ArticleJournal of community hospital internal medicine perspectives2015
A prediction model for Clostridium difficile recurrence.
Article in Journal of community hospital internal medicine perspectives, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.F1000Research · 2019Pooled it
- Predictive Modeling for Clostridioides difficile Infection: Current State of the Science, Clinical Applications, and Future Directions.Infectious disease clinics of North America · 2025Review
- Fecal microbiota composition is a better predictor of recurrent Clostridioides difficile infection than clinical factors in a prospective, multicentre cohort study.BMC infectious diseases · 2024Observational
- An integrated pipeline for prediction of Clostridioides difficile infection.Scientific reports · 2023Article
- Microbiome profile and calprotectin levels as markers of risk of recurrentFrontiers in cellular and infection microbiology · 2023Article
- Gut metabolites predict Clostridioides difficile recurrence.Microbiome · 2022Article
- External validation of two prediction tools for patients at risk for recurrentTherapeutic advances in gastroenterology · 2021Article
- Neighborhood disadvantage and 30-day readmission risk following Clostridioides difficile infection hospitalization.BMC infectious diseases · 2020Observational
- Evaluation of a risk score to predict futureHuman vaccines & immunotherapeutics · 2019Observational
- A comprehensive approach for the patient with Clostridium difficile infection.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2018Review
- Editor's notes.Journal of community hospital internal medicine perspectives · 2015Article
- WSES guidelines for management of Clostridium difficile infection in surgical patients.World journal of emergency surgery : WJES · 2015Review
- 2019 update of the WSES guidelines for management ofWorld journal of emergency surgery : WJESReview
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClostridium difficile infection (CDI) is a growing problem in the community and hospital setting. Its incidence has been on the rise over the past two decades, and it is quickly becoming a major concern for the health care system. High rate of recurrence is one of the major hurdles in the successful treatment of C. difficile infection. There have been few studies that have looked at patterns of recurrence. The studies currently available have shown a number of risk factors associated with C. difficile recurrence (CDR); however, there is little consensus on the impact of most of the identified risk factors.
methodsOur study was a retrospective chart review of 198 patients diagnosed with CDI via Polymerase Chain Reaction (PCR) from February 2009 to Jun 2013. In our study, we decided to use a machine learning algorithm called the Random Forest (RF) to analyze all of the factors proposed to be associated with CDR. This model is capable of making predictions based on a large number of variables, and has outperformed numerous other models and statistical methods.
resultsWe came up with a model that was able to accurately predict the CDR with a sensitivity of 83.3%, specificity of 63.1%, and area under curve of 82.6%. Like other similar studies that have used the RF model, we also had very impressive results.
conclusionsWe hope that in the future, machine learning algorithms, such as the RF, will see a wider application.
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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.