Risk factors and prediction model of breast cancer-related lymphoedema in a Chinese cancer centre: a prospective cohort study protocol
Introduction 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 ra...
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2024-12-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/14/12/e089769.full |
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| Summary: | Introduction 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.Methods and analysis 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.Ethics and dissemination 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.Trial registration number ChiCTR2200057083. |
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| ISSN: | 2044-6055 |