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使用FAERS数据库对药物性勃起功能障碍进行不成比例分析。

Disproportionality analysis of drug-induced erectile dysfunction using FAERS database.

作者信息

Tian Xiaona, Luo Dongqiang, Zeng Wenling, Zhou Xinyu, Chen Yishen, Dai DanDan, Fang Caishan, Xiao Jianjun

机构信息

Department of Medical Oncology, Zhongshan City People's Hospital, Zhongshan, China.

Guangzhou University of Chinese Medicine, Guangzhou, China.

出版信息

Sci Rep. 2025 May 6;15(1):15760. doi: 10.1038/s41598-025-00231-y.

Abstract

This study employs a comprehensive approach to systematically identify drugs associated with Drug-Induced Erectile Dysfunction (DIED) risk and constructs a DIED risk assessment platform. Utilizing the FAERS database, we identified "Erectile Dysfunction," "Organic Erectile Dysfunction," and "Psychogenic Erectile Dysfunction" as relevant Preferred Terms (PTs) for DIED. After excluding patients diagnosed with Erectile Dysfunction (ED), drugs suspected as primary suspects (PS) in ≥ 10 DIP events were selected as target drugs. Through disproportionality analysis, we identified positive signals for these drugs using ROR, PRR, BCPNN, and EBGM. We further assessed the independent effects of positive drugs by adjusting for confounding factors such as age using multivariate logistic regression. Subsequently, we obtained the median onset time and outcome events of DIED for target drugs and compared them by age. The DIED platform is accessible for free at http://116.196.73.86:3838/ADR-DATABASE/DIED/. A total of 67 target drugs were identified as PS in DIED events with 10 or more cases. Based on disproportionality analysis, we further identified 28 drugs with DIED risk signals. Multivariate logistic regression revealed that 23 of these drugs were independent risk factors for DIED (OR > 1 and P < 0.05). Analysis of outcome events showed a significant difference in the median onset time of DIED between different age groups. This study identified 28 drugs associated with DIED risk. We also found some previously unreported DIP risk drugs, including omeprazole, antihypertensive drugs, etc., which should be of clinical concern.

摘要

本研究采用综合方法系统识别与药物性勃起功能障碍(DIED)风险相关的药物,并构建了DIED风险评估平台。利用FAERS数据库,我们将“勃起功能障碍”“器质性勃起功能障碍”和“心因性勃起功能障碍”确定为DIED的相关首选术语(PT)。在排除诊断为勃起功能障碍(ED)的患者后,选择在≥10例药物性问题(DIP)事件中被怀疑为主要嫌疑药物(PS)的药物作为目标药物。通过不成比例分析,我们使用风险比(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)识别这些药物的阳性信号。我们通过多因素逻辑回归调整年龄等混杂因素,进一步评估阳性药物的独立效应。随后,我们获得了目标药物DIED的中位发病时间和结局事件,并按年龄进行比较。DIED平台可在http://116.196.73.86:3838/ADR-DATABASE/DIED/免费访问。在10例及以上病例的DIED事件中,共识别出67种目标药物为PS。基于不成比例分析,我们进一步识别出28种具有DIED风险信号的药物。多因素逻辑回归显示,其中23种药物是DIED的独立危险因素(比值比>1且P<0.05)。结局事件分析显示,不同年龄组DIED的中位发病时间存在显著差异。本研究识别出28种与DIED风险相关的药物。我们还发现了一些以前未报告的DIP风险药物,包括奥美拉唑、抗高血压药物等,这些应引起临床关注。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d1f/12056008/c9aae138e3e8/41598_2025_231_Fig1_HTML.jpg

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