ArticleOpen forum infectious diseases2021
A Risk Score to Predict
Article in Open forum infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Derivation and validation of a prediction model for primary and recurrentAntimicrobial stewardship & healthcare epidemiology : ASHE · 2026Article
- Predictive Modeling for Clostridioides difficile Infection: Current State of the Science, Clinical Applications, and Future Directions.Infectious disease clinics of North America · 2025Review
- Modeling and predicting individual variation in COVID-19 vaccine-elicited antibody response in the general population.PLOS digital health · 2024Article
- An integrated pipeline for prediction of Clostridioides difficile infection.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background
methodsWe included these risk factors in logistic regression models to develop 2 risk scores that predict future CDI after an Index Date for Risk Score Assessment (IDRSA), marking the beginning of a period for which we estimated CDI risk.
resultsDuring May 2011 to July 2014, we included 9986 CDI cases and 2 230 354 members without CDI. The CDI cases tended to be older, female, white race, and have more hospitalizations, emergency department and office visits, skilled nursing facility stays, antibiotic and proton pump inhibitor use, and specific comorbidities. Using hospital discharge as the IDRSA, our risk score model yielded excellent performance in predicting the likelihood of developing CDI in the subsequent 31-365 days (C-statistic of 0.848). Using a random date as the IDRSA, our model also predicted CDI risk in the subsequent 31-365 days reasonably well (C-statistic 0.722).
conclusionsThese results can be used to identify high-risk populations for enrollment in
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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.