Shen Aomei, Ye Jingming, Zhao Hongmei, Qiang Wanmin, Zhao Hongmeng, Huang Yubei, Zhou Yujie, Wang Yue, Li Xin, Zhang Zhongning, Bian Jingru, Zhang Liyuan, Wu Peipei, Wang Ying, Lu Qian
Department of Nursing, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin, China.
Peking University School of Nursing, Beijing, China.
BMJ Open. 2024 Dec 20;14(12):e089769. doi: 10.1136/bmjopen-2024-089769.
Lymphoedema is a distressing and long-term complication for breast cancer survivors. However, the reported incidence of lymphoedema varies, and its risk factors remain underexplored. Currently, a well-established risk prediction model is still lacking. This study aims to describe the rationale, objectives, protocol and baseline characteristics of a prospective cohort study focused on examining the incidence and risk factors of breast cancer-related lymphoedema (BCRL), as well as developing a risk prediction model.
This study is an ongoing single-centre prospective observational cohort study recruiting 1967 patients with breast cancer scheduled for surgery treatment in northern China between 15 February 2022 and 21 June 2023. Assessments will be conducted presurgery and at 1, 3, 6, 12, 18, 24, 30 and 36 months postsurgery. Bilateral limb circumferences will be measured by patients at home or by researchers at the outpatient clinics during follow-up visits. The diagnosis of lymphoedema is based on a relative limb volume increase of ≥10% from the preoperative assessment. Self-reported symptoms will be assessed to assist in diagnosis. Potential risk factors are classified into innate personal traits, behavioural lifestyle, interpersonal networks, socioeconomic status and macroenvironmental factors, based on health ecology model. Data collection, storage and management were conducted using the online 'H6WORLD' data management platform. Survival analysis using the Kaplan-Meier estimate will determine the incidence of BCRL. Risk factors of BCRL will be analysed using log-rank test and COX-LASSO regression. Traditional COX regression analysis and seven common survival analysis machine learning algorithms (COX, CARST, RSF, GBSM, XGBS, SSVM and SANN) will be employed for model construction and validation.
The study protocol was approved by the Biomedical Ethics Committee of Peking University (IRB00001052-21124) and the Research Ethics Committee of Tianjin Medical University Cancer Institute and Hospital (bc2023013). The results of this study will be published in peer-reviewed journals and will be presented at several research conferences.
ChiCTR2200057083.
淋巴水肿是乳腺癌幸存者面临的一种令人痛苦的长期并发症。然而，报道的淋巴水肿发病率各不相同，其危险因素仍未得到充分探索。目前，仍缺乏一个成熟的风险预测模型。本研究旨在描述一项前瞻性队列研究的基本原理、目标、方案和基线特征，该研究重点关注乳腺癌相关淋巴水肿（BCRL）的发病率和危险因素，并开发一个风险预测模型。
本研究是一项正在进行的单中心前瞻性观察性队列研究，在中国北方招募1967例计划接受手术治疗的乳腺癌患者，时间为2022年2月15日至2023年6月21日。评估将在术前以及术后1、3、6、12、18、24、30和36个月进行。在随访期间，患者将在家中自行测量双侧肢体周长，或由研究人员在门诊诊所进行测量。淋巴水肿的诊断基于与术前评估相比肢体体积相对增加≥10%。将评估自我报告的症状以辅助诊断。根据健康生态模型，潜在危险因素分为先天个人特质、行为生活方式、人际网络、社会经济地位和宏观环境因素。使用在线“H6WORLD”数据管理平台进行数据收集、存储和管理。使用Kaplan-Meier估计进行生存分析将确定BCRL的发病率。将使用对数秩检验和COX-LASSO回归分析BCRL的危险因素。将采用传统的COX回归分析和七种常见的生存分析机器学习算法（COX、CARST、RSF、GBSM、XGBS、SSVM和SANN）进行模型构建和验证。
本研究方案已获得北京大学医学伦理委员会（IRB00001052-21124）和天津医科大学肿瘤研究所和医院研究伦理委员会（bc2023013）的批准。本研究结果将发表在同行评审期刊上，并将在多个研究会议上展示。
ChiCTR2200057083。
