Liu Luyu, Wu Shaobo, Wei Liangliang, Xia Zhihao, Ji Jiajia, Huang Dageng
Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, 710054, Shaanxi, China.
Department of Orthopaedics, Lanzhou University Second Hospital, Lanzhou, 730030, Gansu, China.
Aging Clin Exp Res. 2025 Jan 14;37(1):23. doi: 10.1007/s40520-024-02921-5.
This study aims to analyze adverse drug events (ADE) related to romosozumab from the second quarter of 2019 to the third quarter of 2023 from FAERS database.
The ADE data related to romosozumab from 2019 Q2 to 2023 Q3 were collected. After data normalization, four signal strength quantification algorithms were used: ROR (Reporting Odds Ratios), PRR (Proportional Reporting Ratios), BCPNN (Bayesian Confidence Propagation Neural Network), and EBGM (Empirical Bayesian Geometric Mean).
Screening for romosozumab-related AEs (adverse events) included 23 system organ categories (SOCs). PT (preferred terms) levels were screened for adverse drug reaction (ADR) signals. A total of 7055 reports with romosozumab as the primary suspect (PS) and 14,041 PTs induced by romosozumab as PS were identified. Common significant signals of general disorders and administration site conditions, musculoskeletal and connective tissue disorders have emerged. Specifically, unexpected AEs such as gastrointestinal disorder, respiratory, thoracic and mediastinal disorders also occur. Notably, fracture (n = 503, ROR = 107.8, PRR = 103.83, IC = 6.6, EBGM = 97.02) and bone density abnormal (n = 429, ROR = 343.65, PRR = 332.77, IC = 8.08, EBGM = 271.34) exhibited relatively high occurrence rates and signal strengths.
Our study identifies potential new AE signals and provides broader data support for the safety of romosozumab. In clinical application, doctors are provided with a warning to closely monitor adverse reactions to support their rational use in diseases such as osteoporosis.
本研究旨在从美国食品药品监督管理局不良事件报告系统（FAERS）数据库分析2019年第二季度至2023年第三季度与罗莫索单抗相关的药物不良事件（ADE）。
收集2019年第二季度至2023年第三季度与罗莫索单抗相关的ADE数据。数据标准化后，使用四种信号强度量化算法：报告比值比（ROR）、比例报告比值比（PRR）、贝叶斯置信传播神经网络（BCPNN）和经验贝叶斯几何均值（EBGM）。
筛选出与罗莫索单抗相关的不良事件（AE）包括23个系统器官类别（SOC）。对首选术语（PT）水平进行不良药物反应（ADR）信号筛选。共识别出7055份以罗莫索单抗为主要怀疑对象（PS）的报告，以及14041个由罗莫索单抗作为PS引起的PT。出现了一般疾病和给药部位状况、肌肉骨骼和结缔组织疾病的常见显著信号。具体而言，还发生了如胃肠道疾病、呼吸、胸和纵隔疾病等意外AE。值得注意的是，骨折（n = 503，ROR = 107.8，PRR = 103.83，IC = 6.6，EBGM = 97.02）和骨密度异常（n = 429，ROR = 343.65，PRR = 332.77，IC = 8.08，EBGM = 271.34）表现出相对较高的发生率和信号强度。
我们的研究识别出潜在的新AE信号，并为罗莫索单抗的安全性提供更广泛的数据支持。在临床应用中，为医生提供警示，以密切监测不良反应，支持其在骨质疏松症等疾病中的合理使用。
