Noemi Gozzi, Greta Preatoni, Federico Ciotti, Michèle Hubli, Petra Schweinhardt, Armin Curt, Stanisa Raspopovic
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引用次数: 0
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).
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.
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