ArticleFrontiers in public health2026
Development and validation of a novel model based on hematological biomarkers for predicting risk factors and outcomes in cancer patients with carbapenem-resistant organism infection.
Article in Frontiers in public health, 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aimed to develop and validate a novel model for predicting mortality risk in cancer patients with carbapenem-resistant organism (CRO) infections. Methods: Cancer patients with CRO infections in Henan Cancer Hospital between January 2022 and March 2024 were included in this retrospective study. LASSO regression was used to construct a novel model for predicting mortality in cancer patients with CRO infections. Receiver operating characteristic (ROC), decision curve analysis (DCA), and clinical impact curves (CIC) were used to assess the predictive ability and clinical utility of the prediction model. Results: A total of 417 cancer patients with CRO infections were included in the study. Fourteen factors were selected, including sample source, radiotherapy, blood culture, exposure to antibiotics after susceptibility testing, lymphocyte-to-monocyte ratio (LMR), lymphocyte-to-monocyte ratio (LMR), total protein (TP), blood urea nitrogen (BUN), calcium (CA), C-reactive protein (CRP), triglyceride (TG), procalcitonin (PCT), prothrombin time (PT), and thrombin time (TT), which were found to be associated with 30-day mortality in cancer patients with CRO infections. The areas under the ROC curve for the prediction model were 0.815 (95% CI: 0.767-0.857) and 0.801 (95% CI: 0.716-0.871) for the primary cohort and validation cohort, respectively. The models demonstrated good predictive accuracy, with Conclusion: Our model could be used as an effective individualized risk prediction tool for clinicians. It would provide personalized risk assessments for cancer patients with CRO infections.
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.