) patients had previous hospital admissions. Only two cases were community-acquired. In total, 73 (47.4%) patients died. There was no difference in outcomes of HIV-positive patients compared to HIV-negative patients; however, the clinical features were more marked in the HIV-negative patients. Non-survivors tended to have a greater number of severity indicators compared to the survivors.

CONCLUSION

Patients with Clostridium difficile infection in this study were younger and had a higher mortality than that reported in the Western world. Most infections were hospital-acquired.

External Validation of Three Prediction Tools for Patients at Risk of a Complicated Course of Clostridium difficile Infection: Disappointing in an Outbreak Setting

OBJECTIVE Estimating the risk of a complicated course of Clostridium difficile infection (CDI) might help doctors guide treatment. We aimed to validate 3 published prediction models: Hensgens (2014), Na (2015), and Welfare (2011). METHODS The validation cohort comprised 148 patients diagnosed with CDI between May 2013 and March 2014. During this period, 70 endemic cases of CDI occurred as well as 78 cases of CDI related to an outbreak of C. difficile ribotype 027. Model calibration and discrimination were assessed for the 3 prediction rules. RESULTS A complicated course (ie, death, colectomy, or ICU admission due to CDI) was observed in 31 patients (21%), and 23 patients (16%) died within 30 days of CDI diagnosis. The performance of all 3 prediction models was poor when applied to the total validation cohort with an estimated area under the curve (AUC) of 0.68 for the Hensgens model, 0.54 for the Na model, and 0.61 for the Welfare model. For those patients diagnosed with CDI due to non-outbreak strains, the prediction model developed by Hensgens performed the best, with an AUC of 0.78. CONCLUSION All 3 prediction models performed poorly when using our total cohort, which included CDI cases from an outbreak as well as endemic cases. The prediction model of Hensgens performed relatively well for patients diagnosed with CDI due to non-outbreak strains, and this model may be useful in endemic settings. Infect Control Hosp Epidemiol 2017;38:897-905.

Development and validation of a Clostridium difficile risk assessment tool

The incidence of Clostridium difficile infection has increased rapidly during the past decade, increasing lengths of stays in the hospital, costs, and mortality rates. To address this increased incidence, we performed a retrospective case-control study using known risk factors to develop a tool to determine which patients are at risk for infection. Multivariate analysis generated a combination of risk factors associated with development of infection including prior admission, endoscopy within 30 days, cephalosporin/fluoroquinolone use, length of stay 7 days or longer, age 65 years or older, body mass index less than 25, and albumin level less than 2.7 g/dL. A weighted scoring tool was created that predicted disease with a sensitivity of 86% and a specificity of 44% in the derivation sample, and 92% sensitivity and a specificity of 39% when applied to the validation sample. Application of this 8-item tool by nurses in multiple settings could aid in the determination of patients who are at risk, allowing prophylactic treatment, prompt isolation of patients, restricted antibiotic use, and decreased testing of low-risk patients.

Can we improve the therapy of Clostridium difficile infection in elderly patients?

BACKGROUND

Clostridium difficile infection (CDI) is becoming a serious problem predominantly in geriatric patients, who are a significant risk group. The goal of this study was to evaluate the risk factors for mortality in CDI patients and to construct a binary logistic regression model that describes the probability of mortality in geriatric patients suffering from CDI.

METHODS

In this retrospective study, we evaluated a group of 235 patients over 65 years of age with confirmed diagnoses of CDI, hospitalized at the Department of Internal Medicine, Geriatrics and General Practice, Brno, from January 2008 to December 2013. The examined group comprised 148 women (63 %) and 87 men (37 %). For the diagnosis of CDI, confirmation of A and B toxins in the patients' stool or an autopsy confirmation was crucial.

RESULTS

The impact of antibiotic therapy on the increased incidence of CDI was clearly confirmed in our study group when examining patients' histories. Other risk factors included cerebrovascular disease, dementia, the presence of pressure ulcers, and immobility. Our new model consisted of a combination of the following parameters: the number of antibiotics used (from patients' history), nutritional status (Mini Nutritional Assessment short-form test), presence of pressure ulcers, and occurrence of fever.

CONCLUSION

Our logistic regression model may predict mortality in geriatric patients suffering from CDI. This could help improve the therapeutic process.

Clostridium difficile outbreak caused by NAP1/BI/027 strain and non-027 strains in a Mexican hospital

Background

Clostridium difficile infections caused by the NAP1/B1/027 strain are more severe, difficult to treat, and frequently associated with relapses.

Methods

A case–control study was designed to examine a C. difficile infection (CDI) outbreak over a 12-month period in a Mexican hospital. The diagnosis of toxigenic CDI was confirmed by real-time polymerase chain reaction, PCR (Cepheid Xpert C. difficile/Epi).

Results

During the study period, 288 adult patients were evaluated and 79 (27.4%) patients had confirmed CDI (PCR positive). C. difficile strain NAP1/B1/027 was identified in 31 (39%) of the patients with confirmed CDI (240 controls were included). Significant risk factors for CDI included any underlying disease (p < 0.001), prior hospitalization (p < 0.001), and antibiotic (p < 0.050) or steroid (p < 0.001) use. Laboratory abnormalities included leukocytosis (p < 0.001) and low serum albumin levels (p < 0.002). Attributable mortality was 5%. Relapses occurred in 10% of patients. Risk factors for C. difficile NAP1/B1/027 strain infections included prior use of quinolones (p < 0.03).

Risk factors for CDI caused by non-027 strains included chronic cardiac disease (p < 0.05), chronic renal disease (p < 0.009), and elevated serum creatinine levels (p < 0.003). Deaths and relapses were most frequent in the 027 group (10% and 19%, respectively).

Conclusions

C. difficile NAP1/BI/027 strain and non-027 strains are established pathogens in our hospital. Accordingly, surveillance of C. difficile infections is now part of our nosocomial prevention program.

Excess Length of Stay Attributable to Clostridium difficile Infection (CDI) in the Acute Care Setting: A Multistate Model

BACKGROUND

Standard estimates of the impact of Clostridium difficile infections (CDI) on inpatient lengths of stay (LOS) may overstate inpatient care costs attributable to CDI. In this study, we used multistate modeling (MSM) of CDI timing to reduce bias in estimates of excess LOS.

METHODS

A retrospective cohort study of all hospitalizations at any of 120 acute care facilities within the US Department of Veterans Affairs (VA) between 2005 and 2012 was conducted. We estimated the excess LOS attributable to CDI using an MSM to address time-dependent bias. Bootstrapping was used to generate 95% confidence intervals (CI). These estimates were compared to unadjusted differences in mean LOS for hospitalizations with and without CDI.

RESULTS

During the study period, there were 3.96 million hospitalizations and 43,540 CDIs. A comparison of unadjusted means suggested an excess LOS of 14.0 days (19.4 vs 5.4 days). In contrast, the MSM estimated an attributable LOS of only 2.27 days (95% CI, 2.14-2.40). The excess LOS for mild-to-moderate CDI was 0.75 days (95% CI, 0.59-0.89), and for severe CDI, it was 4.11 days (95% CI, 3.90-4.32). Substantial variation across the Veteran Integrated Services Networks (VISN) was observed.

CONCLUSIONS

CDI significantly contributes to LOS, but the magnitude of its estimated impact is smaller when methods are used that account for the time-varying nature of infection. The greatest impact on LOS occurred among patients with severe CDI. Significant geographic variability was observed. MSM is a useful tool for obtaining more accurate estimates of the inpatient care costs of CDI.

Development and Validation of a Clostridium Difficile Risk Assessment Tool

The incidence of Clostridium difficile infection has increased rapidly during the past decade, increasing lengths of stays in the hospital, costs, and mortality rates. To address this increased incidence, we performed a retrospective case-control study using known risk factors to develop a tool to determine which patients are at risk for infection. Multivariate analysis generated a combination of risk factors associated with development of infection including prior admission, endoscopy within 30 days, cephalosporin/fluoroquinolone use, length of stay 7 days or longer, age 65 years or older, body mass index less than 25, and albumin level less than 2.7 g/dL. A weighted scoring tool was created that predicted disease with a sensitivity of 86% and a specificity of 44% in the derivation sample, and 92% sensitivity and a specificity of 39% when applied to the validation sample. Application of this 8-item tool by nurses in multiple settings could aid in the determination of patients who are at risk, allowing prophylactic treatment, prompt isolation of patients, restricted antibiotic use, and decreased testing of low-risk patients.

Serum mannose-binding lectin concentration, but not genotype, is associated with Clostridium difficile infection recurrence: a prospective cohort study

Low mannose-binding lectin concentration, but not genotype, was associated with disease recurrence in a large prospective cohort of patients with Clostridium difficile infection.

Background. Mannose-binding lectin (MBL) plays a key role in the activation of the lectin-complement pathway of innate immunity, and its deficiency has been linked with several acute infections. However, its role in predisposing to, or modulating disease severity in, Clostridium difficile infection (CDI) has not been investigated.

Methods. We prospectively recruited 308 CDI case patients and 145 control patients with antibiotic-associated diarrhea (AAD). CDI outcome measures were disease severity, duration of symptoms, 30-day mortality, and 90-day recurrence. Serum concentrations of MBL were determined using a commercial enzyme-linked immunosorbent assay transferred to an electrochemiluminescence–based platform. MBL2 polymorphisms were typed using a combination of pyrosequencing and TaqMan genotyping assays.

Results. The frequency of the MBL2 genetic variants was similar to that reported in other white populations. MBL serum concentrations in CDI and AAD subjects were determined by MBL2 exonic variants B, C, and D and the haplotypes (LYPB, LYQC, and HYPD). There was no difference in either MBL concentrations or genotypes between cases and controls. MBL concentration, but not genotype, was a determinant of CDI recurrence (odds ratios, 3.18 [95% confidence interval {CI}, 1.40–7.24] and 2.61 [95% CI, 1.35–5.04] at the <50 ng/mL and <100 ng/mL cutoff points, respectively; P < .001). However, neither MBL concentration nor MBL2 genotype was linked with the other CDI outcomes.

Conclusions. Serum MBL concentration did not differentiate between CDI cases and AAD controls, but among CDI cases, MBL concentration, but not genotype, was associated with CDI recurrence, indicating that MBL acts as a modulator of disease, rather than a predisposing factor.

Microbiome data distinguish patients with Clostridium difficile infection and non-C. difficile-associated diarrhea from healthy controls

Antibiotic usage is the most commonly cited risk factor for hospital-acquired Clostridium difficile infections (CDI). The increased risk is due to disruption of the indigenous microbiome and a subsequent decrease in colonization resistance by the perturbed bacterial community; however, the specific changes in the microbiome that lead to increased risk are poorly understood. We developed statistical models that incorporated microbiome data with clinical and demographic data to better understand why individuals develop CDI. The 16S rRNA genes were sequenced from the feces of 338 individuals, including cases, diarrheal controls, and nondiarrheal controls. We modeled CDI and diarrheal status using multiple clinical variables, including age, antibiotic use, antacid use, and other known risk factors using logit regression. This base model was compared to models that incorporated microbiome data, using diversity metrics, community types, or specific bacterial populations, to identify characteristics of the microbiome associated with CDI susceptibility or resistance. The addition of microbiome data significantly improved our ability to distinguish CDI status when comparing cases or diarrheal controls to nondiarrheal controls. However, only when we assigned samples to community types was it possible to differentiate cases from diarrheal controls. Several bacterial species within the Ruminococcaceae, Lachnospiraceae, Bacteroides, and Porphyromonadaceae were largely absent in cases and highly associated with nondiarrheal controls. The improved discriminatory ability of our microbiome-based models confirms the theory that factors affecting the microbiome influence CDI.

The gut microbiome, composed of the trillions of bacteria residing in the gastrointestinal tract, is responsible for a number of critical functions within the host. These include digestion, immune system stimulation, and colonization resistance. The microbiome’s role in colonization resistance, which is the ability to prevent and limit pathogen colonization and growth, is key for protection against Clostridium difficile infections. However, the bacteria that are important for colonization resistance have not yet been elucidated. Using statistical modeling techniques and different representations of the microbiome, we demonstrated that several community types and the loss of several bacterial populations, including Bacteroides, Lachnospiraceae, and Ruminococcaceae, are associated with CDI. Our results emphasize the importance of considering the microbiome in mediating colonization resistance and may also direct the design of future multispecies probiotic therapies.

Microbiome data distinguish patients with Clostridium difficile infection and non-C. difficile-associated diarrhea from healthy controls

Antibiotic usage is the most commonly cited risk factor for hospital-acquired Clostridium difficile infections (CDI). The increased risk is due to disruption of the indigenous microbiome and a subsequent decrease in colonization resistance by the perturbed bacterial community; however, the specific changes in the microbiome that lead to increased risk are poorly understood. We developed statistical models that incorporated microbiome data with clinical and demographic data to better understand why individuals develop CDI. The 16S rRNA genes were sequenced from the feces of 338 individuals, including cases, diarrheal controls, and nondiarrheal controls. We modeled CDI and diarrheal status using multiple clinical variables, including age, antibiotic use, antacid use, and other known risk factors using logit regression. This base model was compared to models that incorporated microbiome data, using diversity metrics, community types, or specific bacterial populations, to identify characteristics of the microbiome associated with CDI susceptibility or resistance. The addition of microbiome data significantly improved our ability to distinguish CDI status when comparing cases or diarrheal controls to nondiarrheal controls. However, only when we assigned samples to community types was it possible to differentiate cases from diarrheal controls. Several bacterial species within the Ruminococcaceae, Lachnospiraceae, Bacteroides, and Porphyromonadaceae were largely absent in cases and highly associated with nondiarrheal controls. The improved discriminatory ability of our microbiome-based models confirms the theory that factors affecting the microbiome influence CDI. IMPORTANCE The gut microbiome, composed of the trillions of bacteria residing in the gastrointestinal tract, is responsible for a number of critical functions within the host. These include digestion, immune system stimulation, and colonization resistance. The microbiome's role in colonization resistance, which is the ability to prevent and limit pathogen colonization and growth, is key for protection against Clostridium difficile infections. However, the bacteria that are important for colonization resistance have not yet been elucidated. Using statistical modeling techniques and different representations of the microbiome, we demonstrated that several community types and the loss of several bacterial populations, including Bacteroides, Lachnospiraceae, and Ruminococcaceae, are associated with CDI. Our results emphasize the importance of considering the microbiome in mediating colonization resistance and may also direct the design of future multispecies probiotic therapies.

Validation of a clinical risk-scoring algorithm for severe scrub typhus

OBJECTIVE

The aim of the study reported here was to validate the risk-scoring algorithm for prognostication of scrub typhus severity.

METHODS

The risk-scoring algorithm for prognostication of scrub typhus severity developed earlier from two general hospitals in Thailand was validated using an independent dataset of scrub typhus patients in one of the hospitals from a few years later. The predictive performances of the two datasets were compared by analysis of the area under the receiver-operating characteristic curve (AuROC). Classification of patients into non-severe, severe, and fatal cases was also compared.

RESULTS

The proportions of non-severe, severe, and fatal patients by operational definition were similar between the development and validation datasets. Patient, clinical, and laboratory profiles were also similar. Scores were similar in both datasets, both in terms of discriminating non-severe from severe and fatal patients (AuROC =88.74% versus 91.48%, P=0.324), and in discriminating fatal from severe and non-severe patients (AuROC =88.66% versus 91.22%, P=0.407). Over- and under-estimations were similar and were clinically acceptable.

CONCLUSION

The previously developed risk-scoring algorithm for prognostication of scrub typhus severity performed similarly with the validation data and the first dataset. The scoring algorithm may help in the prognostication of patients according to their severity in routine clinical practice. Clinicians may use this scoring system to help make decisions about more intensive investigations and appropriate treatments.