Department of Medicine, Surgery and Dentistry, Anesthesia and Pain Medicine, University of Salerno, Via Salvador Allende 43, Baronissi Salerno, 84081, Italy.
Epidemiology and Biostatistics Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Via Mariano Semmola 53, Naples, 80131, Italy.
BMC Palliat Care. 2024 Aug 3;23(1):198. doi: 10.1186/s12904-024-01526-z.
Tailoring effective strategies for cancer pain management requires a careful analysis of multiple factors that influence pain phenomena and, ultimately, guide the therapy. While there is a wealth of research on automatic pain assessment (APA), its integration with clinical data remains inadequately explored. This study aimed to address the potential correlations between subjective and APA-derived objectives variables in a cohort of cancer patients.
A multidimensional statistical approach was employed. Demographic, clinical, and pain-related variables were examined. Objective measures included electrodermal activity (EDA) and electrocardiogram (ECG) signals. Sensitivity analysis, multiple factorial analysis (MFA), hierarchical clustering on principal components (HCPC), and multivariable regression were used for data analysis.
The study analyzed data from 64 cancer patients. MFA revealed correlations between pain intensity, type, Eastern Cooperative Oncology Group Performance status (ECOG), opioids, and metastases. Clustering identified three distinct patient groups based on pain characteristics, treatments, and ECOG. Multivariable regression analysis showed associations between pain intensity, ECOG, type of breakthrough cancer pain, and opioid dosages. The analyses failed to find a correlation between subjective and objective pain variables.
The reported pain perception is unrelated to the objective variables of APA. An in-depth investigation of APA is required to understand the variables to be studied, the operational modalities, and above all, strategies for appropriate integration with data obtained from self-reporting.
This study is registered with ClinicalTrials.gov, number (NCT04726228), registered 27 January 2021, https://classic.
gov/ct2/show/NCT04726228?term=nct04726228&draw=2&rank=1.
为了制定有效的癌症疼痛管理策略，需要仔细分析影响疼痛现象的多种因素，这些因素最终会指导治疗。虽然有大量关于自动疼痛评估（APA）的研究，但它与临床数据的整合仍未得到充分探索。本研究旨在分析癌症患者队列中主观和 APA 衍生的客观变量之间的潜在相关性。
采用多维统计方法。检查了人口统计学、临床和疼痛相关变量。客观测量包括皮肤电活动（EDA）和心电图（ECG）信号。采用灵敏度分析、多因素分析（MFA）、主成分的层次聚类（HCPC）和多变量回归进行数据分析。
该研究分析了 64 名癌症患者的数据。MFA 显示疼痛强度、类型、东部合作肿瘤学组表现状态（ECOG）、阿片类药物和转移之间存在相关性。聚类根据疼痛特征、治疗和 ECOG 将患者分为三组。多变量回归分析显示疼痛强度、ECOG、爆发性癌痛类型和阿片类药物剂量之间存在关联。分析未发现主观和客观疼痛变量之间的相关性。
报告的疼痛感知与 APA 的客观变量无关。需要深入研究 APA，以了解要研究的变量、操作模式，最重要的是，要研究与自我报告数据的适当整合策略。
本研究在 ClinicalTrials.gov 注册，编号为（NCT04726228），于 2021 年 1 月 27 日注册，https://classic.clinicaltrials.gov/ct2/show/NCT04726228?term=nct04726228&draw=2&rank=1。
NCT04726228。
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