Electronic health record-based detection of risk factors for Clostridium difficile infection relapse

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.

Generalizability of predictive models for Clostridioides difficile infection, severity and recurrence at an urban safety-net hospital

INTRODUCTION

Predictive models for Clostridioides difficile infection can identify high-risk patients and aid clinicians in preventing infection. Issues of generalizability regarding current predictive models have been acknowledged but, to the authors' knowledge, have never been quantified.

METHODS

C. difficile infection, severity and recurrence predictive models were created using multi-variate logistic regression through case-control sampling from an urban safety-net hospital. Models were validated using five-fold cross-validation, and inverse probability weights (IPW) based on two different catchment area definitions were used to improve external validity. Akaike Information Criterion (AIC), area under the receiver operating characteristic curve (AUROC), and sensitivity and specificity with bootstrapped confidence intervals (CI) were used to assess and compare model fit and performance.

RESULTS

Changes in performance before and after weighting were small across all models, although differences were more apparent after weighting the recurrence model (AUROC values of 0.78, 0.76 and 0.71 for the unweighted and two weighted models, respectively). Overall, the infection model performed the best (AUROC 0.82, 95% CI 0.78-0.85), followed by the recurrence model (AUROC 0.78, 95% CI 0.69-0.86) and then the severity model (AUROC 0.70, 95% CI 0.63-0.78).

CONCLUSIONS

The performance of the models after weighting did not change drastically, suggesting that the models predicting C. difficile infection, severity and recurrence may not be impacted by patient selection factors. However, other researchers may wish to consider addressing these catchment forces using IPW.

Derivation of clinical predictive factors (CHIEF) for first recurrent Clostridioides difficile infection

BACKGROUND

Current models for predicting Clostridioides difficile infection (CDI) recurrence rates have a limited capacity to account for important risk factors. This study developed a clinical prediction rule for CDI recurrence.

METHODS

This retrospective cohort study evaluated 209 patients with CDI at a university hospital in Japan. Logistic regression and receiver operating characteristic curve analyses were performed to identify potential predictors (age, sex, underlying diseases, antibiotic use, acid suppressants, immunosuppressants, CDI history) of CDI recurrence.

RESULTS

Forty-five patients developed recurrent CDI. Univariate analyses identified several significant recurrence predictors (enteral feeding, inflammatory bowel diseases [IBD], community-onset CDI, severe CDI). Enteral feeding (odds ratio: 3.87, 95% confidence interval: 1.75-8.56) and IBD (odds ratio: 7.08, 95% confidence interval: 1.28-39.06) were significant factors in the multivariate analysis. The CHIEF predictive scoring system was developed using 5 relevant variables (carbapenem use, hematologic malignancy, IBD, enteral feeding, fluoroquinolone use); the area under the receiver operating characteristic curve for the CHIEF score was 0.70.

DISCUSSION

The CHIEF score incorporates useful, clinically available factors and could help identify patients at risk of recurrent CDI.

CONCLUSIONS

These findings contribute to the understanding of risk factors associated with CDI recurrence and provide support for the development of prevention strategies.

Clostridioides difficile colonization and the frequency of subsequent treatment for C. difficile infection in critically ill patients

OBJECTIVE

To determine risk factors for Clostridioides difficile colonization and C. difficile infection (CDI) among patients admitted to the intensive care unit (ICU).

DESIGN

Retrospective observational cohort study.

SETTING

Tertiary-care facility.

PATIENTS

All adult patients admitted to an ICU from July 1, 2015, to November 6, 2019, who were tested for C. difficile colonization. Patients with CDI were excluded.

METHODS

Information was collected on patient demographics, comorbidities, laboratory results, and prescriptions. We defined C. difficile colonization as a positive nucleic acid amplification test for C. difficile up to 48 hours before or 24 hours after intensive care unit (ICU) admission without evidence of active infection. We defined active infection as the receipt of an antibiotic whose only indication is the treatment of CDI. The primary outcome measure was the development of CDI up to 30 days after ICU admission. Logistic regression was used to model associations between clinical variables and the development of CDI.

RESULTS

The overall C. difficile colonization rate was 4% and the overall CDI rate was 2%. Risk factors for the development of CDI included C. difficile colonization (aOR, 13.3; 95% CI, 8.3-21.3; P < .0001), increased ICU length of stay (aOR, 1.04; 95% CI, 1.03-1.05; P < .0001), and a history of inflammatory bowel disease (aOR, 3.8; 95% CI, 1.3-11.1; P = .02). Receipt of any antibiotic during the ICU stay was associated with a borderline increased odds of CDI (aOR, 1.9; 95% CI, 1.0-3.4; P = .05).

CONCLUSION

C. difficile colonization is associated with the development of CDI among ICU patients.

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.

Risk factors for recurrent healthcare-facility associated Clostridioides difficile infection in a Swedish setting

PURPOSE

The objectives were to determine the risk factors for recurrent healthcare facility-associated Clostridioides difficile infection (HCF-CDI) in a high CDI incidence, low antibiotic use setting and to determine if length of cefotaxime exposure is a risk factor for recurrent HCF-CDI.

METHODS

The risk factors for recurrent HCF-CDI were evaluated with a retrospective nested case control study based on chart reading. The risk factors were evaluated univariately and multivariately. Length of risk antibiotic exposure was evaluated further in a subanalysis.

RESULTS

Risk factors for recurrent HCF-CDI were renal insufficiency (25.4% of cases compared to 15.4% of controls p = 0.006) and metronidazole treatment of initial CDI episode (88.4% compared to 71.7% p = 0.01). Exposure to cefotaxime and risk for recurrent CDI showed a dose-dependent relationship (linear by linear p = 0.028).

CONCLUSIONS

Renal insufficiency and metronidazole treatment were independent risk factors for recurrent HCF-CDI in our setting. The relationship between cefotaxime exposure and risk for recurrent HCF-CDI, dose-dependent, could be evaluated further in a setting with high cefotaxime use.

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

SCOPE

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

METHODS AND QUESTIONS

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

RECOMMENDATIONS

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

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Acid Suppression Medications During Hospitalization as a Risk Factor for Recurrence of Clostridioides difficile Infection: Systematic Review and Meta-analysis

BACKGROUND

Studies have had conflicting results regarding the influence of acid-suppression medications (ASMs) during hospitalization on the recurrence of Clostridioides difficile infection (CDI).

METHODS

A systematic review and meta-analysis investigating the association between recurrent CDI and ASM use in inpatients was performed. Relevant literature was identified using Medline, Google Scholar, and Web of Science. All human studies were considered regardless of publication date. Case-control and cohort studies and clinical trials were included if they contained the necessary information to calculate appropriate statistics related to the objective of this study. Review articles, meta-analyses, and commentaries were excluded; however, their references were searched to identify any studies missed. The random-effects model was selected since significant heterogeneity in study design was identified. To evaluate the sensitivity of the analysis various subgroup analyses were performed.

RESULTS

Our search identified 9 studies involving 5668 patients of whom 1003 (17.7%) developed recurrent CDI. Patients on ASM were 64% more likely to develop recurrent CDI than patients not on ASM (OR, 1.64; 95% CI, 1.13-2.38; P = .009; I2 = 79.54%). Proton pump inhibitor (PPI) use was associated with an 84% increased risk of recurrent CDI versus no ASM (OR, 1.84; 95% CI, 1.18-2.85; P = .007; I2 = 83.4%).

CONCLUSIONS

ASM use during hospitalization was associated with a 64% increase in recurrent CDI. The association was greater with PPI use. Due to significant heterogeneity in the analyses, additional studies are essential to further elucidate iatrogenic effects of ASM. Unnecessary PPI use should be discontinued.

Economic burden of recurrent Clostridioides difficile infection in adults admitted to Spanish hospitals. A multicentre retrospective observational study

OBJECTIVE

Clostridioides difficile infection (CDI) is associated with increased hospital stays and mortality and a high likelihood of rehospitalization, leading to increased health resource use and costs. The objective was to estimate the economic burden of recurrent CDI (rCDI).

METHODS

Observational, retrospective study carried out in six hospitals. Adults aged ≥18 years with ≥1 confirmed diagnosis (primary or secondary) of rCDI between January 2010 and May 2018 were included. rCDI-related resource use included days of hospital stay (emergency room, ward, isolation and ICU), tests and treatments. For patients with primary diagnosis of rCDI, the complete hospital stay was attributed to rCDI. When diagnosis of rCDI was secondary, hospital stay attributed to rCDI was estimated using 1:1 propensity score matching as the difference in hospital stay compared to controls. Controls were hospitalizations without CDI recorded in the Spanish National Hospital Discharge Database. The cost was calculated by multiplying the natural resource units by the unit cost. Costs (euros) were updated to 2019.

RESULTS

We included 282 rCDI episodes (188 as primary diagnosis): 66.31% of patients were aged ≥65 years and 57.80% were female. The mean hospital stay (SD) was 17.18 (23.27) days: 86.17% of rCDI episodes were isolated for a mean (SD) of 10.30 (9.97) days. The total mean cost (95%-CI) per episode was €10,877 (9,499-12,777), of which the hospital stay accounted for 92.56.

CONCLUSIONS

There is high cost and resource use associated with rCDI, highlighting the importance of preventing rCDI to the Spanish National Health System.

Proton pump inhibitor use and risk for recurrent Clostridioides difficile infection: a systematic review and meta-analysis

OBJECTIVES

Proton pump inhibitor (PPI) therapy is a potentially modifiable risk factor for recurrent Clostridioides difficile infection (CDI). Citing an absence of clinical trials, many guidelines do not provide recommendations for addressing PPI management. Our aim was to perform an updated systematic review and meta-analysis evaluating the association between PPI use and recurrent CDI addressing prior methodological limitations.

METHODS

Data sources were MEDLINE and EMBASE. Eligible studies were cohort and case-control studies; there were no restrictions on study setting or duration of follow-up. Participants were adults with prior CDI who did or did not receive PPI therapy and were assessed for recurrent CDI. Summary (unadjusted) odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using a random effects model. Prespecified subgroup analyses were performed to explore heterogeneity including study design, study quality, duration of follow-up, adjustment for confounders, and outcome definition.

RESULTS

Sixteen studies were included in the meta-analysis, comprising 57 477 patients with CDI, of whom 6870 (12%) received PPIs. The rate of recurrent CDI was 24% in patients treated with PPIs versus 18% in those who were not. A meta-analysis that pooled unadjusted odds ratios demonstrated higher odds of recurrent CDI in patients who received PPIs (OR 1.69, 95%CI 1.46-1.96) versus those who did not. There was moderate heterogeneity between studies (I2 56%); however, a sensitivity analysis restricted to studies with 56 days of follow-up substantially reduced the heterogeneity (OR 1.59, 95%CI 1.36-1.85; I2 12%). An analysis restricted to multivariate studies that combined adjusted ORs also demonstrated higher odds of recurrent CDI in patients who received PPIs (OR 1.49, 95%CI 1.12-2.00). No publication bias was identified.

CONCLUSIONS

We found significantly higher odds of recurrent CDI among users of PPIs that persisted across multiple sensitivity analyses. These results support stronger recommendations for PPI stewardship at CDI diagnosis.

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.

Modelling disease risk for amyloid A (AA) amyloidosis in non-human primates using machine learning

Objective: Amyloid A (AA) amyloidosis is found in humans and non-human primates, but quantifying disease risk prior to clinical symptoms is challenging. We applied machine learning to identify the best predictors of amyloidosis in rhesus macaques from available clinical and pathology records. To explore potential biomarkers, we also assessed whether changes in circulating serum amyloid A (SAA) or lipoprotein profiles accompany the disease. Methods: We conducted a retrospective study using 86 cases and 163 controls matched for age and sex. We performed data reduction on 62 clinical, pathological and demographic variables, and applied multivariate modelling and model selection with cross-validation. To test the performance of our final model, we applied it to a replication cohort of 2,775 macaques. Results: The strongest predictors of disease were colitis, gastrointestinal adenocarcinoma, endometriosis, arthritis, trauma, diarrhoea and number of pregnancies. Sensitivity and specificity of the risk model were predicted to be 82%, and were assessed at 79 and 72%, respectively. Total, low density lipoprotein and high density lipoprotein cholesterol levels were significantly lower, and SAA levels and triglyceride-to-HDL ratios were significantly higher in cases versus controls. Conclusion: Machine learning is a powerful approach to identifying macaques at risk of AA amyloidosis, which is accompanied by increased circulating SAA and altered lipoprotein profiles.

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

Background

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

Methods

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

Results

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

Conclusions

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

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.

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.

Magnitude and direction of the association between Clostridium difficile infection and proton pump inhibitors in adults and pediatric patients: a systematic review and meta-analysis

BACKGROUND

Clostridium difficile infection (CDI) is a cause of increased morbidity and health care costs among hospitalized patients. Proton pump inhibitors (PPIs) are mainly used for the treatment of acid-related upper gastrointestinal diseases. The aim of the study was to assess the risks associated with initial and recurrent CDI in adult and pediatric patients treated with PPIs.

METHODS

A systematic search was performed using PubMed (Medline), Embase, and Web of Science with the following search terms: ("proton pump inhibitor," "PPI," or "acid suppression") AND ("infection," "diarrhea," "diarrhoea," "colitis," or "disease") AND ("Clostridium difficile"). Meta-analysis was performed using Revman5.3 software. Pooled odds ratios (ORs) presented as standard plots with 95% confidence intervals (CIs) were determined.

RESULTS

Sixty-seven eligible studies were selected. PPI use was significantly associated with risk of CDI (OR 2.34, 95% CI 1.94-2.82; P < 0.00001). Pooled data from twelve studies demonstrated a significant association between PPI use and recurrent CDI (OR 1.73, 95% CI 1.39-2.15; P = 0.02). Subgroup analysis revealed significant associations between PPI use and an increased incidence of CDI among adult (OR 2.30, 95% CI 1.89-2.80; P < 0.00001) and pediatric (OR 3.00, 95% CI 1.44-6.23; P < 0.00001) patients.

CONCLUSIONS

PPI use was associated with CDI in adult and pediatric patients, and with recurrent CDI. Although many risk factors are associated with the occurrence and recurrence of CDI, consideration should be given to not administering PPIs at any age if they are unnecessary.

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.

Association of Gastric Acid Suppression With Recurrent Clostridium difficile Infection: A Systematic Review and Meta-analysis

IMPORTANCE

Gastric acid suppression has been associated with an increased risk of primary Clostridium difficile infection (CDI), but the risk of recurrent CDI in patients taking gastric acid suppressant medications is unclear.

OBJECTIVE

To perform a systematic review and meta-analysis to evaluate the association between gastric acid suppressants and recurrent CDI.

DATA SOURCES

MEDLINE, EMBASE, the Cochrane Central Register, the Cochrane Database, and Web of Science were searched from January 1, 1995, to September 30, 2015, for studies assessing the association between gastric acid suppressant exposure and recurrent CDI. Search terms included Clostridium difficile, pseudomembranous colitis, proton pump inhibitor, and histamine H2 blocker.

STUDY SELECTION

Case-control studies, cohort studies, and clinical trials that included patients with CDI who did or did not receive gastric acid suppressant therapy and who were evaluated for recurrent CDI were included, with no restriction on study setting (inpatient or outpatient).

DATA EXTRACTION AND SYNTHESIS

The Newcastle-Ottawa scale was used to assess the methodologic quality of included studies. In this scale, case-control and cohort studies were scored on selection, comparability, and ascertainment of the outcome of interest. Data were independently abstracted to a predetermined collection form by 2 investigators. Summary odds ratio estimates with 95% CIs were calculated using the random-effects model and software to calculate the pooled effect size of studies reporting multivariate analyses.

MAIN OUTCOMES AND MEASURES

Risk of recurrent infection in patients with CDI and its association with use of gastric acid suppressant medication.

RESULTS

Sixteen observational studies were included, together reporting 7703 patients with CDI; among these, 1525 patients (19.8%) developed recurrent CDI. The rate of recurrent CDI in patients with gastric acid suppression was 22.1% (892 of 4038 patients) compared with 17.3% (633 of 3665) in patients without gastric acid suppression, which indicated an increased risk by meta-analysis (odds ratio [OR], 1.52; 95% CI, 1.20-1.94; P < .001). There was significant heterogeneity among the studies, with an I2 value of 64%. Subgroup analyses of studies adjusting for age and potential confounders confirmed an increased risk of recurrent CDI with use of gastric acid suppressants (OR, 1.38; 95% CI, 1.08-1.76; P = .02).

CONCLUSIONS AND RELEVANCE

Meta-analyses of observational studies suggest that patients who receive gastric acid suppressants may be at increased risk for recurrent CDI. These data should be interpreted with caution because they may be confounded owing to the observational design of the individual studies. It may be reasonable to re-evaluate the need for these medications in patients with CDI.

Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record

Proper handling of missing data is important for many secondary uses of electronic health record (EHR) data. Data imputation methods can be used to handle missing data, but their use for analyzing EHR data is limited and specific efficacy for postoperative complication detection is unclear. Several data imputation methods were used to develop data models for automated detection of three types (i.e., superficial, deep, and organ space) of surgical site infection (SSI) and overall SSI using American College of Surgeons National Surgical Quality Improvement Project (NSQIP) Registry 30-day SSI occurrence data as a reference standard. Overall, models with missing data imputation almost always outperformed reference models without imputation that included only cases with complete data for detection of SSI overall achieving very good average area under the curve values. Missing data imputation appears to be an effective means for improving postoperative SSI detection using EHR clinical data.

Prevention of Clostridium difficile Infection: A Systematic Survey of Clinical Practice Guidelines

BACKGROUND Clostridium difficile infection (CDI) is the most common cause of hospital-acquired infectious diarrhea. OBJECTIVE To analyze the methodological quality, content, and supporting evidence among clinical practice guidelines (CPGs) on CDI prevention. DESIGN AND SETTING We searched medical databases and gray literature for CPGs on CDI prevention published January 2004-January 2015. Three reviewers independently screened articles and rated CPG quality using the Appraisal of Guidelines for Research and Evaluation II (AGREE II) instrument, composed of 23 items, rated 1-7, within 6 domains. We reported each domain score as a percentage of its maximum possible score and standardized range. We summarized recommendations, extracted their supporting articles, and rated individually the level of evidence using the Oxford Centre for Evidence-Based Medicine Levels of Evidence. RESULTS Of 2,578 articles screened, 5 guidelines met inclusion criteria. Median AGREE II scores and interquartile ranges were: clarity of presentation, 75.9% (75.9%-79.6%); scope and purpose, 74.1% (68.5%-85.2%); editorial independence, 63.9% (47.2%-66.7%); applicability, 43.1% (19.4%-55.6%); stakeholder involvement, 40.7% (38.9%-44.4%); and rigor of development, 18.1% (17.4%-35.4%). CPGs addressed several common strategies for CDI prevention, including antibiotic stewardship, hypochlorite solutions, probiotic prophylaxis, and bundle strategies. Recommendations were often not consistent with evidence, and most were based on low-level studies. CONCLUSION CPGs did not adhere well to AGREE II reporting standards. Furthermore, there was limited transparency in moving from evidence to recommendations. CDI prevention CPGs need to better adhere to AGREE-II and be transparent in moving from evidence to recommendations, and recommendations need to be consistent with available evidence. Infect Control Hosp Epidemiol 2016;37:901-908.

Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review

OBJECTIVE

Electronic health records (EHRs) are an increasingly common data source for clinical risk prediction, presenting both unique analytic opportunities and challenges. We sought to evaluate the current state of EHR based risk prediction modeling through a systematic review of clinical prediction studies using EHR data.

METHODS

We searched PubMed for articles that reported on the use of an EHR to develop a risk prediction model from 2009 to 2014. Articles were extracted by two reviewers, and we abstracted information on study design, use of EHR data, model building, and performance from each publication and supplementary documentation.

RESULTS

We identified 107 articles from 15 different countries. Studies were generally very large (median sample size = 26 100) and utilized a diverse array of predictors. Most used validation techniques (n = 94 of 107) and reported model coefficients for reproducibility (n = 83). However, studies did not fully leverage the breadth of EHR data, as they uncommonly used longitudinal information (n = 37) and employed relatively few predictor variables (median = 27 variables). Less than half of the studies were multicenter (n = 50) and only 26 performed validation across sites. Many studies did not fully address biases of EHR data such as missing data or loss to follow-up. Average c-statistics for different outcomes were: mortality (0.84), clinical prediction (0.83), hospitalization (0.71), and service utilization (0.71).

CONCLUSIONS

EHR data present both opportunities and challenges for clinical risk prediction. There is room for improvement in designing such studies.

Quality Outcomes in the Surgical Intensive Care Unit after Electronic Health Record Implementation

BACKGROUND

The electronic health record (EHR) is increasingly viewed as a means to provide more coordinated, patient-centered care. Few studies consider the impact of EHRs on quality of care in the intensive care unit (ICU) setting.

OBJECTIVES

To evaluate key quality measures of a surgical intensive care unit (SICU) following implementation of the Epic EHR system in a tertiary hospital.

METHODS

A retrospective chart review was undertaken to record quality indicators for all patients admitted to the SICU two years before and two years after EHR implementation. Data from the twelve-month period of transition to EHR was excluded. We collected length of stay, mortality, central line associated blood stream infection (CLABSI) rates, Clostridium difficile (C. diff.) colitis rates, readmission rates, and number of coded diagnoses. To control for variation in the patient population over time, the case mix indexes (CMIs) and APACHE II scores were also analyzed.

RESULTS

There was no significant difference in length of stay, C. diff. colitis, readmission rates, or case mix index before and after EHR. After EHR implementation, the rate of central line blood stream infection (CLABSI) per 1 000 catheter days was 85% lower (2.16 vs 0.39; RR, 0.18; 95% CI, 0.05 to 0.61, p < .005), and SICU mortality was 28% lower (12.2 vs 8.8; RR, 1.35; 95% CI, 1.06 to 1.71, p < .01). Moreover, after EHR there was a significant increase in the average number of coded diagnoses from 17.8 to 20.8 (p < .000).

CONCLUSIONS

EHR implementation was statistically associated with reductions in CLABSI rates and SICU mortality. The EHR had an integral role in ongoing quality improvement endeavors which may explain the changes in CLABSI and mortality, and this invites further study of the impact of EHRs on quality of care in the ICU.

Recurrent Clostridium difficile infection: From colonization to cure

Clostridium difficile infection (CDI) is increasingly prevalent, dangerous and challenging to prevent and manage. Despite intense national and international attention the incidence of primary and of recurrent CDI (PCDI and RCDI, respectively) have risen rapidly throughout the past decade. Of major concern is the increase in cases of RCDI resulting in substantial morbidity, morality and economic burden. RCDI management remains challenging as there is no uniformly effective therapy, no firm consensus on optimal treatment, and reliable data regarding RCDI-specific treatment options is scant. Novel therapeutic strategies are critically needed to rapidly, accurately, and effectively identify and treat patients with, or at-risk for, RCDI. In this review we consider the factors implicated in the epidemiology, pathogenesis and clinical presentation of RCDI, evaluate current management options for RCDI and explore novel and emerging therapies.

Doxycycline and Tigecycline: Two Friendly Drugs with a Low Association with Clostridium Difficile Infection

Clostridium difficile infection (CDI) is known to be associated with prior exposure to many classes of antibiotics. Standard therapy for CDI (i.e., metronidazole and vancomycin) is associated with high recurrence rates. Although tetracycline derivatives such as tetracycline, doxycycline or tigecycline are not the standard therapeutic choices for CDI, they may serve as an alternative or a component of combination therapy. Previous tetracycline or doxycycline usage had been shown to have less association with CDI development. Tigecycline, a broad-spectrum glycylcycline with potency against many gram-positive or gram-negative pathogens, had been successfully used to treat severe or refractory CDI. The in vitro susceptibility of C. difficile clinical isolates to tigecycline in many studies showed low minimal inhibitory concentrations. Tigecycline can suppress in vitro toxin production in both historical and hypervirulent C. difficile strains and reduce spore production in a dose-dependent manner. Tetracycline compounds such as doxycycline, minocycline, and tigecycline possess anti-inflammatory properties that are independent of their antibiotic activity and may contribute to their therapeutic effect for CDI. Although clinical data are limited, doxycycline is less likely to induce CDI, and tigecycline can be considered one of the therapeutic choices for severe or refractory CDI.

Risk factors for recurrent Clostridium difficile infection: a systematic review and meta-analysis

OBJECTIVE

An estimated 20-30% of patients with primary Clostridium difficile infection (CDI) develop recurrent CDI (rCDI) within 2 weeks of completion of therapy. While the actual mechanism of recurrence remains unknown, a variety of risk factors have been suggested and studied. The aim of this systematic review and meta-analysis was to evaluate current evidence on the risk factors for rCDI.

DESIGN

We searched MEDLINE and 5 other databases for subject headings and text related to rCDI. All studies investigating risk factors of rCDI in a multivariate model were eligible. Information on study design, patient population, and assessed risk factors were collected. Data were combined using a random-effects model and pooled relative risk ratios (RRs) were calculated.

RESULTS

A total of 33 studies (n=18,530) met the inclusion criteria. The most frequent independent risk factors associated with rCDI were age≥65 years (risk ratio [RR], 1.63; 95% confidence interval [CI], 1.24-2.14; P=.0005), additional antibiotics during follow-up (RR, 1.76; 95% CI, 1.52-2.05; P<.00001), use of proton-pump inhibitors (PPIs) (RR, 1.58; 95% CI, 1.13-2.21; P=.008), and renal insufficiency (RR, 1.59; 95% CI, 1.14-2.23; P=.007). The risk was also greater in patients previously on fluoroquinolones (RR, 1.42; 95% CI, 1.28-1.57; P<.00001).

CONCLUSIONS

Multiple risk factors are associated with the development of rCDI. Identification of modifiable risk factors and judicious use of antibiotics and PPI can play an important role in the prevention of rCDI.

Attributable inpatient costs of recurrent Clostridium difficile infections

OBJECTIVE

To determine the attributable inpatient costs of recurrent Clostridium difficile infections (CDIs).

DESIGN

Retrospective cohort study.

SETTING

Academic, urban, tertiary care hospital.

PATIENTS

A total of 3,958 patients aged 18 years or more who developed an initial CDI episode from 2003 through 2009.

METHODS

Data were collected electronically from hospital administrative databases and were supplemented with chart review. Patients with an index CDI episode during the study period were followed up for 180 days from the end of their index hospitalization or the end of their index CDI antibiotic treatment (whichever occurred later). Total hospital costs during the outcome period for patients with recurrent versus a single episode of CDI were analyzed using zero-inflated lognormal models.

RESULTS

There were 421 persons with recurrent CDI (recurrence rate, 10.6%). Recurrent CDI case patients were significantly more likely than persons without recurrence to have any hospital costs during the outcome period (P < .001). The estimated attributable cost of recurrent CDI was $11,631 (95% confidence interval, $8,937-$14,588).

CONCLUSIONS

The attributable costs of recurrent CDI are considerable. Patients with recurrent CDI are significantly more likely to have inpatient hospital costs than patients who do not develop recurrences. Better strategies to predict and prevent CDI recurrences are needed.

Clinical characteristics of relapses and re-infections in Clostridium difficile infection

The purpose of this study was to identify factors associated with relapses or re-infections in patients with recurring Clostridium difficile infections (CDIs). From September 2008 to January 2012, cases with two or more isolates from consecutive CDI episodes were included. PCR-ribotyping and multilocus variable-number tandem-repeat analysis were performed using paired isolates. Among 473 patients, 68 (14.4%) experienced one to five recurrences. Fifty-one of these with two or more isolates from consecutive CDI episodes were included in the study; 25 (49%) were classified as relapses and 26 (51%) as re-infections. Recurrence interval was shorter in the relapse group (26.0 versus 67.5 p 0.001), but more patients in the re-infection group were hospitalized during recurrence interval (53.8% versus 8.0%, p<0.001). Relapse rates in infections by ribotype 017, ribotype 018 and other ribotypes were 63.6%, 63.6% and 22.2%, respectively (p 0.274, p 0.069, and p 0.005). In multivariate logistic regression, infections by ribotypes 017 and 018 were associated with CDI relapse (OR 4.77, 95% CI 1.02-22.31, p 0.047; OR 11.49, 95% CI 2.07-63.72, p 0.005). Conversely, admission during recurrence interval lowered the risk of relapse (OR 0.044, 95% CI 0.006-0.344, p 0.003). In conclusion, relapse was more likely when infection was caused by PCR ribotypes 017 and 018.

Development and validation of a recurrent Clostridium difficile risk-prediction model

BACKGROUND

Recurrent Clostridium difficile infection (rCDI) affects 10% to 25% of patients with initial CDI (iCDI). Initiation of new therapies that reduce recurrences rests on identifying patients at high risk for rCDI at iCDI onset.

OBJECTIVE

To develop a predictive model for rCDI based on factors present at iCDI onset.

DESIGN

Retrospective cohort study.

SETTING

Large urban academic medical center.

PATIENTS

All adult patients with an inpatient iCDI from January 1, 2003 to December 31, 2009.

INTERVENTION

None.

MEASUREMENTS

Positive toxin assay for C difficile with no C difficile infection in the previous 60 days constituted iCDI. Repeat positive toxin within 42 days of stopping iCDI treatment defined rCDI. Three demographic, 13 chronic, and 12 acute disease characteristics, and 7 processes of care prior to or at the onset of iCDI, were assessed for association with rCDI. A logistic regression model to identify predictors for rCDI was developed and cross-validated.

RESULTS

Among the 4196 patients enrolled, 425 (10.1%) developed rCDI. Six factors (case status as community-onset healthcare-associated, ≥2 hospitalizations in the prior 60 days, new gastric acid suppression, fluoroquinolone and high-risk antibiotic use at the onset of iCDI, age) predicted rCDI in multivariate analyses, whereas intensive care unit stay appeared protective. The model achieved moderate discrimination (C statistic 0.643) and calibration (Brier score 0.089). Its negative predictive value was 90% or higher across a wide range of risk.

CONCLUSIONS

Among patients hospitalized with rCDI, multiple factors present at the onset of iCDI increased the risk for rCDI. Recognizing patients at high-risk for rCDI can help clinicians tailor early treatment and prevention.

Use of Department of Veterans Affairs administrative data to identify veterans with acute low back pain: a pilot study

STUDY DESIGN

This work compared administrative data obtained from the Department of Veterans Affairs (VA) databases with structured chart review.

OBJECTIVE

We set out to determine whether a decision tool using administrative data could discriminate acute from nonacute cases among the many patients seen for a low back pain (LBP)-related diagnosis.

SUMMARY OF BACKGROUND DATA

Large health care systems' databases present an opportunity for conducting research and planning operations related to the management of highly burdensome conditions. An efficient method of identifying cases of acute LBP in these databases may be useful.

METHODS

This was a retrospective review of all consecutive Iraq and/or Afghanistan Veterans seen in a VA primary care service during a 6-month period. Administrative data were extracted from VA databases. Patients with at least 1 encounter that was coded with at least 1 LBP-related ICD-9 code were included. Structured chart review of electronic medical record free text was the "gold standard" to determine acute LBP cases. Logistic regression models were used to assess the association of administrative data variables with chart review findings.

RESULTS

We obtained complete data on 354 patient encounters, of which 83 (23.4%) were designated acute upon chart review. No diagnostic code was more likely to be used in acute cases than nonacute. We identified an administrative data model of 18 variables that were significant and positively associated with an acute case (C-statistic = 0.819). A reduced model of 5 variables including a lumbar magnetic resonance imaging order, tramadol prescription, skeletal muscle relaxant prescription, physical therapy order, and addition of a new LBP-related ICD-9 code to the electronic medical record remained reasonable (C-statistic = 0.784).

CONCLUSION

Our results suggest that a decision model can identify acute from nonacute LBP cases in Veterans using readily available VA administrative data.

LEVEL OF EVIDENCE

N/A.

Clostridium difficile infection in the hematopoietic unit: a meta-analysis of published studies

Hematopoietic stem cell transplant (HSCT) recipients are at high risk of contracting Clostridium difficile infection (CDI). We systematically searched the PubMed and EMBASE databases through March 2014 and performed a random-effects meta-analysis to estimate the prevalence and trends of CDI over time. Among 48 eligible articles that included 12,025 patients at risk, we estimated that 7.9% (95% confidence interval [CI], 6.5% to 9.5%) of HSCT patients are diagnosed with CDI during the peri-transplantation and late post-transplantation periods, an estimation that is relatively consistent across studies (τ(2) = .032). Prevalence of CDI is significantly higher among the 5120 allogeneic patients (9.3% [95% CI, 7.0% to 11.9%]), compared with the 4665 autologous patients (5.2% [95% CI, 3.8% to 6.9%]) (P = .02), and as many as 1 of 10 allogeneic transplant recipients are expected to be diagnosed with CDI compared with 1 of 20 autologous transplantation patients. However, this difference did not reach statistical significance when stratified data from the same centers were examined (P = .11). Importantly, we found an increasing trend of CDI diagnosis both worldwide (P = .02) and across studies conducted in North America (P = .03) over the last 34 years. Notably, studies with a follow-up period that extended through the late post-transplantation period (after day +100) had a similar prevalence of CDI as those that followed patients only during the peri-transplantation period (up to day +100) (P = .94). In summary, CDI is common in the hematopoietic transplantation setting and the majority of infections occur in the peri-transplantation period. The prevalence is almost 9-times higher than that reported among all hospital stays, with an increasing trend over time.

Fidaxomicin in Clostridium difficile infection: latest evidence and clinical guidance

The incidence of Clostridium difficile infection (CDI) has risen 400% in the last decade. It currently ranks as the third most common nosocomial infection. CDI has now crossed over as a community-acquired infection. The major failing of current therapeutic options for the management of CDI is recurrence of disease after the completion of treatment. Fidaxomicin has been proven to be superior to vancomycin in successful sustained clinical response to therapy. Improved outcomes may be due to reduced collateral damage to the gut microflora by fidaxomicin, bactericidal activity, inhibition of Clostridial toxin formation and inhibition of new sporulation. This superiority is maintained in groups previously reported as being at high risk for CDI recurrence including those: with relapsed infection after a single treatment course; on concomitant antibiotic therapy; aged >65 years; with cancer; and with chronic renal insufficiency. Because the acquisition cost of fidaxomicin far exceeds that of metronidazole or vancomycin, in order to rationally utilize this agent, it should be targeted to those populations who are at high risk for relapse and in whom the drug has demonstrated superiority. In this manuscript is reviewed the changing epidemiology of CDI, current treatment options for this infection, proposed benefits of fidaxomicin over currently available antimicrobial options, available analysis of cost effectiveness of the drug, and is given recommendations for judicious use of the drug based upon the available published literature.

Clinical significance of methicillin-resistant Staphylococcus aureus colonization on hospital admission: one-year infection risk

BACKGROUND

Methicillin-resistant Staphylococcus aureus (MRSA) nasal colonization among inpatients is a well-established risk factor for MRSA infection during the same hospitalization, but the long-term risk of MRSA infection is uncertain. We performed a retrospective cohort study to determine the one-year risk of MRSA infection among inpatients with MRSA-positive nasal polymerase chain reaction (PCR) tests confirmed by positive nasal culture (Group 1), patients with positive nasal PCR but negative nasal culture (Group 2), and patients with negative nasal PCR (Group 3).

METHODOLOGY/PRINCIPAL FINDINGS

Subjects were adults admitted to a four-hospital system between November 1, 2006 and March 31, 2011, comprising 195,255 admissions. Patients underwent nasal swab for MRSA PCR upon admission; if positive, nasal culture for MRSA was performed; if recovered, MRSA was tested for Panton-Valentine Leukocidin (PVL). Outcomes included MRSA-positive clinical culture and skin and soft tissue infection (SSTI). Group 1 patients had a one-year risk of MRSA-positive clinical culture of 8.0% compared with 3.0% for Group 2 patients, and 0.6% for Group 3 patients (p<0.001). In a multivariable model, the hazard ratios for future MRSA-positive clinical culture were 6.52 (95% CI, 5.57 to 7.64) for Group 1 and 3.40 (95% CI, 2.70 to 4.27) for Group 2, compared with Group 3 (p<0.0001). History of MRSA and concurrent MRSA-positive clinical culture were significant risk factors for future MRSA-positive clinical culture. Group 1 patients colonized with PVL-positive MRSA had a one-year risk of MRSA-positive clinical culture of 10.1%, and a one-year risk of MRSA-positive clinical culture or SSTI diagnosis of 21.7%, compared with risks of 7.1% and 12.5%, respectively, for patients colonized with PVL-negative MRSA (p = 0.04, p = 0.005, respectively).

CONCLUSIONS/SIGNIFICANCE

MRSA nasal colonization is a significant risk factor for future MRSA infection; more so if detected by culture than PCR. Colonization with PVL-positive MRSA is associated with greater risk than PVL-negative MRSA.

Clostridium difficile infection in older adults

Clostridium difficile infection, the most frequent cause of nosocomial diarrhea, disproportionately affects older adults. The two most important risk factors for developing C. difficile infection are antimicrobial exposure and age >65 years old. Risk factors specific to older adults are frequent interactions with healthcare systems and age-related changes in physiology, including immune senescence and changes to the gut microbiome. Metronidazole and oral vancomcyin are the mainstays of conventional treatment for C. difficile infection. Alternative therapies include fidaxomicin, a narrow-spectrum macrocyclic antibiotic, and fectal bacteriotherapy, which offers an excellent therapeutic outcome. Strategies to prevent C. difficile infections include enhanced infection control measures and reducing inappropriate antimicrobial use through stewardship.

Predictive diagnostics for Escherichia coli infections based on the clonal association of antimicrobial resistance and clinical outcome

The ability to identify bacterial pathogens at the subspecies level in clinical diagnostics is currently limited. We investigated whether splitting Escherichia coli species into clonal groups (clonotypes) predicts antimicrobial susceptibility or clinical outcome. A total of 1,679 extraintestinal E. coli isolates (collected from 2010 to 2012) were collected from one German and 5 U.S. clinical microbiology laboratories. Clonotype identity was determined by fumC and fimH (CH) sequencing. The associations of clonotype with antimicrobial susceptibility and clinical variables were evaluated. CH typing divided the isolates into >200 CH clonotypes, with 93% of the isolates belonging to clonotypes with ≥ 2 isolates. Antimicrobial susceptibility varied substantially among clonotypes but was consistent across different locations. Clonotype-guided antimicrobial selection significantly reduced "drug-bug" mismatch compared to that which occurs with the use of conventional empirical therapy. With trimethoprim-sulfamethoxazole and fluoroquinolones, the drug-bug mismatch was predicted to decrease 62% and 78%, respectively. Recurrent or persistent urinary tract infection and clinical sepsis were significantly correlated with specific clonotypes, especially with CH40-30 (also known as H30), a recently described clonotype within sequence type 131 (ST131). We were able to clonotype directly from patient urine samples within 1 to 3 h of obtaining the specimen. In E. coli, subspecies-level identification by clonotyping can be used to significantly improve empirical predictions of antimicrobial susceptibility and clinical outcomes in a timely manner.