ArticleInfection control and hospital epidemiology2011
Development and validation of a Clostridium difficile infection risk prediction model.
Article in Infection control and hospital epidemiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Predictive Modeling for Clostridioides difficile Infection: Current State of the Science, Clinical Applications, and Future Directions.Infectious disease clinics of North America · 2025Review
- Guiding Clostridioides difficile Infection Prevention Efforts in a Hospital Setting With AI.JAMA network open · 2025Article
- Predicting positiveHeliyon · 2025Article
- Curbing inappropriateAntimicrobial stewardship & healthcare epidemiology : ASHE · 2025Article
- Development of a Prediction Model to Identify the Risk ofPharmacy (Basel, Switzerland) · 2024Article
- An integrated pipeline for prediction of Clostridioides difficile infection.Scientific reports · 2023Article
- Strategies to preventInfection control and hospital epidemiology · 2023Article
- Clinical prediction rule for identifying older patients with toxigenic clostridioides difficile at the time of hospital admission.BMC geriatrics · 2023Article
- Oral Vancomycin Prophylaxis for Primary and Secondary Prevention ofAntibiotics (Basel, Switzerland) · 2022Review
- Risk-aware temporal cascade reconstruction to detect asymptomatic cases.Knowledge and information systems · 2022Article
- An Osmotic Laxative Renders Mice Susceptible to Prolonged Clostridioides difficile Colonization and Hinders Clearance.mSphere · 2021Article
- Characterizing Risk Factors for Clostridioides difficile Infection among Hospitalized Patients with Community-Acquired Pneumonia.Antimicrobial agents and chemotherapy · 2021Article
- Designing an Elderly Hospital Admission Risk Prediction Model in Iran's Hospitals.International journal of preventive medicine · 2021Article
- Article
- Evaluation of a risk score to predict futureHuman vaccines & immunotherapeutics · 2019Observational
- Stress Ulcer Prophylaxis in Neurocritical Care.Neurocritical care · 2018Review
- Learning Credible Models.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2018Article
- The Systemic Lupus Erythematosus Infection Predictive Index (LIPI): A Clinical-Immunological Tool to Predict Infections in Lupus Patients.Frontiers in immunology · 2018Article
- Article
- Estimating Local Costs Associated With Clostridium difficile Infection Using Machine Learning and Electronic Medical Records.Infection control and hospital epidemiology · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectiveTo develop and validate a risk prediction model that could identify patients at high risk for Clostridium difficile infection (CDI) before they develop disease. DESIGN AND
settingRetrospective cohort study in a tertiary care medical center. PATIENTS: Patients admitted to the hospital for at least 48 hours during the calendar year 2003.
methodsData were collected electronically from the hospital's Medical Informatics database and analyzed with logistic regression to determine variables that best predicted patients' risk for development of CDI. Model discrimination and calibration were calculated. The model was bootstrapped 500 times to validate the predictive accuracy. A receiver operating characteristic curve was calculated to evaluate potential risk cutoffs.
resultsA total of 35,350 admitted patients, including 329 with CDI, were studied. Variables in the risk prediction model were age, CDI pressure, times admitted to hospital in the previous 60 days, modified Acute Physiology Score, days of treatment with high-risk antibiotics, whether albumin level was low, admission to an intensive care unit, and receipt of laxatives, gastric acid suppressors, or antimotility drugs. The calibration and discrimination of the model were very good to excellent (C index, 0.88; Brier score, 0.009).
conclusionsThe CDI risk prediction model performed well. Further study is needed to determine whether it could be used in a clinical setting to prevent CDI-associated outcomes and reduce costs.
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