Development and validation of a nomogram to predict the risk of breast cancer-related lymphedema among Chinese breast cancer survivors

Liu, Yan fei; Liu, Jun E.; Zhu, Ye; Mak, Yim Wah; Qiu, Hui; Liu, Li hui; Yang, Shuang; Chen, Shao hua

doi:10.1007/s00520-021-06122-y

Cited by 9 publications

(11 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with foreign countries, Chinese researchers focus on the influence of patients’ behavior on lymphedema. For example, Liu et al and Li et al [ 12 , 38 ] include the exercise of the affected arm and the level of physical activity. The predictors included from 3 to 7 in the study.…”

Section: Resultsmentioning

confidence: 99%

“…2 ). For example, the study by Liu et al [ 12 ] limited population and included those who had completed breast cancer surgery at least 6 months, and patients who had not been included in the study within 6 months after surgery probably cause selection bias. The study by Gross et al [ 11 ] used a data derived from randomized controlled trials.…”

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%

“…For example, Kim et al’s study [ 39 ] is bounded by the number of dissections 10, and Yang et al’s study [ 33 ] is leveled by the number of dissections 7 and 15. For radiotherapy, it can be divided into whether radiotherapy and radiotherapy area, the study of Yuan et al, Liu et al, and Gross et al [ 11 , 12 , 14 ]. BMI was presented as a continuous variable, while Yang et al [ 33 ] classified it by 18.5 and 22.9.…”

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

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

et al. 2022

View full text Add to dashboard Cite

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

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2022

View full text Add to dashboard Cite

show abstract

“…However, carrying the drainage tube for a long time can lead to stiffness in the arm and inevitably, the optimal time for post-operative rehabilitation exercises is missed. A cross-sectional study of 766 patients showed that women who exercised their affected arm decreased the risk of developing BCRL through a potential mechanism called "muscle pump" [12].…”

Section: Discussionmentioning

confidence: 99%

Factors affecting lymphedema after neoadjuvant chemotherapy and axillary dissection in female breast cancer patients: A retrospective cohort study based on the Chinese population

Chen

et al. 2023

Preprint

View full text Add to dashboard Cite

Purpose Breast cancer-related lymphedema (BCRL) is a common complication in breast cancer survivors. Most BCRL studies have focused on patients receiving adjuvant chemotherapy, and relatively little attention has been paid to BCRL in patients with neoadjuvant chemotherapy (NAC). This study aimed to investigate the risk factors associated with BCRL in Chinese women undergoing NAC and axillary lymph node dissection (ALND). Methods In our institution, this cohort study collected data from 336 women with clinically node-positive breast cancer who received NAC and ALND surgery from 2015 to 2020. The BCRL was assessed by objective measurement of limb circumference. Multivariate logistic regression was used to determine risk factors for BCRL, including clinical, demographic and lifestyle-related characteristics. Results The cumulative incidence of BCRL within 2.5 years was 43.75%. The factors independently associated with BCRL were radiotherapy (versus no radiotherapy; hazard ratio [HR]=1.611; P=0.020), NAC for 105 days or shorter (versus NAC for 105-143 days; HR=0.471; P=0.020), >15 lymph nodes removed (versus ≤15 lymph nodes removed; HR=1.593; P=0.036 ), drainage for 20-29 days (versus drainage for 10-19 days; HR=1.568; P=0.028), and sleeping biased toward affected arm (versus sleeping biased toward healthy arm; HR=2.033; P=0.019). Conclusion This study identified several risk factors for BCRL in breast cancer patients after NAC and ALND. Patients with one or more of these factors should be monitored carefully for early detection and intervention. Further research is needed to investigate the effects of drainage time and sleep position on the development of BCRL.

show abstract

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Development and validation of a nomogram to predict the risk of breast cancer-related lymphedema among Chinese breast cancer survivors

Cited by 9 publications

References 36 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

Factors affecting lymphedema after neoadjuvant chemotherapy and axillary dissection in female breast cancer patients: A retrospective cohort study based on the Chinese population

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

Contact Info

Product

Resources

About