Development and Validation of an Intraoperative Nomogram to Predict Breast Cancer-Related Lymphedema Based on the Arm Lymphatics Distribution

Yuan, Qianqian; Hou, Jian; Zhou, Rui; Liao, Yiqin; Zheng, Luming; Jiao, Chong; Zhou, Wenbo; Wu, Zeyu

doi:10.1245/s10434-021-09982-0

Cited by 7 publications

(14 citation statements)

References 31 publications

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“…Most of BCRL prediction models were based on Chinese people ( n =9, 52.94%) and 12 studies were published in the past 5 years. Most studies included various factors to establish the prediction model of breast cancer lymphedema: Yuan et al’s study [ 14 ] was based on the normal level of lymphatic vessels, and Penn et al [ 32 ] studied the prediction model of persistent lymphedema. Wei et al [ 28 ] used symptoms as predictors to develop a risk prediction model for the early detection of lymphedema.…”

Section: Resultsmentioning

confidence: 99%

“…Kim et al [ 39 ] diagnosed lymphedema with swelling of the affected arm exceeding 5% of the circumference difference; 5 studies used the capacity difference to diagnose BCRL, which also had a threshold difference. Li and Bevilacqua et al [ 30 , 40 ] indicated that the volume difference between the arms was greater than 200 mL, and Yuan et al [ 14 ] indicated that the volume difference was more than 10%, which was diagnosed as lymphedema.…”

Section: Resultsmentioning

confidence: 99%

“…And in Liu et al, Kim et al, Wang et al, Penn et al, Basta et al, Byun et al, and Martinez-Jaimez et al [ 13 , 29 , 30 , 32 , 35 , 36 , 39 ], EPV was higher than 20. Question 4 is about participants with missing data handled: most studies did not report changes in follow-up data; only Yuan et al, Liu et al, and Li et al [ 12 , 14 , 30 ] were at a low-bias risk; the study of Yuan et al [ 14 ] has no missing data; and Liu et al and Li et al [ 12 , 30 ] are cross-sectional analysis without missing data. Question 5 is about the selection of predictors, and only Gross et al and Martinez-Jaimez et al [ 11 , 13 ] avoided selecting the predictors with a statistically significant univariable association.…”

Section: Resultsmentioning

confidence: 99%

“…Only one of the models included in this study is of low concern regarding applicability. The models established by Yuan et al, Wang et al, Kwan et al, and Basta et al [ 14 , 31 , 35 , 36 ] were of unclear concerns regarding applicability and Kim et al [ 39 ] were of low concerns regarding applicability. The remaining 12 studies were of high concern regarding applicability.…”

Section: Resultsmentioning

confidence: 99%

“…BCRL has different aspect risk factors, including demographic, physiological, biochemical, and treatment-related factors [ 8 , 9 ]. In most of the studies, age is one of the risk factors for BCRL [ 10 ], but the predictor of age is not included in the BCRL prediction model in the study of prediction models commonly [ 11 – 14 ]. It may be related to the age of each included population and the different age stratification of each study.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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. Systematic review registration PROSPERO CRD42021258832

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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Feasibility and Clinical Utility of Prediction Models for Breast Cancer–Related Lymphedema Incorporating Racial Differences in Disease Incidence

et al. 2023

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ImportanceBreast cancer–related lymphedema (BCRL) is a common complication of axillary lymph node dissection (ALND) but can also develop after sentinel lymph node biopsy (SLNB). Several models have been developed to predict the risk of disease development before and after surgery; however, these models have shortcomings that include the omission of race, inclusion of variables that are not readily available to patients, low sensitivity or specificity, and lack of risk assessment for patients treated with SLNB.ObjectiveTo create simple and accurate prediction models for BCRL that can be used to estimate preoperative or postoperative risk.Design, Setting, and ParticipantsIn this prognostic study, women with breast cancer who underwent ALND or SLNB from 1999 to 2020 at Memorial Sloan Kettering Cancer Center and the Mayo Clinic were included. Data were analyzed from September to December 2022.Main Outcomes and MeasuresDiagnosis of lymphedema based on measurements. Two predictive models were formulated via logistic regression: a preoperative model (model 1) and a postoperative model (model 2). Model 1 was externally validated using a cohort of 34 438 patients with an International Classification of Diseases diagnosis of breast cancer.ResultsOf 1882 included patients, all were female, and the mean (SD) age was 55.6 (12.2) years; 80 patients (4.3%) were Asian, 190 (10.1%) were Black, 1558 (82.8%) were White, and 54 (2.9%) were another race (including American Indian and Alaska Native, other race, patient refused to disclose, or unknown). A total of 218 patients (11.6%) were diagnosed with BCRL at a mean (SD) follow-up of 3.9 (1.8) years. The BCRL rate was significantly higher among Black women (42 of 190 [22.1%]) compared with all other races (Asian, 10 of 80 [12.5%]; White, 158 of 1558 [10.1%]; other race, 8 of 54 [14.8%]; P &lt; .001). Model 1 included age, weight, height, race, ALND/SLNB status, any radiation therapy, and any chemotherapy. Model 2 included age, weight, race, ALND/SLNB status, any chemotherapy, and patient-reported arm swelling. Accuracy was 73.0% for model 1 (sensitivity, 76.6%; specificity, 72.5%; area under the receiver operating characteristic curve [AUC], 0.78; 95% CI, 0.75-0.81) at a cutoff of 0.18, and accuracy was 81.1% for model 2 (sensitivity, 78.0%; specificity, 81.5%; AUC, 0.86; 95% CI, 0.83-0.88) at a cutoff of 0.10. Both models demonstrated high AUCs on external (model 1: 0.75; 95% CI, 0.74-0.76) or internal (model 2: 0.82; 95% CI, 0.79-0.85) validation.Conclusions and RelevanceIn this study, preoperative and postoperative prediction models for BCRL were highly accurate and clinically relevant tools comprised of accessible inputs and underscored the effects of racial differences on BCRL risk. The preoperative model identified high-risk patients who require close monitoring or preventative measures. The postoperative model can be used for screening of high-risk patients, thus decreasing the need for frequent clinic visits and arm volume measurements.

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Risk prediction models for breast cancer-related lymphedema: A systematic review and meta-analysis

Shen

Wei

Zhu

et al. 2023

European Journal of Oncology Nursing

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Development and Validation of an Intraoperative Nomogram to Predict Breast Cancer-Related Lymphedema Based on the Arm Lymphatics Distribution

Cited by 7 publications

References 31 publications

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

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

Feasibility and Clinical Utility of Prediction Models for Breast Cancer–Related Lymphedema Incorporating Racial Differences in Disease Incidence

Risk prediction models for breast cancer-related lymphedema: A systematic review and meta-analysis

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