关键词: Cardiotoxicity Chimeric antigen receptor T-cell Machine learning algorithm Pharmacovigilance

Mesh : Humans Pharmacovigilance Machine Learning Female Male Middle Aged Cardiovascular Diseases Aged Adult Immunotherapy, Adoptive / adverse effects methods Adolescent Young Adult Child Receptors, Antigen, T-Cell Hematologic Neoplasms / drug therapy Child, Preschool

来  源:   DOI:10.1038/s41598-024-64466-x   PDF(Pubmed)

Abstract:
Chimeric antigen receptor T-cell (CAR-T) therapies are a paradigm-shifting therapeutic in patients with hematological malignancies. However, some concerns remain that they may cause serious cardiovascular adverse events (AEs), for which data are scarce. In this study, gradient boosting machine algorithm-based model was fitted to identify safety signals of serious cardiovascular AEs reported for tisagenlecleucel in the World Health Organization Vigibase up until February 2024. Input dataset, comprised of positive and negative controls of tisagenlecleucel based on its labeling information and literature search, was used to train the model. Then, we implemented the model to calculate the predicted probability of serious cardiovascular AEs defined by preferred terms included in the important medical event list from European Medicine Agency. There were 467 distinct AEs from 3,280 safety cases reports for tisagenlecleucel, of which 363 (77.7%) were classified as positive controls, 66 (14.2%) as negative controls, and 37 (7.9%) as unknown AEs. The prediction model had area under the receiver operating characteristic curve of 0.76 in the test dataset application. Of the unknown AEs, six cardiovascular AEs were predicted as the safety signals: bradycardia (predicted probability 0.99), pleural effusion (0.98), pulseless electrical activity (0.89), cardiotoxicity (0.83), cardio-respiratory arrest (0.69), and acute myocardial infarction (0.58). Our findings underscore vigilant monitoring of acute cardiotoxicities with tisagenlecleucel therapy.
摘要:
嵌合抗原受体T细胞(CAR-T)疗法是血液恶性肿瘤患者的范式转变疗法。然而,一些担忧仍然存在,他们可能会导致严重的心血管不良事件(AE),数据稀缺。在这项研究中,对基于梯度提升机算法的模型进行拟合,以识别截至2024年2月世界卫生组织Vigibase报告的tisagenlecleucel严重心血管不良事件的安全信号.输入数据集,由tisagenlecleucel的阳性和阴性对照组成,基于其标记信息和文献检索,是用来训练模型的.然后,我们实施了该模型,以计算由欧洲医学机构重要医学事件列表中的首选术语定义的严重心血管不良事件的预测概率.从3,280例tisagenlecleucel安全病例报告中,有467例不同的AE,其中363人(77.7%)被列为阳性对照,66(14.2%)作为阴性对照,37例(7.9%)为未知不良事件。在测试数据集应用中,预测模型的接受者工作特征曲线下面积为0.76。在未知的AE中,六个心血管不良事件被预测为安全信号:心动过缓(预测概率0.99),胸腔积液(0.98),无脉电活动(0.89),心脏毒性(0.83),心跳呼吸停止(0.69),和急性心肌梗死(0.58)。我们的发现强调了tisagenlecleucel治疗对急性心脏毒性的警惕监测。
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