ArticleInfection control and hospital epidemiology2026
Prospective validation and implementation of a model to identify patients with carbapenem-resistant Enterobacterales (CRE) carriage on admission to acute care hospitals.
Article in Infection control and hospital epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveUse the electronic health record (EHR) to prospectively validate a risk prediction tool identifying patients at high-risk for carbapenem-resistant Enterobacterales (CRE).
designProspective, cross-sectional analysis.
participantsAdults admitted to two hospitals in Atlanta, Georgia.
methodsAn EHR report calculated a CRE risk score on all admissions from 6/2024-3/2025. The risk score was determined from a prior model incorporating data from current and prior hospitalizations. Stool or perianal samples were cultured from a convenience sample of patients with the highest risk scores. A receiver operating curve analysis calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive values (NPV) of various risk scores that could be used as a "threshold" for CRE admission testing. Using the threshold with the greatest Youden's index, we estimated the cost of implementing a CRE screening program.
resultsOf the 853 patients approached, 342 (40%) consented. Eleven (3.2%) tested positive for CRE. Patients with CRE had a higher median CRE risk score (0.19% vs 0.04%) than those who tested negative. The AUC of the model was 0.66. Using a testing threshold of 0.16% yielded a 55% sensitivity, 84% specificity, 10% PPV, and 98% NPV. The number needed to screen to diagnose 1 patient with CRE was 12 patients, and the screening program approximately costs $8,015/month.
conclusionsAn EHR-based risk prediction tool can detect patients likely to be colonized with CRE. In facilities with a low CRE prevalence, identifying a high-risk subset of patients to test could be a cost-effective infection prevention initiative.
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.