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癌症患者疼痛的生理反应:系统评价。

Physiological responses to pain in cancer patients: A systematic review.

机构信息

Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" - DEI, University of Bologna, Bologna, Italy.

IRCCS Istituto Delle Scienze Neurologiche Di Bologna, UOC Clinica Neurologica NeuroMet, Ospedale Bellaria, Bologna, Italy; Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy.

出版信息

Comput Methods Programs Biomed. 2022 Apr;217:106682. doi: 10.1016/j.cmpb.2022.106682. Epub 2022 Feb 5.

Abstract

BACKGROUND AND OBJECTIVE

Pain is one of the most debilitating symptoms in persons with cancer. Still, its assessment is often neglected both by patients and healthcare professionals. There is increasing interest in conducting pain assessment and monitoring via physiological signals that promise to overcome the limitations of state-of-the-art pain assessment tools. This systematic review aims to evaluate existing experimental studies to identify the most promising methods and results for objectively quantifying cancer patients' pain experience.

METHODS

Four electronic databases (Pubmed, Compendex, Scopus, Web of Science) were systematically searched for articles published up to October 2020.

RESULTS

Fourteen studies (528 participants) were included in the review. The selected studies analyzed seven physiological signals. Blood pressure and ECG were the most used signals. Sixteen physiological parameters showed significant changes in association with pain. The studies were fairly consistent in stating that heart rate, the low-frequency to high-frequency component ratio (LF/HF), and systolic blood pressure positively correlate with the pain.

CONCLUSIONS

Current evidence supports the hypothesis that physiological signals can help objectively quantify, at least in part, cancer patients' pain experience. While there is much more to be done to obtain a reliable pain assessment method, this review takes an essential first step by highlighting issues that should be taken into account in future research: use of a wearable device for pervasive recording in a real-world context, implementation of a big-data approach possibly supported by AI, including multiple stratification factors (e.g., cancer site and stage, source of pain, demographic and psychosocial data), and better-defined recording procedures. Improved methods and algorithms could then become valuable add-ons in taking charge of cancer patients.

摘要

背景与目的

疼痛是癌症患者最具致残性的症状之一。然而,无论是患者还是医疗保健专业人员,往往都忽略了对其的评估。人们越来越感兴趣的是通过生理信号来进行疼痛评估和监测,这些信号有望克服现有疼痛评估工具的局限性。本系统综述旨在评估现有的实验研究,以确定最有前途的方法和结果,用于客观量化癌症患者的疼痛体验。

方法

系统地检索了四个电子数据库(Pubmed、Compendex、Scopus、Web of Science),以获取截至 2020 年 10 月发表的文章。

结果

综述纳入了 14 项研究(528 名参与者)。所选研究分析了七种生理信号。血压和心电图是使用最多的信号。16 个生理参数与疼痛有显著变化相关。这些研究在陈述心率、低频到高频成分比(LF/HF)和收缩压与疼痛呈正相关方面相当一致。

结论

现有证据支持这样一种假设,即生理信号可以帮助客观地量化(至少部分地)癌症患者的疼痛体验。虽然要获得一种可靠的疼痛评估方法还有很多工作要做,但本综述通过突出未来研究中应考虑的问题迈出了重要的第一步:使用可穿戴设备在真实环境中进行普遍记录,实施可能由人工智能支持的大数据方法,包括多个分层因素(例如,癌症部位和阶段、疼痛来源、人口统计学和社会心理数据),以及更明确的记录程序。改进的方法和算法随后可能成为管理癌症患者的有价值的附加手段。

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