Abstract：
OBJECTIVE: To perform temporal validation of a risk prediction model for breast cancer-related lymphoedema in the European population.
METHODS: Temporal validation of a previously developed prediction model using a new retrospective cohort of women who had undergone axillary lymph node dissection between June 2018 and June 2020.
METHODS: We reviewed clinical records to identify women who did and did not develop lymphoedema within 2 years of surgery and to gather data regarding the variables included in the prediction model. The model was calibrated by calculating Spearman\'s correlation between observed and expected cases. Its accuracy in discriminating between patients who did versus did not develop lymphoedema was assessed by calculating the area under the receiver operating characteristic curve (AUC).
RESULTS: The validation cohort comprised 154 women, 41 of whom developed lymphoedema within 2 years of surgery. The value of Spearman\'s coefficient indicated a strong correlation between observed and expected cases. Sensitivity of the model was higher than in the derivation cohort, as was the value of the AUC.
CONCLUSIONS: The model shows a good capacity to discriminate women at risk of lymphoedema and may therefore help in developing improved care pathways for individual patients.
UNASSIGNED: Identifying risk factors for lymphoedema secondary to breast cancer treatment is vital given its impact on women\'s physical and emotional well-being.
CONCLUSIONS: What problem did the study address? Risk of BCRL. What were the main findings? The prediction model has a good capacity to discriminate women at risk of lymphoedema. Where and on whom will the research have an impact? In clinical practice with women at risk of BCRL.
UNASSIGNED: STROBE checklist. WHAT DOES THIS PAPER CONTRIBUTE TO THE WIDER GLOBAL CLINICAL COMMUNITY?: It presents a validated risk prediction model for BCRL.
UNASSIGNED: There was no patient or public contribution in the conduct of this study.
METHODS: Temporal validation of a previously developed prediction model using a new retrospective cohort of women who had undergone axillary lymph node dissection between June 2018 and June 2020.
METHODS: We reviewed clinical records to identify women who did and did not develop lymphoedema within 2 years of surgery and to gather data regarding the variables included in the prediction model. The model was calibrated by calculating Spearman\'s correlation between observed and expected cases. Its accuracy in discriminating between patients who did versus did not develop lymphoedema was assessed by calculating the area under the receiver operating characteristic curve (AUC).
RESULTS: The validation cohort comprised 154 women, 41 of whom developed lymphoedema within 2 years of surgery. The value of Spearman\'s coefficient indicated a strong correlation between observed and expected cases. Sensitivity of the model was higher than in the derivation cohort, as was the value of the AUC.
CONCLUSIONS: The model shows a good capacity to discriminate women at risk of lymphoedema and may therefore help in developing improved care pathways for individual patients.
UNASSIGNED: Identifying risk factors for lymphoedema secondary to breast cancer treatment is vital given its impact on women\'s physical and emotional well-being.
CONCLUSIONS: What problem did the study address? Risk of BCRL. What were the main findings? The prediction model has a good capacity to discriminate women at risk of lymphoedema. Where and on whom will the research have an impact? In clinical practice with women at risk of BCRL.
UNASSIGNED: STROBE checklist. WHAT DOES THIS PAPER CONTRIBUTE TO THE WIDER GLOBAL CLINICAL COMMUNITY?: It presents a validated risk prediction model for BCRL.
UNASSIGNED: There was no patient or public contribution in the conduct of this study.
摘要：
目的：对欧洲人群乳腺癌相关淋巴水肿的风险预测模型进行时间验证。
方法：使用一项新的回顾性队列研究，对先前开发的预测模型进行时间验证，该研究是在2018年6月至2020年6月期间接受腋窝淋巴结清扫术的女性。
方法：我们回顾了临床记录，以确定在手术后2年内发生和未发生淋巴水肿的妇女，并收集有关预测模型中变量的数据。通过计算观察到的病例和预期病例之间的Spearman\'s相关性来对模型进行校准。通过计算受试者工作特征曲线（AUC）下的面积来评估其区分发生淋巴水肿和未发生淋巴水肿的患者的准确性。
结果：验证队列包括154名女性，其中41人在手术后2年内出现淋巴水肿。Spearman系数的值表明观察到的病例和预期的病例之间有很强的相关性。模型的灵敏度高于衍生队列，AUC的值也是如此。
结论：该模型显示出良好的区分有淋巴水肿风险的女性的能力，因此可能有助于为个体患者开发改进的护理途径。
■识别乳腺癌治疗继发的淋巴水肿的危险因素是至关重要的，因为它对女性的身体和情绪健康的影响。
结论：研究解决了什么问题？BCRL的风险。主要发现是什么？预测模型具有很好的区分有淋巴水肿风险的女性的能力。这项研究将在哪里以及对谁产生影响？在临床实践中，有BCRL风险的女性。
■STROBE检查表。本文对广泛的全球临床社区有什么贡献？：它为BCRL提供了一个经过验证的风险预测模型。
■这项研究没有患者或公众的贡献。
方法：使用一项新的回顾性队列研究，对先前开发的预测模型进行时间验证，该研究是在2018年6月至2020年6月期间接受腋窝淋巴结清扫术的女性。
方法：我们回顾了临床记录，以确定在手术后2年内发生和未发生淋巴水肿的妇女，并收集有关预测模型中变量的数据。通过计算观察到的病例和预期病例之间的Spearman\'s相关性来对模型进行校准。通过计算受试者工作特征曲线（AUC）下的面积来评估其区分发生淋巴水肿和未发生淋巴水肿的患者的准确性。
结果：验证队列包括154名女性，其中41人在手术后2年内出现淋巴水肿。Spearman系数的值表明观察到的病例和预期的病例之间有很强的相关性。模型的灵敏度高于衍生队列，AUC的值也是如此。
结论：该模型显示出良好的区分有淋巴水肿风险的女性的能力，因此可能有助于为个体患者开发改进的护理途径。
■识别乳腺癌治疗继发的淋巴水肿的危险因素是至关重要的，因为它对女性的身体和情绪健康的影响。
结论：研究解决了什么问题？BCRL的风险。主要发现是什么？预测模型具有很好的区分有淋巴水肿风险的女性的能力。这项研究将在哪里以及对谁产生影响？在临床实践中，有BCRL风险的女性。
■STROBE检查表。本文对广泛的全球临床社区有什么贡献？：它为BCRL提供了一个经过验证的风险预测模型。
■这项研究没有患者或公众的贡献。
