Department of Clinical Pharmacy, Faculty of Pharmacy, Hacettepe University, Ankara, Türkiye.
Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA.
Expert Rev Clin Pharmacol. 2024 Sep;17(9):803-816. doi: 10.1080/17512433.2024.2390927. Epub 2024 Aug 13.
Assessment of drug-related adverse events is essential to fully understand the benefit-risk balance of any drug exposure, weighing efficacy versus safety. This is needed for both drug labeling and clinical decision-making. Assessment is based on seriousness, severity and causality, be it more difficult to apply in neonates. Adverse event detection or prevention in the neonatal clinical setting is also more complicated because of polypharmacy, and off-label or unlicensed pharmacotherapy.
Tools became available to assess severity and causality of adverse events in neonates recruited in clinical trials. The first version of the Neonatal Adverse Event severity score (NAESS) reduced the inter-observer variability. Causality tools like the Naranjo score were also tailored to neonates. These tools are also instrumental to support proactive pharmacovigilance in clinical care, while multidisciplinary care teams and computerized pharmacovigilance using advanced data analysis, like machine learning are emerging approaches to develop effective decision strategies.
All stakeholders involved in development of medicines or its clinical use should be aware of the limitations of the currently available assessment tools. Extension and optimization of these tools, advanced data analysis approaches, and capturing the variability in time-dependent physiology are warranted to improve pharmacovigilance in neonates.
评估药物相关不良事件对于全面了解任何药物暴露的获益-风险平衡至关重要,需要权衡疗效与安全性。这对于药物标签和临床决策制定都是必要的。评估基于严重程度、严重程度和因果关系,而在新生儿中应用则更为困难。由于新生儿临床环境中存在多种药物治疗、超说明书或无标签的药物治疗,因此不良事件的检测或预防也更加复杂。
已开发出工具来评估临床试验中招募的新生儿的不良事件的严重程度和因果关系。新生儿不良事件严重程度评分(NAESS)的第一个版本降低了观察者间的变异性。针对新生儿的因果关系工具,如 Naranjo 评分也应运而生。这些工具也有助于在临床护理中支持主动药物警戒,而多学科护理团队和使用先进数据分析(如机器学习)的计算机化药物警戒则是新兴的方法,旨在制定有效的决策策略。
所有参与药品开发或临床使用的利益相关者都应意识到目前可用评估工具的局限性。需要扩展和优化这些工具、先进的数据分析方法,并捕捉随时间变化的生理学的变异性,以改善新生儿的药物警戒。