Prediction models for breast cancer-related lymphedema: a systematic review and critical appraisal

Purpose: The development of risk prediction models for breast cancer lymphedema is increasing, but few studies focus on the quality of the model and its application. Therefore, this study aimed to systematically review and critically evaluate prediction models developed to predict breast cancer-related lymphedema. Methods: PubMed, Web of Science, Embase, MEDLINE, CNKI, Wang Fang DATA, Vip Database, and SinoMed were searched for studies published from 1 January 2000 to 1 June 2021. And it will be re-run before the final analysis. Two independent investigators will undertake the literature search and screening, and discrepancies will be resolved by another investigator. The Prediction model Risk Of Bias Assessment Tool will be used to assess the prediction models' risk of bias and applicability. Results: Seventeen studies were included in the systematic review, including 7 counties, of which 6 were prospective studies, only 7 models were validation studies, and 4 models were externally validated. The area under the curve of 17 models was 0.680-0.908. All studies had a high risk of bias, primarily due to the participants, outcome, and analysis. The most common predictors included body mass index, radiotherapy, chemotherapy, and axillary lymph node dissection. Conclusions: The predictive factors' strength, external validation, and clinical application of the breast cancer lymphedema risk prediction model still need further research. Healthcare workers should choose prediction models in clinical practice judiciously.