Evidence map›Paper›PMID 42608005›Full record

ArticleJornal de pediatria2026

Dynamic multimodal brain function monitoring enables quantitative severity grading and prognostic prediction in pediatric neurocritical care.

Yanmei Wang, Haili Wang, Minglei Li, Xianli Shao, Huafeng Zhao

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Article in Jornal de pediatria, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Yanmei WangDepartment of Pediatric Internal Medicine One, Weifang People's Hospital, Weifang City, Shandong Province, China.
Haili WangDepartment of Pediatric Internal Medicine One, Weifang People's Hospital, Weifang City, Shandong Province, China.
Minglei LiDepartment of Pediatric Internal Medicine One, Weifang People's Hospital, Weifang City, Shandong Province, China.
Xianli ShaoDepartment of Pediatric Internal Medicine One, Weifang People's Hospital, Weifang City, Shandong Province, China.
Huafeng ZhaoDepartment of Pediatric Internal Medicine One, Weifang People's Hospital, Weifang City, Shandong Province, China. Electronic address: rmyyekzhf@sdsmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveMultimodal Brain Function Monitoring (MBFM) enables simultaneous assessment of cerebral blood flow, intracranial pressure (ICP), and brain tissue oxygenation, providing dynamic evaluation for pediatric neurocritical care. This study evaluated MBFM-derived parameters, including perturbation factor (PF), edema factor (EF), ICP, and regional cerebral oxygen saturation (rSO₂), for disease severity stratification and prognostic prediction.

methodsWe prospectively enrolled 120 pediatric patients (6 months-12 years) in the PICU of the Children's Hospital, the Weifang People's Hospital (January 2022- November 2025). Continuous MBFM monitoring was performed for 72 h. Patients were stratified into mild, moderate, and severe groups using K-means and hierarchical clustering. Key predictive thresholds were identified via CART decision tree. Predictive performance for adverse outcomes, including Glasgow Outcome Scale scores, complications, and survival, was assessed by ROC analysis, logistic regression, Kaplan-Meier survival analysis, and decision curve analysis.

resultsPF, ICP, EF, and rSO₂ significantly differed among severity groups. PF predicted adverse outcomes with an AUC 0.86 (cutoff 0.34), while the combined model (PF + EF + ICP + rSO₂) improved the AUC to 0.91. Patients with PF > 0.34 and severe disease showed reduced 6-month survival (Log-rank χ² = 7.76, P = 0.005).

conclusionsMBFM provides a quantitative framework for grading pediatric neurocritical illness. PF is a core predictor, and integrating multimodal metrics enhances prognostic accuracy, guiding PICU interventions and outcome prediction.

Indexed as

Multimodal brain function monitoringPediatric neurocritical carePerturbation factorPrognostic predictionSeverity grading

Identifiers

PMID42608005
PMCPMC13571992

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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.