Morphine is the opioid most commonly used for neonatal pain management. In intravenous form, it is administered as continuous infusions and intermittent injections, mostly based on empirically established protocols. Inadequate pain control in neonates can cause long-term adverse consequences; however, providing appropriate individualized morphine dosing is particularly challenging due to the interplay of rapid natural physiological changes and multiple life-sustaining procedures in patients who cannot describe their symptoms. At most institutions, morphine dosing in neonates is largely carried out as an iterative process using a wide range of starting doses and then titrating to effect based on clinical response and side effects using pain scores and levels of sedation. Our background data show that neonates exhibit large variability in morphine clearance resulting in a wide range of exposures, which are poorly predicted by dose alone. Here, we describe the development and implementation of an electronic health record-integrated, model-informed decision support platform for the precision dosing of morphine in the management of neonatal pain. The platform supports pharmacokinetic model-informed dosing guidance and has functionality to incorporate real-time drug concentration information. The feedback is inserted directly into prescribers' workflows so that they can make data-informed decisions. The expected outcomes are better clinical efficacy and safety with fewer side effects in the neonatal population.

中文翻译：

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用于新生儿吗啡精确剂量的电子健康记录嵌入式决策支持平台。


吗啡是最常用于新生儿疼痛治疗的阿片类药物。在静脉注射形式中，它以连续输注和间歇注射的方式给药，主要基于经验建立的方案。新生儿疼痛控制不足可能会导致长期不良后果；然而，由于快速的自然生理变化和无法描述症状的患者的多种维持生命程序的相互作用，提供适当的个体化吗啡剂量特别具有挑战性。在大多数机构，新生儿的吗啡剂量主要是一个迭代过程，使用多种起始剂量，然后根据临床反应和副作用，使用疼痛评分和镇静水平滴定至效果。我们的背景数据显示，新生儿的吗啡清除率表现出很大的变异性，从而导致广泛的暴露范围，仅通过剂量很难预测这一点。在这里，我们描述了电子健康记录集成、模型知情决策支持平台的开发和实施，用于在新生儿疼痛管理中精确剂量吗啡。该平台支持基于药代动力学模型的给药指导，并具有整合实时药物浓度信息的功能。反馈被直接插入处方者的工作流程中，以便他们能够做出基于数据的决策。预期结果是在新生儿群体中具有更好的临床疗效和安全性以及更少的副作用。