PO.CL05.02 · 临床研究
CAR-T治疗后血液学毒性的预测模型：现有风险评分及未满足的AI需求的系统评价
Predictive models for hematologic toxicity after CAR-T therapy: A systematic review of current risk scores and unmet AI needs
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：血液学毒性，包括早期及迁延性中性粒细胞减少、贫血和血小板减少，是CD19、CD20、CD22和BCMA靶向CAR-T治疗后最常见且具有临床意义的并发症之一。已开发出若干临床评分——最著名的是CAR-HEMATOTOX（CAR-HT）以及经改良的ALL-HT——用于预测输注后血细胞减少，但其在不同疾病间的对比性能尚未得到系统评估。此前尚无综述对现有模型进行汇总或评估基于AI的预测方法的需求。
方法：我们对开发或验证CAR-T治疗后血液学恶性肿瘤患者血液学毒性预测模型或风险评分的研究进行了系统评价。符合条件的研究包括临床型、基于细胞因子型或复合型预测因子，并报告了曲线下面积（AUC）等区分度指标。提取的变量包括CAR-T产品、靶抗原、疾病类型、所用预测因子、模型类型、验证策略以及性能统计数据。由于模型结构和结局定义存在异质性，结果以定性方式进行综合。
结果：关于B细胞淋巴瘤和多发性骨髓瘤中CAR-HT评分的研究报告，预测CD19和BCMA靶向CAR-T治疗后严重或长期中性粒细胞减少的AUC值分别为0.89和0.82。关于ALL-HT评分（CAR-HT的一种改良/优化版本，以骨髓负荷替代铁蛋白以提高在B-ALL中的区分度）的研究报告，预测CD19/CD22 CAR-T治疗后迁延性中性粒细胞减少和较差总生存（OS）的AUC为0.84-0.90。一项纳入侵袭性/惰性NHL、MM/PCL和ALL患者、接受CD19、CD20或BCMA CAR-T治疗的多中心研究报告，预测ICA-HT的AUC为0.87（eIPM-Pre）和0.88（eIPM-Post）。一项在B-ALL和LBCL中开展的CD19 CAR-T研究报告，采用TNF-和CRP的早期血液毒性列线图的AUC为0.85。
结论：现有的CAR-T治疗后血液学毒性预测工具展现出较强但具疾病特异性的性能。然而，不一致的终点和异质性的预测因子限制了其临床普适性。目前尚无模型纳入当代AI或机器学习方法，凸显出一项重大的未满足需求。先进的预测框架或可更早识别存在严重或迁延性血细胞减少风险的患者，并指导更加个体化的支持治疗，有望减少下游并发症。
查看英文原文 English abstract
Background: Hematologic toxicity, including early and prolonged neutropenia, anemia, and thrombocytopenia, is among the most common and clinically significant complications after CD19, CD20, CD22 and BCMA-directed CAR-T therapy. Several clinical scoresmost notably CAR-HEMATOTOX (CAR-HT) and the refined ALL-HThave been developed to predict post-infusion cytopenias, but their comparative performance across diseases has not been systematically evaluated. No prior review has summarized existing models or assessed the need for AI-based predictive approaches.
Methods: We conducted a systematic review of studies that developed or validated predictive models or risk scores for hematologic toxicity after CAR-T therapy in hematologic malignancies. Eligible studies included clinical, cytokine-based, or composite predictors and reported discrimination metrics such as area under the curve (AUC). Extracted variables included CAR-T product, target antigen, disease type, predictors used, model type, validation strategy, and performance statistics. Results were synthesized qualitatively due to heterogeneity in model structure and outcome definitions.
Results: Studies on the CAR-HT score in B-cell lymphomas and multiple myelomas reported AUC values of 0.89 and 0.82, respectively, for predicting severe or long-term neutropenia after CD19 and BCMA-directed CAR-T therapy. Studies on the ALL-HT score (a modified/refined version of CAR-HT replacing ferritin with bone-marrow burden to improve discrimination in B-ALL) reported AUC 0.84-0.90 for predicting prolonged neutropenia and worse overall survivability (OS) after CD19/CD22 CAR-T. A multicenter study including aggressive/indolent NHL, MM/PCL, and ALL patients treated with CD19, CD20, or BCMA CAR-T reported AUC 0.87 (eIPM-Pre) and 0.88 (eIPM-Post) for predicting ICA-HT. A CD19 CAR-T study in B-ALL and LBCL reported AUC 0.85 for an early hematotoxicity nomogram using TNF- and CRP.
Conclusion: Existing prediction tools for hematologic toxicity after CAR-T therapy demonstrate strong but disease-specific performance. Yet inconsistent endpoints and heterogeneous predictors limit clinical generalizability. No current model incorporates contemporary AI or machine-learning methods, underscoring a significant unmet need. Advanced predictive frameworks may allow earlier identification of patients at risk for severe or prolonged cytopenias and guide more individualized supportive care, with the potential to reduce downstream complications.
利益披露 Disclosure
M. Gupta, None..
A. Vemulapalli, None..
S. Cherukuri, None..
R. Nimmagadda, None.