ArticlePeerJ2025
Construction and validation of a risk prediction model for postoperative pulmonary infection in patients with brain tumor: a retrospective study.
Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07321262 (An Interpretable and Clinically Deployable Machine Learning Model for Predicting Early Postoperative Pneumonia of Brain Tumor), which is not on this map. Cited by 2 papers.
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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
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An Interpretable and Clinically Deployable Machine Learning Model for Predicting Early Postoperative Pneumonia of Brain Tumor: a Multicenter Diagnostic Study
Who cites it
2 citing papers in PubMed.
- Early risk stratification of postoperative pneumonia after brain tumor surgery using routine perioperative variables: development and prospective multicenter validation of an interpretable prediction model.Frontiers in cellular and infection microbiology · 2026Article
- Risk Factors Analysis and Nomogram Prediction Model Construction for Pulmonary Infection After Laparoscopic Cholecystectomy.Infection and drug resistance · 2026Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: This study aimed to investigate the influencing factors and construct a risk prediction model for postoperative pulmonary infection in patients with brain tumor. Methods: This investigation encompassed a cohort of 636 individuals who were diagnosed with brain tumors and underwent surgical treatment between October 2019 and October 2023. According to the ratio of 7:3, the patients were randomly divided into training set and validation set. Univariate analysis and multivariate Logistic regression analysis were performed on the data in the training set. Finally, the independent risk factors of postoperative pulmonary infection in patients with brain tumor were screened out. R software was used to establish a nomogram model for predicting the risk of postoperative pulmonary infection. Receiver operating characteristic (ROC) curve, calibration curve and Hosmer-Lemeshow test were used to evaluate the discrimination and calibration of the model. Decision curve analysis was used to evaluate the clinical benefit of the model. Results: The prevalence of postoperative pulmonary infection in patients with brain tumors was 17.9%. The nomogram contained several independent risk factors: age ≥ 60 years, diabetes mellitus, GCS score < 13 points, postoperative bedtime, and postoperative D-Dimer. The prediction model yielded an area under the curve (AUC) of 0.814 (95% confidence interval CI [0.756-0.873]) in the training set, and an AUC of 0.752 (95% CI [0.653-0.850]) in the validation set. The Conclusions: Age ≥ 60 years, diabetes mellitus, GCS score < 13 points, postoperative bedtime and postoperative D-Dimer are risk factors for postoperative pulmonary infection in patients with brain tumor. The developed prediction model demonstrates substantial predictive value and clinical applicability, serving as a valuable reference for medical professionals in recognizing postoperative pulmonary infections in patients with brain tumors and facilitating preventive nursing measures.
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