Evidence map›Paper›PMID 37854590›Full record

ArticleFrontiers in immunology2023

Prediction of severe CRS and determination of biomarkers in B cell-acute lymphoblastic leukemia treated with CAR-T cells.

Zhenyu Wei, Jiayu Xu, Chengkui Zhao, Min Zhang, Nan Xu, Liqing Kang, Xiaoyan Lou, Lei Yu, Weixing Feng

Erratum issuedOpen access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
6.7field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

  1. Pooled it
  2. B-cell targeting strategies for the treatment of multiple sclerosis: from non-cellular therapies to CAR-engineered cell therapies.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
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  16. Deep insight into cytokine storm: from pathogenesis to treatment.Signal transduction and targeted therapy · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Zhenyu WeiIntelligent Systems Science and Engineering College, Harbin Engineering University, Harbin, China.
Jiayu XuIntelligent Systems Science and Engineering College, Harbin Engineering University, Harbin, China.
Chengkui ZhaoIntelligent Systems Science and Engineering College, Harbin Engineering University, Harbin, China.
Min ZhangIntelligent Systems Science and Engineering College, Harbin Engineering University, Harbin, China.
Nan XuShanghai Unicar-Therapy BioMedicine Technology Co., Ltd, Shanghai, China.
Liqing KangShanghai Unicar-Therapy BioMedicine Technology Co., Ltd, Shanghai, China.
Xiaoyan LouShanghai Unicar-Therapy BioMedicine Technology Co., Ltd, Shanghai, China.
Lei YuShanghai Unicar-Therapy BioMedicine Technology Co., Ltd, Shanghai, China.
Weixing FengIntelligent Systems Science and Engineering College, Harbin Engineering University, Harbin, China.
Harbin Engineering University · CNEast China Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: CAR-T cell therapy is a novel approach in the treatment of hematological tumors. However, it is associated with life-threatening side effects, such as the severe cytokine release syndrome (sCRS). Therefore, predicting the occurrence and development of sCRS is of great significance for clinical CAR-T therapy. The study of existing clinical data by artificial intelligence may bring useful information. Methods: By analyzing the heat map of clinical factors and comparing them between severe and non-severe CRS, we can identify significant differences among these factors and understand their interrelationships. Ultimately, a decision tree approach was employed to predict the timing of severe CRS in both children and adults, considering variables such as the same day, the day before, and initial values. Results: We measured cytokines and clinical biomarkers in 202 patients who received CAR-T therapy. Peak levels of 25 clinical factors, including IFN-γ, IL6, IL10, ferritin, and D-dimer, were highly associated with severe CRS after CAR T cell infusion. Using the decision tree model, we were able to accurately predict which patients would develop severe CRS consisting of three clinical factors, classified as same-day, day-ahead, and initial value prediction. Changes in serum biomarkers, including C-reactive protein and ferritin, were associated with CRS, but did not alone predict the development of severe CRS. Conclusion: Our research will provide significant information for the timely prevention and treatment of sCRS, during CAR-T immunotherapy for tumors, which is essential to reduce the mortality rate of patients.

Indexed as

Burkitt LymphomaPrecursor B-Cell Lymphoblastic Leukemia-LymphomaReceptors, Chimeric AntigenAdultArtificial IntelligenceBiomarkersChildFerritinsHumansT-LymphocytesBiomarkersFerritinsReceptors, Chimeric AntigenbiomarkerCAR-T cell therapycytokine release syndrome (CRS)decision treeprediction

Identifiers

PMID37854590
PMCPMC10579557
OpenAlexW4387306935

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.