European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
October 13, 2026 · via Europe PMC
Development and temporal validation of an interpretable prediction model for 30-day meningitis or ventriculitis after pediatric brain tumor surgery: An 895-patient cohort study.
Wang H, Ying Z, Cai Y, Yang W, Zhang N, Zhang D, Jiang T, Ge M
AI summary — for doctors, generated from the abstract only
A model using routine perioperative variables predicted postoperative meningitis or ventriculitis after pediatric brain tumor surgery with moderate-to-good accuracy.
What was studied
A single-center retrospective study of 895 children (2015-2025), training on pre-2023 operations and validating temporally, comparing five machine-learning models with SHAP interpretation.
Key findings
- 274 of 895 patients (30.6%) developed postoperative MEN
- Temporal validation AUCs ranged from 0.771 to 0.808
- Postoperative IVH was the largest contributor to XGBoost predictions
- External drain duration of 7 days or more was associated with MEN (OR 2.34; 95% CI 1.04-5.28)
Clinical relevance
May aid risk stratification and external-drain management.
Who should care
Limitations
Single-center and retrospective; the authors state multicenter validation is required.
Useful but not yet ready for clinical implementation.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
BACKGROUND
Meningitis or ventriculitis (MEN) is an important complication after pediatric brain tumor surgery, yet tools for early risk stratification remain limited. We developed and temporally validated prediction models for postoperative MEN.
METHODS
We retrospectively included pediatric patients who underwent brain tumor resection at a single center during 2015-2025. Operations performed before January 1, 2023 constituted training cohort, and subsequent operations constituted temporal validation cohort. Elastic net-selected predictors were entered into five models: logistic regression (LR), support vector machine, decision tree, random forest (RF), and XGBoost. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration, Brier score, and decision curve analysis. SHapley Additive exPlanations (SHAP) analysis was used to interpret model predictions.
RESULTS
Among 895 patients, 274 (30.6%) developed postoperative MEN, training and validation cohorts comprised 568 and 327 patients. Ten predictors were retained. AUCs in temporal validation ranged from 0.771 to 0.808. LR achieved the highest AUC (0.808), whereas XGBoost remained stable (training 0.823, validation 0.802), with low prediction error, and net benefit. SHAP analysis identified postoperative intraventricular hemorrhage (IVH) as the largest contributor to XGBoost predictions. Among 167 patients with intraoperative external-drain placement, external drain duration ≥7 days was independently associated with postoperative MEN (OR = 2.34; 95% CI: 1.04-5.28).
CONCLUSION
Routinely available perioperative variables enabled temporal prediction of postoperative MEN after pediatric brain tumor surgery. The interpretable XGBoost model may assist postoperative risk stratification and external-drain management, although multicenter validation is required before clinical implementation.
DOI
10.1016/j.ejso.2026.112209
PMID
42887075
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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