ArticleVaccine2015
Identification of population at risk for future Clostridium difficile infection following hospital discharge to be targeted for vaccine trials.
Article in Vaccine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- Predictive Modeling for Clostridioides difficile Infection: Current State of the Science, Clinical Applications, and Future Directions.Infectious disease clinics of North America · 2025Review
- An integrated pipeline for prediction of Clostridioides difficile infection.Scientific reports · 2023Article
- A new score to predict Clostridioides difficile infection in medical patients: a sub-analysis of the FADOI-PRACTICE study.Internal and emergency medicine · 2023Article
- A Risk Score to PredictOpen forum infectious diseases · 2021Article
- Reductions in inpatient fluoroquinolone use and postdischargeAntimicrobial stewardship & healthcare epidemiology : ASHE · 2021Article
- Evaluation of a risk score to predict futureHuman vaccines & immunotherapeutics · 2019Observational
- Increasing Age Has Limited Impact on Risk ofOpen forum infectious diseases · 2018Article
- Predictors of Early Failure After Fecal Microbiota Transplantation for the Therapy of Clostridium Difficile Infection: A Multicenter Study.The American journal of gastroenterology · 2016Article
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundEfforts to develop a Clostridium difficile vaccine are underway; identification of patients at risk for C. difficile infection (CDI) is critical to inform vaccine trials. We identified groups at high risk of CDI ≥ 2 8 days after hospital discharge.
methodsHospital discharge data and pharmacy data from two large academic centers, in New York and Connecticut, were linked to active population-based CDI surveillance data from the Emerging Infections Program (EIP). Adult residents of the EIP surveillance area were included if they had an inpatient stay at a study hospital without prior history of CDI. The primary outcome was CDI by either toxin or molecular assay ≥ 28 days after an index hospitalization. Important predictors of CDI ≥ 28 days post discharge were initially identified through a Cox proportional hazards model (stepwise backward selection) using a derivation cohort; final model parameters were used to develop a risk score evaluated in the validation cohort.
resultsOf the 35,186 index hospitalizations, 288 (0.82%) had CDI ≥ 28 days post discharge. After parameter selection, age, number of hospitalizations in the prior 90 days, admission diagnosis, and the use of 3rd/4th generation cephalosporin, clindamycin or fluoroquinolone antibiotic classes remained in the model. Using the validation cohort, the risk score was predictive (p<0.001) with a c-score of 0.75. Based on the distribution of scores in the derivation cohort, we divided the patients into low and high risk groups. In the high risk group, 1.6% experienced CDI ≥ 28 days post discharge compared to 0.3% among our low risk group.
conclusionsOur study identified specific parameters for a risk score that can be applied at discharge to identify a patient population whose risk of CDI ≥ 28 days following an acute care hospitalization was 5 times greater than other patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.