ArticleBMC infectious diseases2026
Prediction hospital mortality for critical illness lung cancer patients with pneumonia.
Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Association between elevated multiple circulating biomarkers and short-term mortality in critically ill lung cancer patients: a meta-analysis.Frontiers in oncology · 2026Pooled it
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
backgroundPneumonia is a common and severe complication in patients with lung cancer, often resulting in prolonged intensive care stays and increased risk of death. Despite this, no predictive models have been specifically developed for this high-risk population to aid clinical decision-making and early risk identification.
methodsThis study retrospectively analyzed patient data from two large critical care databases: one used for model development and the other for external validation. Adult patients with a diagnosis of lung cancer and pneumonia were included. Clinical features associated with in-hospital death were first screened using single-variable regression, and those with statistical significance were further refined using a variable selection method based on penalized regression. A visual prediction tool was then developed using multivariable regression analysis. Performance was evaluated using standard metrics of discrimination and calibration. Additional machine learning algorithms, including tree-based models, were used to compare performance. Survival analysis was conducted to assess risk grouping capability.
resultsA total of 1046 patients were included in the final analysis. The visual prediction tool incorporated clinical features such as severity scores, mental status assessments, white blood cell count, blood gas indicators, and use of life-support measures. It demonstrated high predictive accuracy (C-index: 0.763) in the external test cohort. The tool outperformed several commonly used machine learning models. Survival curves showed a clear distinction between high-risk and low-risk groups. Calibration and decision analysis confirmed the tool’s clinical usefulness.
conclusionsThis study developed and validated a practical, interpretable prediction model for hospital mortality in patients with lung cancer complicated by pneumonia. The tool enables risk stratification and supports personalized clinical management in intensive care settings. CLINICAL TRAIN NUMBER: Not applicable.
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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 Socratic graph.