Predicting the Risk of Clostridium difficile Infection upon Admission: A Score to Identify Patients for Antimicrobial Stewardship Efforts

Derivation and internal validation of the multivariate toxigenic C. difficile diarrhea model and risk score for emergency room and hospitalized patients with diarrhea

Background

Many factors have been associated with the risk of toxigenic C. difficile diarrhea (TCdD). This study derived and internally validated a multivariate model for estimating the risk of TCdD in patients with diarrhea using readily available clinical factors.

Methods

A random sample of 3,050 symptomatic emergency department or hospitalized patients undergoing testing for toxigenic C. difficile at a single teaching hospital between 2014 and 2018 was created. Unformed stool samples positive for both glutamate dehydrogenase antigen by enzyme immunoassay and tcdB gene by polymerase chain reaction were classified as TCdD positive. The TCdD Model was created using logistic regression and was modified to the TCdD Risk Score to facilitate its use.

Results

8.1% of patients were TCdD positive. TCdD risk increased with abdominal pain (adjusted odds ratio 1.3; 95% CI, 1.0-1.8), previous C. difficile diarrhea (2.5, 1.1-6.1), and prior antibiotic exposure, especially when sampled in the emergency department (4.2, 2.5-7.0) versus the hospital (1.7, 1.3-2.3). TCdD risk also increased when testing occurred earlier during the hospitalization encounter, when age and white cell count increased concurrently, and with decreased eosinophil count. In internal validation, the TCdD Model had moderate discrimination (optimism-corrected C-statistic 0.65, 0.62-0.68) and good calibration (optimism-corrected Integrated Calibration Index [ICI] 0.017, 0.001-0.022). Performance decreased slightly for the TCdD Risk Score (C-statistic 0.63, 0.62-0.63; ICI 0.038, 0.004-0.038).

Conclusions

TCdD risk can be predicted using readily available clinical risk factors with modest accuracy.

A Real-World Study on the Clinical Characteristics, Outcomes, and Relationship between Antibiotic Exposure and Clostridioides difficile Infection

Clostridioides difficile is a Gram-positive bacteria that causes nosocomial infections, significantly impacting public health. In the present study, we aimed to describe the clinical characteristics, outcomes, and relationship between antibiotic exposure and Clostridioides difficile infection (CDI) in patients based on reports from two databases. Thus, we conducted a retrospective study of patients diagnosed with CDI from Sibiu County Clinical Emergency Hospital (SCCEH), Romania, followed by a descriptive analysis based on spontaneous reports submitted to the EudraVigilance (EV) database. From 1 January to 31 December 2022, we included 111 hospitalized patients with CDI from SCCEH. Moreover, 249 individual case safety reports (ICSRs) from EVs were analyzed. According to the data collected from SCCEH, CDI was most frequently reported in patients aged 65-85 years (66.7%) and in females (55%). In total, 71.2% of all patients showed positive medical progress. Most cases were reported in the internal medicine (n = 30, 27%), general surgery (n = 26, 23.4%), and infectious disease (n = 22, 19.8%) departments. Patients were most frequently exposed to ceftriaxone (CFT) and meropenem (MER). Also, in the EV database, most CDI-related ADRs were reported for CFT, PIP/TAZ (piperacillin/tazobactam), MER, and CPX (ciprofloxacin). Understanding the association between previous antibiotic exposure and the risk of CDI may help update antibiotic stewardship protocols and reduce the incidence of CDI by lowering exposure to high-risk antibiotics.

A new score to predict Clostridioides difficile infection in medical patients: a sub-analysis of the FADOI-PRACTICE study

Medical divisions are at high risk of Clostridioides difficile infection (CDI) due to patients' frailty and complexity. This sub-analysis of the FADOI-PRACTICE study included patients presenting with diarrhea either at admission or during hospitalization. CDI diagnosis was confirmed when both enzyme immunoassay and A and B toxin detection were found positive. The aim of this sub-analysis was the identification of a new score to predict CDI in hospitalized, medical patients. Five hundred and seventy-two patients with diarrhea were considered. More than half of patients was female, 40% on antibiotics in the previous 4 weeks and 60% on proton pump inhibitors (PPIs). CDI diagnosis occurred in 103 patients (18%). Patients diagnosed with CDI were older, more frequently of female sex, recently hospitalized and bed-ridden, and treated with antibiotics and PPIs. Through a backward stepwise logistic regression model, age > 65 years, female sex, recent hospitalization, recent antibiotic therapy, active cancer, prolonged hospital stay (> 12 days), hypoalbuminemia (albumin < 3 g/dL), and leukocytosis (white blood cells > 9 × 10^9/L) were found to independently predict CDI occurrence. These variables contributed to building a clinical prognostic score with a good sensitivity and a modest specificity for a value > 3 (79% and 58%, respectively; AUC 0.75, 95% CI 0.71-0.79, p < 0.001), that identified low-risk (score ≤ 3; 42.5%) and high-risk (score > 3; 57.5%) patients. Although some classical risk factors were confirmed to increase CDI occurrence, the changing landscape of CDI epidemiology suggests a reappraisal of common risk factors and the development of novel risk scores based on local epidemiology.

Impact of a Mortality Prediction Rule for Organizing and Guiding Antimicrobial Stewardship Program Activities

BACKGROUND

Antimicrobial stewardship program (ASP) surveillance at our hospital is supplemented by an internally developed surveillance database. In 2013, the database incorporated a validated, internally developed, prediction rule for patient mortality within 30 days of hospital admission. This study describes the impact of an expanded ASP review in patients at the highest risk for mortality.

METHODS

This retrospective, quasi-experimental study analyzed adults who received antimicrobials with the highest mortality risk score. Study periods were defined as 2011-Q3 2013 (historical group) and Q4 2013-2018 (intervention group). Primary and secondary outcomes were assessed for confounders and analyzed using both unadjusted and propensity score weighted analyses. Interrupted time-series analyses also analyzed key outcomes.

RESULTS

A total of 3282 and 5456 patients were included in the historical and intervention groups, respectively. There were significant reductions in median antimicrobial duration (5 vs 4 days; P < .001), antimicrobial days of therapy (8 vs 7; P < .001), antimicrobial cost ($96 vs $85; P = .003), length of stay (LOS) (6 vs 5 days; P < .001), intensive care unit (ICU) LOS (3 vs 2 days; P < .001), total hospital cost ($10 946 vs $9119; P < .001), healthcare facility-onset vancomycin-resistant Enterococcus (HO-VRE) incidence (1.3% vs 0.3%; P ≤ .001), and HO-VRE infections (0.6% vs 0.2%; P = .018) in the intervention cohort.

CONCLUSIONS

Reductions in antimicrobial use, hospital and ICU LOS, HO-VRE, HO-VRE infections, and costs were associated with incorporation of a novel mortality prediction rule to guide ASP surveillance and intervention.

Ribaxamase, an Orally Administered β-Lactamase, Diminishes Changes to Acquired Antimicrobial Resistance of the Gut Resistome in Patients Treated with Ceftriaxone

Introduction

Intravenous (IV) β-lactam antibiotics, excreted through bile into the gastrointestinal (GI) tract, may disrupt the gut microbiome by eliminating the colonization resistance from beneficial bacteria. This increases the risk for Clostridium difficile infection (CDI) and can promote antimicrobial resistance by selecting resistant organisms and eliminating competition by non-resistant organisms. Ribaxamase is an orally administered β-lactamase for use with IV β-lactam antibiotics (penicillins and cephalosporins) and is intended to degrade excess antibiotics in the upper GI before they can disrupt the gut microbiome and alter the resistome.

Methods

Longitudinal fecal samples (349) were collected from patients who participated in a previous Phase 2b clinical study with ribaxamase for prevention of CDI. In that previous study, patients were treated with ceftriaxone for a lower respiratory tract infection and received concurrent ribaxamase or placebo. Extracted fecal DNA from the samples was subjected to whole-genome shotgun sequencing and analyzed for the presence of antimicrobial resistance (AMR) genes by alignment of sequences against the Comprehensive Antibiotic Resistance Database. A qPCR assay was also used to confirm some of the results.

Results

Database alignment identified ~1300 acquired AMR genes and gene variants, including those encoding β-lactamases and vancomycin resistance which were significantly increased in placebo vs ribaxamase-treated patients following antibiotic exposure. qPCR corroborated the presence of these genes and supported both new acquisition and expansion of existing gene pools based on no detectable copy number or a low copy number in pre-antibiotic samples which increased post-antibiotics. Additional statistical analyses demonstrated significant correlations between changes in the gut resistome and clinical study parameters including study drug assignment and β-lactamase and vancomycin resistance gene frequency.

Discussion

These findings demonstrated that ribaxamase reduced changes to the gut resistome subsequent to ceftriaxone administration and may help limit the emergence of AMR.

Use of aClostridium difficileclinical prediction rule to facilitate antimicrobial stewardship

In this study, we assessed whether a Clostridium difficile clinical prediction rule could be used to facilitate antimicrobial stewardship in an acute care hospital. We found that patients with higher scores were more likely to receive unnecessary antimicrobials and had the greatest potential for antimicrobial stewardship interventions. This novel method has the potential to expedite antimicrobial stewardship efforts, particularly for complex patients, in health care institutions.

Prolonged antimicrobial prophylaxis following cardiac device procedures increases preventable harm: insights from the VA CART program

BACKGROUND

The rate of cardiovascular implantable electronic device (CIED) infection is increasing coincident with an increase in the number of device procedures. Preprocedural antimicrobial prophylaxis reduces CIED infections; however, there is no evidence that prolonged postprocedural antimicrobials additionally reduce risk. Thus, we sought to quantify the harms associated with this approach.

OBJECTIVE

To measure the association between Clostridium difficile infection (CDI), acute kidney injury (AKI) and receipt of prolonged postprocedural antimicrobials.

METHODS

CIED procedures entered into the VA Clinical Assessment Reporting and Tracking Electrophysiology (CART-EP) database during fiscal years 2008-2016 were included. The primary outcome was 90-day incidence of CDI and the secondary outcome was the 7-day incidence of AKI. The primary exposure measure was duration of postprocedural antimicrobial therapy. Associations were measured using Cox-proportional hazards and binomial regression.

RESULTS

Prolonged postprocedural antimicrobial therapy was identified following 3,331 of 6,497 CIED procedures (51.3%), and the median duration of prophylaxis was 5 days. Prolonged postprocedural antimicrobial use was associated with increased risk of CDI (hazard ratio [HR], 2.90; 95% confidence interval [CI], 1.54-5.46). Of the 27 patients who developed CDI, 11 subsequently died. Postprocedural antimicrobial use with ≥2 antimicrobials was associated with an increased risk of AKI (OR, 4.16; 95% CI, 2.50-6.90). The impact was particularly significant when one of the dual agents prescribed was vancomycin (adjusted OR, 8.41; 95% CI, 5.53-12.79).

CONCLUSIONS

Prolonged antimicrobial prophylaxis following CIED procedures increases preventable harm; this practice should be discouraged in procedural settings such as the cardiac electrophysiology laboratory.

Real-world effectiveness of infection prevention interventions for reducing procedure-related cardiac device infections: Insights from the veterans affairs clinical assessment reporting and tracking program

OBJECTIVE

To measure the association between receipt of specific infection prevention interventions and procedure-related cardiac implantable electronic device (CIED) infections.

DESIGN

Retrospective cohort with manually reviewed infection status.

SETTING

Setting: National, multicenter Veterans Health Administration (VA) cohort.

PARTICIPANTS

Sampling of procedures entered into the VA Clinical Assessment Reporting and Tracking-Electrophysiology (CART-EP) database from fiscal years 2008 through 2015.

METHODS

A sample of procedures entered into the CART-EP database underwent manual review for occurrence of CIED infection and other clinical/procedural variables. The primary outcome was 6-month incidence of CIED infection. Measures of association were calculated using multivariable generalized estimating equations logistic regression.

RESULTS

We identified 101 procedure-related CIED infections among 2,098 procedures (4.8% of reviewed sample). Factors associated with increased odds of infections included (1) wound complications (adjusted odds ratio [aOR], 8.74; 95% confidence interval [CI], 3.16-24.20), (2) revisions including generator changes (aOR, 2.4; 95% CI, 1.59-3.63), (3) an elevated international normalized ratio (INR) &gt;1.5 (aOR, 1.56; 95% CI, 1.12-2.18), and (4) methicillin-resistant Staphylococcus colonization (aOR, 9.56; 95% CI, 1.55-27.77). Clinically effective prevention interventions included preprocedural skin cleaning with chlorhexidine versus other topical agents (aOR, 0.41; 95% CI, 0.22-0.76) and receipt of β-lactam antimicrobial prophylaxis versus vancomycin (aOR, 0.60; 95% CI, 0.37-0.96). The use of mesh pockets and continuation of antimicrobial prophylaxis after skin closure were not associated with reduced infection risk.

CONCLUSIONS

These findings regarding the real-world clinical effectiveness of different prevention strategies can be applied to the development of evidence-based protocols and infection prevention guidelines specific to the electrophysiology laboratory.

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.

Development of a risk prediction model for hospital-onset Clostridium difficile infection in patients receiving systemic antibiotics

BACKGROUND

Clostridium difficile infection (CDI) is recognized as a significant challenge in health care. Identification of high-risk individuals is essential for the development of CDI prevention strategies. The objective of this study was to develop an easily implementable risk prediction model for hospital-onset CDI in patients receiving systemic antimicrobials.

METHODS

This retrospective, case-control, multicenter study included adult patients admitted to Novant Health Forsyth Medical Center and Novant Health Presbyterian Medical Center from July 1, 2015, to July 1, 2017, who received systemic antibiotics. Cases were subjects with hospital-onset CDI; controls were subjects without a CDI diagnosis. Cases were matched 1:1 with controls by admitted medical unit type. Variables significantly associated with CDI were incorporated into a multivariate analysis. A logistic regression model was used to formulate a point-based risk prediction model. Positive predictive value, negative predictive value, sensitivity, specificity, and accuracy were determined at various point cutoffs of the model. A receiver operating characteristic-area under the curve was created to assess the discrimination of the model.

RESULTS

A total of 200 subjects (100 cases and 100 controls) were included. Most patients were Caucasian and female. Risk factors for CDI identified and incorporated into the model included age ≥70 years (adjusted odds ratio, 1.89; 95% confidence interval 1.05-3.43; P = .0326) and recent hospitalization in the past 90 days (adjusted odds ratio, 3.55; 95% confidence interval 1.90-6.83; P < .0001). Sensitivity and specificity were 76% and 49%, respectively, for scores ≥2 and 20% and 93%, respectively, for a score of 6. Diagnostic performance of various score cutoffs for the model indicated that a score ≥2 was associated with the highest accuracy (63%). The receiver operating characteristic-area under the curve was 0.7.

DISCUSSION

We developed a simple-to-implement hospital-onset CDI risk model that included only independent risks that can be obtained immediately on presentation to the health care facility. Despite this, the model had fair discriminatory power. Similar risk factors were found in previously developed models; however, the utility of these models is limited owing to the difficulty of assessing other included risk factors and the inclusion of risk factors that cannot be evaluated until the patient is discharged from the health care facility.

CONCLUSIONS

Identification of hospitalized patients who are receiving systemic antibiotics, are ≥70 years old, and were recently admitted to the hospital in the past 90 days may allow for an easily implementable hospital-onset CDI risk prevention strategy.

Clostridium Difficile-Associated Infection in Trauma Patients: Development of the Clostridium Difficile Influencing Factors (CDIF) Score

CONTEXT

Clostridium difficile-associated infection (CDAI) can result in longer hospitalization, increased morbidity, and higher mortality rates for surgical patients. The impact on trauma patients is unknown, however.

OBJECTIVE

To assess the effect of CDAI on trauma patients and develop a scoring system to predict CDAI in that population.

METHODS

Records of all trauma patients admitted to a Level I Trauma Center from 2001 to 2014 were retrospectively reviewed. Presence of CDAI was defined as evidence of positive toxin or polymerase chain reaction. Patients with CDAI were matched to patients without CDAI using propensity score matching on a ratio of 1:3.

MAIN OUTCOME MEASURES

Primary outcome was inhospital mortality. Secondary outcomes included length of stay and need for mechanical ventilation. A decision-tree analysis was performed to develop a predicting model for CDAI in the study population.

RESULTS

During the study period, 11,016 patients were identified. Of these, 50 patients with CDAI were matched to 150 patients without CDAI. There were no differences in admission characteristics and demographics. Patients in whom CDAI developed had significantly higher mortality (12% vs 4%, p < 0.01), need for mechanical ventilation (57% vs 23%, p < 0.01), and mean hospital length of stay (15.3 [standard deviation 1.4]) days vs 2.1 [0.6] days, p < 0.0).

CONCLUSION

In trauma patients, CDAI results in significant morbidity and mortality. The C difficile influencing factor score is a useful tool in identifying patients at increased risk of CDAI.

Can the use of probiotics reduce the incidence of nosocomial Clostridium difficile infection in 60 years or older patients?

Background:

Antibiotic-associated diarrhea (AAD) is a common side effect of antibiotic use. The pathogenesis of AAD may be mediated by disruption of the host’s normal flora resulting in overgrowth of pathogens. One of these pathogens is Clostridium difficile. Probiotics may help reestablish the disrupted intestinal flora and help clear the pathogen and its toxins from the host. Although numerous studies have been conducted, results are variable due to study design, type of probiotic, differing dose of probiotic, and duration of treatment. Our study aims to assess the efficacy and safety of the probiotics Lactobacillus GG and Saccharomyces boulardii for the prevention of C. difficile associated diarrhea.

Methods:

Study data were collected from two community hospitals in Kansas City over a period of 5 months. All patients in the study were initiated on antibiotics and were then given probiotics. This study was controlled and not blinded.

Results:

In data from two community hospitals, 816 patients who were being treated with antibiotics prior to the investigation served as a control; 14 (1.72%) had C. difficile Infections. During the study using probiotic administration, 803 patients also being treated with antibiotics prior to the investigation were followed and in this population; five patients (0.62%) contracted a C. difficile infection.

Characterization of Clostridium difficile isolates collected during a phase 2b clinical study with SYN-004 (ribaxamase) for the prevention of C. difficile infection

During a Phase 2b study with SYN-004 (ribaxamase) for prevention of Clostridium difficile infection (CDI) conducted in North America and Eastern Europe, 45 C. difficile isolates from subjects with laboratory-confirmed CDI and or colonized with C. difficile were collected and characterized. Several C. difficile PCR ribotypes, including 027 and 198, were identified.

Design and implementation of population-based specialty care programs

PURPOSE

The development, implementation, and scaling of 3 population-based specialty care programs in a large integrated healthcare system are reviewed, and the role of clinical pharmacy services in ensuring safe, effective, and affordable care is highlighted.

SUMMARY

The Kaiser Permanente (KP) integrated healthcare delivery model allows for rapid development and expansion of innovative population management programs involving pharmacy services. Clinical pharmacists have assumed integral roles in improving the safety and effectiveness of high-complexity, high-cost care for specialty populations. These roles require an appropriate practice scope and are supported by an advanced electronic health record with disease registries and electronic surveillance tools for care-gap identification. The 3 specialty population programs described were implemented to address variation or unrecognized gaps in care for at-risk specialty populations. The Home Phototherapy Program has leveraged internal partnerships with clinical pharmacists to improve access to cost-effective nonpharmacologic interventions for psoriasis and other skin disorders. The Multiple Sclerosis Care Program has incorporated clinical pharmacists into neurology care in order to apply clinical guidelines in a systematic manner. The KP SureNet program has used clinical pharmacists and data analytics to identify opportunities to prevent drug-related adverse outcomes and ensure timely follow-up.

CONCLUSION

Specialty care programs improve quality, cost outcomes, and the patient experience by appropriating resources to provide systematic and targeted care to high-risk patients. KP leverages an integration of people, processes, and technology to develop and scale population-based specialty care.

Prediction of Recurrent Clostridium Difficile Infection Using Comprehensive Electronic Medical Records in an Integrated Healthcare Delivery System

BACKGROUND Predicting recurrent Clostridium difficile infection (rCDI) remains difficult.

METHODS

We employed a retrospective cohort design. Granular electronic medical record (EMR) data had been collected from patients hospitalized at 21 Kaiser Permanente Northern California hospitals. The derivation dataset (2007-2013) included data from 9,386 patients who experienced incident CDI (iCDI) and 1,311 who experienced their first CDI recurrences (rCDI). The validation dataset (2014) included data from 1,865 patients who experienced incident CDI and 144 who experienced rCDI. Using multiple techniques, including machine learning, we evaluated more than 150 potential predictors. Our final analyses evaluated 3 models with varying degrees of complexity and 1 previously published model. RESULTS Despite having a large multicenter cohort and access to granular EMR data (eg, vital signs, and laboratory test results), none of the models discriminated well (c statistics, 0.591-0.605), had good calibration, or had good explanatory power. CONCLUSIONS Our ability to predict rCDI remains limited. Given currently available EMR technology, improvements in prediction will require incorporating new variables because currently available data elements lack adequate explanatory power. Infect Control Hosp Epidemiol 2017;38:1196-1203.