Predictive Modeling for Clostridioides difficile Infection: Current State of the Science, Clinical Applications, and Future Directions.

Krishna Rao, Jenna Wiens

Abstract readReview

In one paragraph

Review in Infectious disease clinics of North America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Krishna RaoDivision of Infectious Diseases, Department of Internal Medicine, University of Michigan Medical School, 1150 W. Medical Center Dr, 1510B MSRB1, Ann Arbor, MI 48103 USA. Electronic address: krirao@umich.edu.

Jenna WiensDivision of Computer Science and Engineering, Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, 2260 Hayward Street, Ann Arbor, MI 48109, USA.

Funding

AHRQ HHS R01 HS027431

6 · The paper itself

Abstract

Despite 2 decades of effort, there is a lack of clinically deployed models for predicting incident, severe, or recurrent Clostridioides difficile infection (CDI). This review outlines the promise of machine learning and biomarker-augmented models for targeted prevention and treatment, but also emphasizes the challenges of real-world deployment-namely integration into clinical workflows and governance. Moving forward, progress will depend on translational biomarker development, pragmatic modeling pipelines, and continuous monitoring. With these elements in place, CDI prediction tools can become a template for precision prevention of healthcare-associated infections.

Indexed as

Clostridioides difficileClostridium InfectionsBiomarkersCross InfectionHumansMachine LearningBiomarkersArtificial intelligenceClinical decision makingClostridioides difficile infectionMachine learningPredictive modeling

Identifiers

PMID40897568

DOI10.1016/j.idc.2025.07.015

PMCPMC13196788

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

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.