| 引用本文: | 芦小燕,董昭兴,陈维,盛洁,徐晓红.基于Openvigil药物警戒数据分析网站的间质性肺疾病相关药物风险信号研究[J].中国现代应用药学,2023,40(11):1536-1541. |
| LU Xiaoyan,DONG Zhaoxing,CHEN Wei,SHENG Jie,XU Xiaohong.Study of Drug Risks Signals Associated with Interstitial Lung Disease Based on the Openvigil Pharmacovigilance Analysis Website[J].Chin J Mod Appl Pharm(中国现代应用药学),2023,40(11):1536-1541. |
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| 基于Openvigil药物警戒数据分析网站的间质性肺疾病相关药物风险信号研究 |
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芦小燕1, 董昭兴1, 陈维2, 盛洁1, 徐晓红1
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1.宁波市第二医院, 浙江 宁波 315010;2.浙江药科职业大学, 浙江 宁波 315100
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| 摘要: |
| 目的 通过Openvigil药物警戒数据分析网站检测和评价引起间质性肺疾病(interstitial lung disease，ILD)的药物不良事件的信号，以期为临床安全用药提供参考。方法 在Openvigil药物警戒数据分析网站使用OpenVigil 2.1-MedDRA-v24(data 2004Q1-2022Q3)在线分析系统，对美国FDA不良事件报告系统2004年第1季度—2022第3季度的数据进行分析：提取PT为“interstitial lung disease”的数据，查询RxNav网站进行商品名和药品通用名查询，合并相同通用名药物，重新计算合并后药品不良事件的相关数据及比例报告比(proportional reporting ratios，PRR)值、Chi_squared值，筛选DE≥30，PRR>2，Chi_squared> 4的条目，药品比对ATC码进行分类，形成旭日图，对比SIDER、药品说明书、国内外文献等分析药物不良事件信号真伪。结果 设定时段内，美国FDA不良事件报告系统来源首要引起ILD不良事件的药品共计报告113 854例，前5位引发ILD信号最强的系统依次是抗肿瘤及免疫调节剂(L)、消化系统及影响代谢药物(A)、全身用抗感染药(J)、心血管系统药物(C)、肌肉-骨骼肌系统药(M)，结合药品说明书、国际不良反应网站及国内外文献报道，吉非替尼、比卡鲁胺、紫杉醇、阿法替尼、奥沙利铂、多西他赛、依维莫司、决奈达隆、他克莫司、伊马替尼、徳喜曲妥珠单抗、复方磺胺甲恶唑、柳氮磺吡啶、地诺单抗、替吉奥、瑞巴派特为最可能引发ILD不良反应的药品。结论 通过Openvigil药物警戒数据分析网站进行药物不良事件信号挖掘，通过严谨医学评价得出最有可能引发ILD不良反应的16个药品，可为临床做好预警工作，早期发现ILD并及时停药，积极予以对症治疗，以降低药源性不良反应的危害。 |
| 关键词: 间质性肺疾病 药物风险 药物不良事件信号 数据挖掘 |
| DOI：10.13748/j.cnki.issn1007-7693.20230425 |
| 分类号:R969.3 |
| 基金项目:国家自然科学基金项目(82170074)；浙江省医学会临床科研基金项目(第二批)(2020ZYC-B30)；中国科学院大学宁波华美医院“华美研究基金”(2021HMKY15)；宁波市呼吸系统疾病临床医学研究中心(甬科社〔2022〕80号) |
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| Study of Drug Risks Signals Associated with Interstitial Lung Disease Based on the Openvigil Pharmacovigilance Analysis Website |
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LU Xiaoyan1, DONG Zhaoxing1, CHEN Wei2, SHENG Jie1, XU Xiaohong1
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1.Ningbo No.2 Hospital, Ningbo 315010, China;2.Zhejiang Pharmaceutical College, Ningbo 315100, China
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| Abstract: |
| OBJECTIVE To detect and evaluate adverse drug event(ADE) associated with interstitial lung disease(ILD) using the Openvigil pharmacovigilance data analysis website data, so as to provide references for safe clinical drug use. METHODS The Openvigil 2.1-MedDRA-v24 (data 2004Q1-2022Q3) online analysis system was used to analyze data from the US FDA Adverse Event Reporting System from the first quarter of 2004 to the third quarter of 2022. Data with preferred terms(PT) as “interstitial lung disease” were extracted, and the RxNav website was queried for brand name and generic name searches. Duplicate generic drug names were merged, and the values of DE, De, dE, de, PRR, and Chi_squared were recalculated for the merged drugs. Entries with DE≥30, PRR＞2, and Chi_squared＞4 were selected. Drug names were matched with ATC codes for classification and presented in a sunburst chart. The authenticity of the ADE signals was analyzed by comparing with SIDER, drug package inserts, and foreign and domestic literature.RESULTS During the specified time period, a total of 113 854 reports of drugs causing ILD were reported in the US FDA Adverse Event Reporting System. The top five systems that caused the strongest ILD signals were antineoplastic and immunomodulating agents(L), drugs affecting the gastrointestinal system and metabolism(A), systemic anti-infective drugs(J), cardiovascular system drugs(C), and drugs affecting the musculoskeletal system(M). Based on drug package inserts, the international adverse reaction database, and domestic and foreign literature reports, gefitinib, biclutamide, paclitaxel, alphatinib, oxaliplatin, docetaxel, everolimus, trastuzumab emtansine, tacrolimus, imatinib, denosumab, compound sulfamethoxazole, dapsone, denosumab, tegafur/gimeracil/oteracil, and ribavirin were identified as the most likely drugs to cause ILD adverse reactions. CONCLUSION ADE signals are explored using the Openvigil drug surveillance data analysis website, and through rigorous medical evaluation, 16 drugs are identified as the most likely to cause ILD adverse reactions. This can provide early warning for clinicians to promptly detect and discontinue drugs, and actively provide targeted treatment, in order to reduce the harm of drug-induced adverse reactions. |
| Key words: interstitial lung disease drug risk adverse drug event signals data mining |
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