ArticleFrontiers in cellular and infection microbiology2026
Machine learning-assisted prognostic model for mortality in ICU patients with culture-confirmed
Article in Frontiers in cellular and infection microbiology, 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: Methods: A total of 587 adult ICU patients with culture-confirmed KP infection (first positive culture) at a tertiary hospital between August 2020 and February 2025 were retrospectively included. Candidate predictors were screened using least absolute shrinkage and selection operator (LASSO) and bootstrap-based stability selection to identify stable prognostic predictors. Using the final stable predictors, 11 models (including logistic regression and tree-based approaches) with 10-fold cross-validation were developed and compared. Model performance was evaluated using discrimination, calibration, and clinical utility. Generalized additive models (GAMs) were used to explore potential non-linear predictor-outcome associations. Results: Mortality occurred in 122/587 patients (20.8%). Five stable prognostic factors were identified: Acute Physiology and Chronic Health Evaluation II (APACHE II) score, lactate, viral co-infection, acute kidney injury (AKI), and the alveolar-arterial oxygen gradient (A-aDO Conclusion: A parsimonious model based on five stable variables showed good performance for predicting mortality among ICU patients with KP infection. However, these findings are exploratory and require external validation before any clinical application.
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.