Author Affiliations: Department of Hematology and Oncology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China (Ms Zhao, Ms He, and Ms Ruan); Department of Nursing, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China (Ms Sun and Ms Shen); School of Nursing, Shanghai Jiao Tong University, Shanghai, China (Mr Lin).
Cancer Nurs. 2025;48(1):3-11. doi: 10.1097/NCC.0000000000001275. Epub 2023 Aug 8.
Chimeric antigen receptor T-cell therapy-related severe cytokine release syndrome (sCRS) has seriously affected the life safety of patients.
To explore the influencing factors of sCRS in children with B-cell hematological malignancies and build a risk prediction model.
The study recruited 115 children with B-cell hematological malignancies who received CD19- and CD22-targeted chimeric antigen receptor T-cell therapy. A nomogram model was established based on symptomatic adverse events and highly accessible clinical variables. The model discrimination was evaluated by the area under the receiver operating characteristic curve. The calibration of our model was evaluated by the calibration curve and Hosmer-Lemeshow test. The bootstrap self-sampling method was used to internally validate.
Thirty-seven percent of the children experienced sCRS. Indicators included in the nomogram were tumor burden before treatment, thrombocytopenia before pretreatment, and the mean value of generalized muscle weakness and headache scores. The results showed that the area under the receiver operating characteristic curve was 0.841, and the calibration curve showed that the probability of sCRS predicted by the nomogram was in good agreement with the actual probability of sCRS. The Hosmer-Lemeshow test indicated that the model fit the data well ( χ2 = 5.759, P = .674). The concordance index (C-index) obtained by internal validation was 0.841 (0.770, 0.912).
The nomogram model constructed has a good degree of discrimination and calibration, which provides a more convenient and visual evaluation tool for identifying the sCRS.
Incorporation of patient-reported outcomes into risk prediction models enables early identification of sCRS.
嵌合抗原受体 T 细胞疗法相关的严重细胞因子释放综合征（sCRS）严重影响了患者的生命安全。
探讨 B 细胞血液系统恶性肿瘤患儿 sCRS 的影响因素，并构建风险预测模型。
本研究纳入 115 例接受 CD19 和 CD22 靶向嵌合抗原受体 T 细胞治疗的 B 细胞血液系统恶性肿瘤患儿。基于症状性不良事件和高可及的临床变量建立了列线图模型。通过受试者工作特征曲线下面积评估模型的区分度。通过校准曲线和 Hosmer-Lemeshow 检验评估模型的校准度。采用自抽样法进行内部验证。
37%的患儿发生了 sCRS。纳入列线图的指标包括治疗前的肿瘤负荷、预处理前的血小板减少症以及全身肌肉无力和头痛评分的平均值。结果显示，受试者工作特征曲线下面积为 0.841，校准曲线表明，列线图预测 sCRS 的概率与 sCRS 的实际概率吻合良好。Hosmer-Lemeshow 检验表明模型拟合数据良好（ χ2=5.759，P=0.674）。内部验证得到的一致性指数（C-index）为 0.841（0.770，0.912）。
构建的列线图模型具有良好的区分度和校准度，为识别 sCRS 提供了更方便、直观的评估工具。
将患者报告的结局纳入风险预测模型可实现 sCRS 的早期识别。
