Background: The primary objective of this study was to assess the adequacy of analgesic care in radiotherapy (RT) patients, with a secondary objective to identify predictive variables associated with pain management adequacy using a modern statistical approach, integrating the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and the Classification and Regression Tree (CART) analysis. Methods: This observational, multicenter cohort study involved 1387 patients reporting pain or taking analgesic drugs from 13 RT departments in Italy. The Pain Management Index (PMI) served as the measure for pain control adequacy, with a PMI score < 0 indicating suboptimal management. Patient demographics, clinical status, and treatment-related factors were examined to discern the predictors of pain management adequacy. Results: Among the analyzed cohort, 46.1% reported inadequately managed pain. Non-cancer pain origin, breast cancer diagnosis, higher ECOG Performance Status scores, younger patient age, early assessment phase, and curative treatment intent emerged as significant determinants of negative PMI from the LASSO analysis. Notably, pain management was observed to improve as RT progressed, with a greater discrepancy between cancer (33.2% with PMI < 0) and non-cancer pain (73.1% with PMI < 0). Breast cancer patients under 70 years of age with non-cancer pain had the highest rate of negative PMI at 86.5%, highlighting a potential deficiency in managing benign pain in younger patients. Conclusions: The study underscores the dynamic nature of pain management during RT, suggesting improvements over the treatment course yet revealing specific challenges in non-cancer pain management, particularly among younger breast cancer patients. The use of advanced statistical techniques for analysis stresses the importance of a multifaceted approach to pain management, one that incorporates both cancer and non-cancer pain considerations to ensure a holistic and improved quality of oncological care.
背景：本研究的主要目的是评估放疗患者镇痛治疗的充分性，次要目标是通过整合最小绝对收缩与选择算子算法和分类回归树分析的现代统计方法，识别与疼痛管理充分性相关的预测变量。方法：这项观察性多中心队列研究纳入了意大利13个放疗科室中报告疼痛或服用镇痛药物的1387例患者。采用疼痛管理指数作为疼痛控制充分性的衡量指标，PMI评分<0表明管理欠佳。通过分析患者人口统计学特征、临床状态和治疗相关因素，识别疼痛管理充分性的预测因子。结果：在分析队列中，46.1%的患者报告疼痛管理不充分。LASSO分析显示，非癌性疼痛来源、乳腺癌诊断、较高的ECOG体能状态评分、较年轻的患者年龄、早期评估阶段以及根治性治疗意向是负向PMI的显著决定因素。值得注意的是，随着放疗的进行，疼痛管理有所改善，但癌性疼痛与非癌性疼痛的管理差异显著（癌性疼痛PMI<0者占33.2%，非癌性疼痛占73.1%）。70岁以下伴有非癌性疼痛的乳腺癌患者负向PMI发生率最高，达86.5%，突显了年轻患者良性疼痛管理方面存在的不足。结论：本研究强调了放疗期间疼痛管理的动态特性，表明在治疗过程中疼痛管理有所改善，但也揭示了非癌性疼痛管理面临的具体挑战，尤其是在年轻乳腺癌患者中。采用先进统计技术进行分析，强调了多维度疼痛管理方法的重要性，该方法应同时考虑癌性和非癌性疼痛，以确保肿瘤治疗质量的整体提升。
肿瘤(癌症)患者之家