Gozzi Noemi, Preatoni Greta, Ciotti Federico, Hubli Michèle, Schweinhardt Petra, Curt Armin, Raspopovic Stanisa
Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland.
Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, 8008 Zürich, Switzerland.
Med. 2024 Dec 13;5(12):1495-1509.e5. doi: 10.1016/j.medj.2024.07.016. Epub 2024 Aug 7.
Pain is a complex subjective experience, strongly impacting health and quality of life. Despite many attempts to find effective solutions, present treatments are generic, often unsuccessful, and present significant side effects. Designing individualized therapies requires understanding of multidimensional pain experience, considering physical and emotional aspects. Current clinical pain assessments, relying on subjective one-dimensional numeric self-reports, fail to capture this complexity.
To this aim, we exploited machine learning to disentangle physiological and psychosocial components shaping the pain experience. Clinical, psychosocial, and physiological data were collected from 118 chronic pain and healthy participants undergoing 40 pain trials (4,697 trials).
To understand the objective response to nociception, we classified pain from the physiological signals (accuracy >0.87), extracting the most important biomarkers. Then, using multilevel mixed-effects models, we predicted the reported pain, quantifying the mismatch between subjective level and measured physiological response. From these models, we introduced two metrics: TIP (subjective index of pain) and Φ (physiological index). These represent possible added value in the clinical process, capturing psychosocial and physiological pain dimensions, respectively. Patients with high TIP are characterized by frequent sick leave from work and increased clinical depression and anxiety, factors associated with long-term disability and poor recovery, and are indicated for alternative treatments, such as psychological ones. By contrast, patients with high Φ show strong nociceptive pain components and could benefit more from pharmacotherapy.
TIP and Φ, explaining the multidimensionality of pain, might provide a new tool potentially leading to targeted treatments, thereby reducing the costs of inefficient generic therapies.
RESC-PainSense, SNSF-MOVE-IT197271.
疼痛是一种复杂的主观体验，对健康和生活质量有重大影响。尽管人们多次尝试寻找有效的解决方案，但目前的治疗方法比较通用，往往效果不佳，且存在明显的副作用。设计个性化治疗方案需要了解多维疼痛体验，同时考虑身体和情感方面。目前的临床疼痛评估依赖于主观的一维数字自我报告，无法捕捉到这种复杂性。
为此，我们利用机器学习来区分构成疼痛体验的生理和心理社会成分。从118名慢性疼痛患者和健康参与者那里收集了临床、心理社会和生理数据，他们共进行了40次疼痛试验（4697次试验）。
为了了解对伤害性刺激的客观反应，我们根据生理信号对疼痛进行分类（准确率>0.87），提取最重要的生物标志物。然后，使用多级混合效应模型，我们预测了报告的疼痛程度，量化了主观水平与测量的生理反应之间的差异。从这些模型中，我们引入了两个指标：TIP（疼痛主观指数）和Φ（生理指数）。它们分别代表了临床过程中可能的附加价值，捕捉了心理社会和生理疼痛维度。TIP值高的患者其特征是经常因病请假、临床抑郁和焦虑增加，这些因素与长期残疾和恢复不佳有关，适合采用替代治疗方法，如心理治疗。相比之下，Φ值高的患者表现出强烈的伤害性疼痛成分，可能从药物治疗中获益更多。
TIP和Φ解释了疼痛的多维性，可能提供一种新工具，有望实现针对性治疗，从而降低低效通用疗法的成本。
RESC-PainSense，瑞士国家科学基金会-MOVE-IT197271。
