Hartley Caroline
Department of Paediatrics University of Oxford Oxford UK.
Paediatr Neonatal Pain. 2021 Nov 22;3(4):147-155. doi: 10.1002/pne2.12065. eCollection 2021 Dec.
Vital signs, such as heart rate and oxygen saturation, are continuously monitored for infants in neonatal care units. Pharmacological interventions can alter an infant's vital signs, either as an intended effect or as a side effect, and consequently could provide an approach to explore the wide variability in pharmacodynamics across infants and could be used to develop models to predict outcome (efficacy or adverse effects) in an individual infant. This will enable doses to be tailored according to the individual, shifting the balance toward efficacy and away from the adverse effects of a drug. Pharmacological analgesics are frequently not given in part due to the risk of adverse effects, yet this exposes infants to the short- and long-term effects of painful procedures. Personalized analgesic dosing will be an important step forward in providing safer effective pain relief in infants. The aim of this paper was to describe a framework to develop predictive models of drug outcome from analysis of vital signs data, focusing on analgesics as a representative example. This framework investigates changes in vital signs in response to the analgesic (prior to the painful procedure) and proposes using machine learning to examine if these changes are predictive of outcome-either efficacy (with pain response measured using a multimodal approach, as changes in vital signs alone have limited sensitivity and specificity) or adverse effects. The framework could be applied to both preterm and term infants in neonatal care units, as well as older children. Sharing vital signs data are proposed as a means to achieve this aim and bring personalized medicine rapidly to the forefront in neonatology.
在新生儿重症监护病房中，会持续监测婴儿的生命体征，如心率和血氧饱和度。药物干预可能会改变婴儿的生命体征，无论是作为预期效果还是副作用，因此可以提供一种方法来探索不同婴儿之间药效学的广泛差异，并可用于开发预测个体婴儿结局（疗效或不良反应）的模型。这将使剂量能够根据个体进行调整，将平衡转向疗效，远离药物的不良反应。由于存在不良反应的风险，药物镇痛剂常常不被使用，但这会使婴儿暴露于疼痛操作的短期和长期影响之下。个性化镇痛给药将是在为婴儿提供更安全有效的疼痛缓解方面向前迈出的重要一步。本文的目的是描述一个从生命体征数据分析中开发药物结局预测模型的框架，以镇痛药作为一个代表性例子进行重点阐述。该框架研究了在镇痛（在疼痛操作之前）过程中生命体征的变化，并建议使用机器学习来检验这些变化是否能预测结局——无论是疗效（使用多模式方法测量疼痛反应，因为仅生命体征的变化敏感性和特异性有限）还是不良反应。该框架可应用于新生儿重症监护病房中的早产儿和足月儿，以及年龄较大的儿童。建议共享生命体征数据作为实现这一目标的一种手段，并使个性化医疗在新生儿学中迅速处于前沿地位。
