Digital monitoring of medication safety in children: an investigation of ADR signalling techniques in Malaysia.

IF 3.3 3区 医学 Q2 MEDICAL INFORMATICS BMC Medical Informatics and Decision Making Pub Date : 2024-12-18 DOI:10.1186/s12911-024-02801-y
Beldona Hema Rekha, Shairyzah Ahmad Hisham, Izyan A Wahab, Norleen Mohamed Ali, Khang Wen Goh, Long Chiau Ming
{"title":"Digital monitoring of medication safety in children: an investigation of ADR signalling techniques in Malaysia.","authors":"Beldona Hema Rekha, Shairyzah Ahmad Hisham, Izyan A Wahab, Norleen Mohamed Ali, Khang Wen Goh, Long Chiau Ming","doi":"10.1186/s12911-024-02801-y","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Digital solutions can help monitor medication safety in children who are often excluded in clinical trials. The lack of reliable safety data often leads to either under- or over-dose of medications during clinical management which make them either not responding well to treatment or susceptible to adverse drug reactions (ADRs).</p><p><strong>Aim: </strong>This study investigated ADR signalling techniques to detect serious ADRs in Malaysian children aged from birth to 12 years old using an electronic ADRs' database.</p><p><strong>Methods: </strong>Four techniques (Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN) and Multi-item Gamma Poisson Shrinker (MGPS)) were tested on ADR reports submitted to the National Pharmaceutical Regulatory Agency between 2016 and 2020. Sensitivity, Specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of the techniques were compared.</p><p><strong>Results: </strong>A total of 31 medicine-Important Medical Event pairs were found and examined among the 3152 paediatric ADR reports. Three techniques (PRR, ROR, MGPS) signalled oculogyric crisis and dystonia for metoclopramide. BCPNN and MGPS signalled angioedema for paracetamol, amoxicillin and ibuprofen. Similar performances were found for PRR, ROR and BCPNN (sensitivity of 12%, specificity of 100%, PPV of 100% and NPV of 21%). MGPS revealed the highest sensitivity (20%) and NPV (23%), as well as similar specificity and PPV (100%).</p><p><strong>Conclusions: </strong>This study suggests that medication safety signalling techniques could be applied on electronic health records to monitor medication safety issues in children. Clinicians and medication safety specialist could prioritise the signals for further clinical consideration and prompt response.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"395"},"PeriodicalIF":3.3000,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11657009/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Informatics and Decision Making","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12911-024-02801-y","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Digital solutions can help monitor medication safety in children who are often excluded in clinical trials. The lack of reliable safety data often leads to either under- or over-dose of medications during clinical management which make them either not responding well to treatment or susceptible to adverse drug reactions (ADRs).

Aim: This study investigated ADR signalling techniques to detect serious ADRs in Malaysian children aged from birth to 12 years old using an electronic ADRs' database.

Methods: Four techniques (Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN) and Multi-item Gamma Poisson Shrinker (MGPS)) were tested on ADR reports submitted to the National Pharmaceutical Regulatory Agency between 2016 and 2020. Sensitivity, Specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of the techniques were compared.

Results: A total of 31 medicine-Important Medical Event pairs were found and examined among the 3152 paediatric ADR reports. Three techniques (PRR, ROR, MGPS) signalled oculogyric crisis and dystonia for metoclopramide. BCPNN and MGPS signalled angioedema for paracetamol, amoxicillin and ibuprofen. Similar performances were found for PRR, ROR and BCPNN (sensitivity of 12%, specificity of 100%, PPV of 100% and NPV of 21%). MGPS revealed the highest sensitivity (20%) and NPV (23%), as well as similar specificity and PPV (100%).

Conclusions: This study suggests that medication safety signalling techniques could be applied on electronic health records to monitor medication safety issues in children. Clinicians and medication safety specialist could prioritise the signals for further clinical consideration and prompt response.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
儿童用药安全的数字监测:对马来西亚不良反应信号技术的调查。
背景:数字化解决方案可以帮助监测经常被排除在临床试验之外的儿童的用药安全性。缺乏可靠的安全性数据往往导致临床管理期间药物剂量不足或过量,使其对治疗反应不佳或容易发生药物不良反应(adr)。目的:本研究利用电子ADR数据库研究了ADR信号技术,以检测马来西亚出生至12岁儿童的严重ADR。方法:采用比例报告比(PRR)、报告优势比(ROR)、贝叶斯置信传播神经网络(BCPNN)和多项目伽玛泊松收缩器(MGPS) 4种技术对2016 - 2020年国家药品监督管理局上报的药品不良反应报告进行检验。比较两种技术的敏感性、特异性、阳性预测值(PPV)和阴性预测值(NPV)。结果:在3152份儿科不良反应报告中,共发现并检查了31对医学-重要医学事件对。三种技术(PRR、ROR、MGPS)提示甲氧氯普胺的眼科危象和肌张力障碍。BCPNN和MGPS提示扑热息痛、阿莫西林和布洛芬组血管性水肿。PRR、ROR和BCPNN的敏感性为12%,特异性为100%,PPV为100%,NPV为21%。MGPS显示最高的敏感性(20%)和NPV(23%),以及相似的特异性和PPV(100%)。结论:本研究表明,药物安全信号技术可以应用于电子健康记录,以监测儿童的药物安全问题。临床医生和药物安全专家可以优先考虑这些信号,以便进一步的临床考虑和迅速反应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
期刊最新文献
Machine learning based model for the early detection of Gestational Diabetes Mellitus. Parallel privacy preservation through partitioning (P4): a scalable data anonymization algorithm for health data. Pseudonymization tools for medical research: a systematic review. Risk-based evaluation of machine learning-based classification methods used for medical devices. Can some algorithms of machine learning identify osteoporosis patients after training and testing some clinical information about patients?
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1