Abstract：
BACKGROUND: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
BACKGROUND: RESC-PainSense, SNSF-MOVE-IT197271.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
BACKGROUND: RESC-PainSense, SNSF-MOVE-IT197271.
摘要：
背景：疼痛是一种复杂的主观体验，强烈影响健康和生活质量。尽管许多人试图找到有效的解决方案，目前的治疗方法是通用的，往往不成功,并表现出明显的副作用。设计个性化疗法需要了解多维疼痛体验，考虑身体和情感方面。目前的临床疼痛评估，依靠主观的一维数字自我报告，无法捕捉到这种复杂性。
方法：为此，我们利用机器学习来解开塑造疼痛体验的生理和心理因素。临床,社会心理,我们收集了118例慢性疼痛和40例疼痛试验(4,697项试验)健康参与者的生理数据.
结果：为了了解对伤害性感受的客观反应，我们从生理信号中分类疼痛(准确度>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，量化主观水平和测量的生理反应之间的不匹配。从这些模型中，我们引入了两个指标：TIP（主观疼痛指数）和Φ（生理指数）。这些代表了临床过程中可能的附加值，捕捉心理社会和生理疼痛维度，分别。高TIP患者的特点是频繁的工作病假和增加临床抑郁和焦虑,与长期残疾和康复不良相关的因素，并用于替代治疗，比如心理上的。相比之下,高Φ患者表现出强烈的伤害性疼痛成分，可以从药物治疗中获益更多。
结论：TIP和Φ，解释疼痛的多维性，可能提供一种可能导致靶向治疗的新工具，从而降低低效通用疗法的成本。
背景：RESC-PainSense，SNSF-MOVE-IT197271。
方法：为此，我们利用机器学习来解开塑造疼痛体验的生理和心理因素。临床,社会心理,我们收集了118例慢性疼痛和40例疼痛试验(4,697项试验)健康参与者的生理数据.
结果：为了了解对伤害性感受的客观反应，我们从生理信号中分类疼痛(准确度>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，量化主观水平和测量的生理反应之间的不匹配。从这些模型中，我们引入了两个指标：TIP（主观疼痛指数）和Φ（生理指数）。这些代表了临床过程中可能的附加值，捕捉心理社会和生理疼痛维度，分别。高TIP患者的特点是频繁的工作病假和增加临床抑郁和焦虑,与长期残疾和康复不良相关的因素，并用于替代治疗，比如心理上的。相比之下,高Φ患者表现出强烈的伤害性疼痛成分，可以从药物治疗中获益更多。
结论：TIP和Φ，解释疼痛的多维性，可能提供一种可能导致靶向治疗的新工具，从而降低低效通用疗法的成本。
背景：RESC-PainSense，SNSF-MOVE-IT197271。
