Abstract：
BACKGROUND: Nusinersen is the first drug for precise targeted therapy of spinal muscular atrophy, a rare disease that occurs in one of 10,000 to 20,000 live births. Therefore, thorough and comprehensive reports on the safety of nusinersen in large, real-world populations are necessary. This study aimed to mine the adverse event (AE) signals related to nusinersen through the Food and Drug Administration Adverse Event Reporting System (FAERS) database.
METHODS: We extracted reports of AEs with nusinersen as the primary suspect from FAERS between December 2016 and March 2023. Reporting odds ratio (ROR) and Bayesian confidence propagation neural network (BCPNN) were used for AE signal detection.
RESULTS: We extracted a total of 4807 suspected AE cases with nusinersen as the primary suspect from the FAERS database. Among them, 106 positive signals were obtained using the ROR and BCPNN. The highest frequency reported systemic organ class was general disorders and administration site conditions. Common clinical AEs of nusinersen were detected in the FAERS database, such as pneumonia, vomiting, back pain, headache, pyrexia, and post-lumbar puncture syndrome. In addition, we identified potential unexpected serious AEs through disproportionality analysis, including sepsis, seizure, epilepsy, brain injury, cardiorespiratory arrest, and cardiac arrest.
CONCLUSIONS: Analyzing large amounts of real-world data from the FAERS database, we identified potential new AEs of nusinersen by disproportionate analysis. It is advantageous for health care professionals and pharmacists to concentrate on effectively managing high-risk AEs of nusinersen, improve medication levels in clinical settings, and uphold patient medication safety.
METHODS: We extracted reports of AEs with nusinersen as the primary suspect from FAERS between December 2016 and March 2023. Reporting odds ratio (ROR) and Bayesian confidence propagation neural network (BCPNN) were used for AE signal detection.
RESULTS: We extracted a total of 4807 suspected AE cases with nusinersen as the primary suspect from the FAERS database. Among them, 106 positive signals were obtained using the ROR and BCPNN. The highest frequency reported systemic organ class was general disorders and administration site conditions. Common clinical AEs of nusinersen were detected in the FAERS database, such as pneumonia, vomiting, back pain, headache, pyrexia, and post-lumbar puncture syndrome. In addition, we identified potential unexpected serious AEs through disproportionality analysis, including sepsis, seizure, epilepsy, brain injury, cardiorespiratory arrest, and cardiac arrest.
CONCLUSIONS: Analyzing large amounts of real-world data from the FAERS database, we identified potential new AEs of nusinersen by disproportionate analysis. It is advantageous for health care professionals and pharmacists to concentrate on effectively managing high-risk AEs of nusinersen, improve medication levels in clinical settings, and uphold patient medication safety.
摘要：
背景：Nusinersen是第一种精确靶向治疗脊髓性肌萎缩的药物，一种罕见的疾病，发生在10,000至20,000个活产婴儿中。因此,关于nusinersen的安全性的全面和全面的报告，现实世界的人口是必要的。本研究旨在通过食品和药物管理局不良事件报告系统（FAERS）数据库挖掘与nusinersen相关的不良事件（AE）信号。
方法：我们在2016年12月至2023年3月期间从FAERS提取了以nusinersen为主要嫌疑人的不良事件报告。报告比值比（ROR）和贝叶斯置信度传播神经网络（BCPNN）用于AE信号检测。
结果：我们从FAERS数据库中提取了4807例疑似AE病例，其中nusinersen为主要嫌疑人。其中,使用ROR和BCPNN获得106个阳性信号。报告的全身器官类别频率最高的是一般疾病和给药部位状况。在FAERS数据库中检测到nusinersen的常见临床AE，比如肺炎,呕吐,背痛,头痛,发热,和腰椎穿刺后综合征.此外,我们通过不成比例分析确定了潜在的意外严重不良事件，包括败血症,癫痫发作,癫痫,脑损伤,心肺骤停,还有心脏骤停.
结论：分析来自FAERS数据库的大量实际数据，我们通过不成比例分析确定了nusinersen的潜在新AE。对于卫生保健专业人员和药剂师来说，专注于有效管理nusinersen的高风险AE是有利的，提高临床环境中的药物水平，维护患者用药安全。
方法：我们在2016年12月至2023年3月期间从FAERS提取了以nusinersen为主要嫌疑人的不良事件报告。报告比值比（ROR）和贝叶斯置信度传播神经网络（BCPNN）用于AE信号检测。
结果：我们从FAERS数据库中提取了4807例疑似AE病例，其中nusinersen为主要嫌疑人。其中,使用ROR和BCPNN获得106个阳性信号。报告的全身器官类别频率最高的是一般疾病和给药部位状况。在FAERS数据库中检测到nusinersen的常见临床AE，比如肺炎,呕吐,背痛,头痛,发热,和腰椎穿刺后综合征.此外,我们通过不成比例分析确定了潜在的意外严重不良事件，包括败血症,癫痫发作,癫痫,脑损伤,心肺骤停,还有心脏骤停.
结论：分析来自FAERS数据库的大量实际数据，我们通过不成比例分析确定了nusinersen的潜在新AE。对于卫生保健专业人员和药剂师来说，专注于有效管理nusinersen的高风险AE是有利的，提高临床环境中的药物水平，维护患者用药安全。
