ArticleInfection control and hospital epidemiology2017
Prediction of Recurrent Clostridium Difficile Infection Using Comprehensive Electronic Medical Records in an Integrated Healthcare Delivery System.
Article in Infection control and hospital epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Recommendations for the diagnosis and treatment of Clostridioides difficile infection: An official clinical practice guideline of the Spanish Society of Chemotherapy (SEQ), Spanish Society of Internal Medicine (SEMI) and the working group of Postoperative Infection of the Spanish Society of Anesthesia and Reanimation (SEDAR).Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2020Guideline
- Predictive Modeling for Clostridioides difficile Infection: Current State of the Science, Clinical Applications, and Future Directions.Infectious disease clinics of North America · 2025Review
- Enhancing Infection Control in ICUS Through AI: A Literature Review.Health science reports · 2025Article
- Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study.BMC infectious diseases · 2024Observational
- Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- 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
- Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine.Diagnostics (Basel, Switzerland) · 2021Review
- Anti-toxin antibody is not associated with recurrent Clostridium difficile infection.Anaerobe · 2021Article
- Risk Factors for PrimaryFrontiers in public health · 2020Observational
- Doctor, my patient has CDI and should continue to receive antibiotics. The (unresolved) risk of recurrent CDI.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2019Review
- Using Machine Learning and the Electronic Health Record to Predict ComplicatedOpen forum infectious diseases · 2019Article
- Recent Progress for the Effective Prevention and Treatment of RecurrentInfectious diseases · 2018Article
- Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
BACKGROUND Predicting recurrent Clostridium difficile infection (rCDI) remains difficult.
methodsWe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.