Klein Ifat, Ben David Merav A, Shahar Danit R, Rosenberg Irena, Susmallian Sergio, Barsuk Daphna, Friger Michael
Assuta Medical Center, Ramat Hahayal, Tel Aviv 6971028, Israel.
Department of Epidemiology, Biostatistics and Community Health, School of Public Health, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel.
Oncologist. 2025 May 8;30(5). doi: 10.1093/oncolo/oyaf060.
This study presents the development process of the Arm Morbidity following Breast Cancer Treatments (ARM-BCT) tool.
A historical prospective study was conducted across five medical centers from 2020 to 2023. Medical information and a questionnaire covering morbidities, lifestyle, emotional state, and functioning were collected. Regression models analyzed 22 risk factors for chronic pain, lymphedema, functional limitations, and decreased range of motion. Significant factors were included in the ARM-BCT tool.
Seventeen significant risk factors were identified, including mastectomy (B = 2.073, CI, 7.403-5.366), axillary lymph node dissection (B = 0.194, CI, 0.988-1.036), breast reconstruction (B = 17.300, CI, 7.105-27.495), advanced stage (B = 0.498, CI, 1.044-2.594), chemotherapy (B = 1.326, CI, 0.870-3.673), BMI (B = 0.092, CI, 1.033-1.163), anxiety (B = 0.177, CI, 1.859-3.079), low physical activity levels (B = -0.059, CI, 0.190-0.001), specific comorbidities (B = -1.491, CI, 2.706-0.277), age (B = 0.035, OR = 1.036, CI, 1.002-1.071), and radiation therapy (B = 0.385, CI, 0.380-2.056), etc. The tool's development involved robust statistical modeling to determine the weight of each factor, evaluate model quality, and establish a clinically relevant cutoff point.
This article describes the development process of the ARM-BCT tool, designed to assess the risk of physical morbidity following breast cancer treatment. The tool incorporates 17 statistically significant risk and protective factors into a scoring scale ranging from 1 to 20. Risk is categorized as low (< 6) or high (> 7), enabling targeted recommendations for rehabilitation timing and necessity. While validation studies evaluating its clinical effectiveness are underway and will be presented in future publications, the ARM-BCT tool shows promise in enhancing recovery outcomes through early intervention.
本研究介绍了乳腺癌治疗后手臂发病率(ARM - BCT)工具的开发过程。
2020年至2023年在五个医疗中心进行了一项历史性前瞻性研究。收集了医疗信息以及一份涵盖发病率、生活方式、情绪状态和功能的问卷。回归模型分析了慢性疼痛、淋巴水肿、功能受限和活动范围减小的22个风险因素。显著因素被纳入ARM - BCT工具。
确定了17个显著风险因素,包括乳房切除术(B = 2.073,CI,7.403 - 5.366)、腋窝淋巴结清扫术(B = 0.194,CI,0.988 - 1.036)、乳房重建(B = 17.300,CI,7.105 - 27.495)、晚期(B = 0.498,CI,1.044 - 2.594)、化疗(B = 1.326,CI,0.870 - 3.673)、体重指数(B = 0.092,CI,1.033 - 1.163)、焦虑(B = 0.177,CI,1.859 - 3.079)、低体力活动水平(B = -0.059,CI,0.190 - 0.001)、特定合并症(B = -1.491,CI,2.706 - 0.277)、年龄(B = 0.035,OR = 1.036,CI,1.002 - 1.071)和放射治疗(B = 0.385,CI,0.380 - 2.056)等。该工具的开发涉及强大的统计建模,以确定每个因素的权重、评估模型质量并建立临床相关的临界点。
本文描述了ARM - BCT工具的开发过程,该工具旨在评估乳腺癌治疗后身体发病的风险。该工具将17个具有统计学意义的风险和保护因素纳入了一个从1到20的评分量表。风险分为低(<6)或高(>7),可为康复时机和必要性提供有针对性的建议。虽然评估其临床有效性的验证研究正在进行,并将在未来的出版物中呈现,但ARM - BCT工具在通过早期干预改善康复结果方面显示出前景。