Noemi Gozzi, Greta Preatoni, Federico Ciotti, Michèle Hubli, Petra Schweinhardt, Armin Curt, Stanisa Raspopovic
{"title":"通过机器学习揭示疼痛的生理和社会心理特征。","authors":"Noemi Gozzi, Greta Preatoni, Federico Ciotti, Michèle Hubli, Petra Schweinhardt, Armin Curt, Stanisa Raspopovic","doi":"10.1016/j.medj.2024.07.016","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Findings: </strong>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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Funding: </strong>RESC-PainSense, SNSF-MOVE-IT197271.</p>","PeriodicalId":29964,"journal":{"name":"Med","volume":" ","pages":"1495-1509.e5"},"PeriodicalIF":12.8000,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unraveling the physiological and psychosocial signatures of pain by machine learning.\",\"authors\":\"Noemi Gozzi, Greta Preatoni, Federico Ciotti, Michèle Hubli, Petra Schweinhardt, Armin Curt, Stanisa Raspopovic\",\"doi\":\"10.1016/j.medj.2024.07.016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Findings: </strong>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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Funding: </strong>RESC-PainSense, SNSF-MOVE-IT197271.</p>\",\"PeriodicalId\":29964,\"journal\":{\"name\":\"Med\",\"volume\":\" \",\"pages\":\"1495-1509.e5\"},\"PeriodicalIF\":12.8000,\"publicationDate\":\"2024-12-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Med\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.medj.2024.07.016\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/8/7 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Med","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.medj.2024.07.016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/7 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
摘要
背景：疼痛是一种复杂的主观感受，严重影响着人们的健康和生活质量。尽管人们多次尝试寻找有效的解决方案，但目前的治疗方法都是通用的，往往不成功，而且副作用很大。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影响疼痛体验的生理和社会心理因素。我们收集了 118 名慢性疼痛和健康参与者的临床、社会心理和生理数据，并对其进行了 40 次疼痛试验（4,697 次试验）：为了了解对痛觉的客观反应，我们从生理信号中对疼痛进行了分类（准确率大于 0.87），提取了最重要的生物标志物。然后，利用多层次混合效应模型，我们预测了报告的疼痛，量化了主观水平与测量的生理反应之间的不匹配。根据这些模型，我们引入了两个指标：TIP（疼痛的主观指数）和Φ（生理指数）。这两个指标代表了临床过程中可能存在的附加值，分别反映了社会心理和生理疼痛方面的情况。高 TIP 患者的特点是经常请病假，临床抑郁和焦虑情绪加重，这些因素与长期残疾和恢复不良有关，因此适用于替代治疗，如心理治疗。相比之下，Φ高的患者表现出强烈的痛觉疼痛成分，可以从药物治疗中获益更多：结论：TIP和Φ解释了疼痛的多维性，可能为针对性治疗提供新的工具，从而降低低效普通疗法的成本：基金：RESC-PainSense、SNSF-MOVE-IT197271。
Unraveling the physiological and psychosocial signatures of pain by machine learning.
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).
Findings: 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.
期刊介绍：
Med is a flagship medical journal published monthly by Cell Press, the global publisher of trusted and authoritative science journals including Cell, Cancer Cell, and Cell Reports Medicine. Our mission is to advance clinical research and practice by providing a communication forum for the publication of clinical trial results, innovative observations from longitudinal cohorts, and pioneering discoveries about disease mechanisms. The journal also encourages thought-leadership discussions among biomedical researchers, physicians, and other health scientists and stakeholders. Our goal is to improve health worldwide sustainably and ethically.
Med publishes rigorously vetted original research and cutting-edge review and perspective articles on critical health issues globally and regionally. Our research section covers clinical case reports, first-in-human studies, large-scale clinical trials, population-based studies, as well as translational research work with the potential to change the course of medical research and improve clinical practice.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
