洛拉替尼的药物不良事件信号挖掘与分析
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篇名: | 洛拉替尼的药物不良事件信号挖掘与分析 |
TITLE: | Mining and analysis of lorlatinib-induced adverse drug event signals |
摘要: | 目的 为临床安全使用洛拉替尼提供参考。方法采用报告比值比(ROR)法、英国药品和保健品管理局综合标准法(简称“MHRA法”)和贝叶斯可信区间递进神经网络(BCPNN)法,对美国FDA不良事件报告系统(FAERS)中2019年第1季度至2022年第4季度关于洛拉替尼的数据进行药物不良事件(ADEs)信号检测。结果与结论3种方法检出重叠的关于洛拉替尼的ADEs信号114个,其中说明书未提及的新的可疑ADEs信号73个。临床在使用洛拉替尼时,应特别关注发生例数和信号数均较多的ADEs,如各类神经系统疾病,精神病类疾病,呼吸系统、胸及纵隔疾病等,临床表现包括脑水肿、大脑梗死、肺动脉高压、缄默症、性欲降低、胸腔积液等。说明书中未提及的移动性血栓性静脉炎、放射性坏死、缄默症、膀胱输尿管反流在特异度高的BCPNN法检测中信号强度均为较强,也应引起临床重视。 |
ABSTRACT: | OBJECTIVE To provide references for the safe use of lorlatinib in clinical practice. METHODS The reporting odds ratio (ROR) method, Medicines and Healthcare Products Regulatory Agency comprehensive standard method (referred to as “MHRA method”) and the Bayesian confidence propagation neural network (BCPNN) method were used to detect adverse drug events (ADEs) signals of lorlatinib in the FDA Adverse Event Reporting System from the first quarter of 2019 to the fourth quarter of 2022. RESULTS & CONCLUSIONS Totally 114 overlapping ADEs signals of lorlatinib were detected by the three methods, among which there were 73 new suspicious ADEs signals which were not covered in the instruction of lorlatinib. When using loratinib in clinical practice, special attention should be paid to ADEs with a high number of cases and signals, such as various neurological diseases, psychiatric diseases, respiratory system, thoracic and mediastinal diseases; clinical manifestations included cerebral edema, cerebral infarction, pulmonary hypertension, mutism, decreased sexual desire, pleural effusion. The signals of mobile thrombophlebitis, radiation necrosis, mutism, vesicoureteral reflux not mentioned in the instructions were all strong in BCPNN detection with high specificity, to which we should pay attention in clinical application. |
期刊: | 2023年第34卷第20期 |
作者: | 龙霞;黄孟文;蒲诗云;王利辰;唐梦蛟;周后凤 |
AUTHORS: | LONG Xia,HUANG Mengwen,PU Shiyun,WANG Lichen,TANG Mengjiao,ZHOU Houfeng |
关键字: | 洛拉替尼;药物不良事件;信号挖掘 |
KEYWORDS: | lorlatinib; adverse drug event; signal mining |
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