• Machine learning-based predict...

Treffer: Machine learning-based predictive model for high- grade cytokine release syndrome in chimeric antigen receptor T-cell therapy.

Title:
Machine learning-based predictive model for high- grade cytokine release syndrome in chimeric antigen receptor T-cell therapy.
Authors:
Yu X; Clinical Medicine Research Center, Shandong Second Provincial General Hospital, Jinan, China., Wang Q; Clinical Medicine Research Center, Shandong Second Provincial General Hospital, Jinan, China., Halimulati T; Clinical Medicine Research Center, Shandong Second Provincial General Hospital, Jinan, China., Lv J; Clinical Medicine Research Center, Shandong Second Provincial General Hospital, Jinan, China., Zhou K; Department of General Surgery, The First Affiliated Hospital, Army Medical University, Chongqing, China., Chen G; Clinical Medicine Research Center, Shandong Second Provincial General Hospital, Jinan, China., Yin L; Clinical Medicine Research Center, Shandong Second Provincial General Hospital, Jinan, China., Liu Y; Clinical Medicine Research Center, Shandong Second Provincial General Hospital, Jinan, China., Bi J; Clinical Medicine Research Center, Shandong Second Provincial General Hospital, Jinan, China., Xiang Z; Clinical Medicine Research Center, Shandong Second Provincial General Hospital, Jinan, China., Wang Q; Clinical Medicine Research Center, Shandong Second Provincial General Hospital, Jinan, China.
Source:
Frontiers in immunology [Front Immunol] 2025 Nov 20; Vol. 16, pp. 1692892. Date of Electronic Publication: 2025 Nov 20 (Print Publication: 2025).
Publication Type:
Comparative Study; Journal Article; Observational Study; Validation Study
Language:
English
Journal Info:
Publisher: Frontiers Research Foundation] Country of Publication: Switzerland NLM ID: 101560960 Publication Model: eCollection Cited Medium: Internet ISSN: 1664-3224 (Electronic) Linking ISSN: 16643224 NLM ISO Abbreviation: Front Immunol Subsets: MEDLINE
Imprint Name(s):
Original Publication: [Lausanne : Frontiers Research Foundation]
MeSH Terms:
Machine Learning* , Immunotherapy, Adoptive*/adverse effects , Cytokine Release Syndrome*/etiology , COVID-19*/complications , Predictive Learning Models*, Retrospective Studies ; Humans ; Male ; Female ; Aged
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Contributed Indexing:
Keywords: CAR-T therapy; COVID-19; XGBoost model; cytokine release syndrome; machine learning technique
Entry Date(s):
Date Created: 20251208 Date Completed: 20251215 Latest Revision: 20251215
Update Code:
20251216
PubMed Central ID:
PMC12675352
DOI:
10.3389/fimmu.2025.1692892
PMID:
41357188
Database:
MEDLINE

Weitere Informationen

Introduction: The development of robust predictive models for high-grade cytokine release syndrome (CRS) in CAR-T recipients remains limited by sparse clinical trial data.
Methods: We analyzed of 496 COVID-19 patients revealed that CRS plays a pivotal role in disease progression and serves as a valuable data source for understanding CRS progression. Building on this insight, we evaluated and compared the predictive performance of three machine learning models, with the ultimate goal of developing a predictive model for high-grade CRS in patients receiving CAR-T therapy.
Results: Among evaluated algorithms (XGBoost, Random Forest, Logistic Regression), XGBoost demonstrated superior performance in high-grade CRS prediction. Feature importance analysis identified SpO2, D-dimer, diastolic blood pressure, and INR as key predictors, enabling development of a validated riskassessment algorithm. In an independent CAR-T cohort (n=45), the algorithm achieved impressive predictive performance for high-grade CRS prediction.
Discussion: Using machine learning, we identified key clinical biomarkers strongly associated with high-grade CRS. This tool efficiently predicts progression to high-grade CRS post-onset and shows significant potential for clinical deployment in CAR-T therapy.
(Copyright © 2025 Yu, Wang, Halimulati, Lv, Zhou, Chen, Yin, Liu, Bi, Xiang and Wang.)

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.