van der Vaart Marianne, Duff Eugene, Raafat Nader, Rogers Richard, Hartley Caroline, Slater Rebeccah
Department of Paediatrics University of Oxford Oxford UK.
Nuffield Department of Anaesthesia John Radcliffe Hospital Oxford UK.
Paediatr Neonatal Pain. 2019 Sep 9;1(1):21-30. doi: 10.1002/pne2.12007. eCollection 2019 Sep.
Infants in neonatal intensive care units frequently experience clinically necessary painful procedures, which elicit a range of behavioral, physiological, and neurophysiological responses. However, the measurement of pain in this population is a challenge and no gold standard exists. The aim of this study was to investigate how noxious-evoked changes in facial expression, reflex withdrawal, brain activity, heart rate, and oxygen saturation are related and to examine their accuracy in discriminating between noxious and non-noxious stimuli. In 109 infants who received a clinically required heel lance and a control non-noxious stimulus, we investigated whether combining responses across each modality, or including multiple measures from within each modality improves our ability to discriminate the noxious and non-noxious stimuli. A random forest algorithm was used to build data-driven models to discriminate between the noxious and non-noxious stimuli in a training set which were then validated in a test set of independent infants. Measures within each modality were highly correlated, while different modalities showed less association. The model combining information across all modalities had good discriminative ability (accuracy of 0.81 in identifying noxious and non-noxious stimuli), which was higher than the discriminative power of the models built from individual modalities. This demonstrates the importance of including multiple modalities in the assessment of infant pain.
新生儿重症监护病房的婴儿经常经历临床上必要的疼痛性操作，这会引发一系列行为、生理和神经生理反应。然而，对这一群体的疼痛进行测量是一项挑战，且不存在金标准。本研究的目的是调查有害刺激诱发的面部表情、反射性退缩、脑活动、心率和血氧饱和度变化之间的关系，并检验它们在区分有害和无害刺激方面的准确性。在109名接受了临床上所需足跟采血和对照无害刺激的婴儿中，我们研究了整合各模态的反应，或纳入各模态内的多项测量指标是否能提高我们区分有害和无害刺激的能力。使用随机森林算法构建数据驱动模型，以区分训练集中的有害和无害刺激，然后在独立婴儿的测试集中进行验证。各模态内的测量指标高度相关，而不同模态之间的关联较小。整合所有模态信息的模型具有良好的判别能力（识别有害和无害刺激的准确率为0.81），高于基于单个模态构建的模型的判别能力。这证明了在评估婴儿疼痛时纳入多种模态的重要性。
