■上肢淋巴水肿是与手术相关的最常见的不良事件之一,因为指南实施与预期的临床结果之间存在巨大差距。然而,由于临床表现模糊,因此监测肢体淋巴水肿仍具有挑战性.本研究旨在通过机器学习开发和验证上肢淋巴水肿的实用预测模型。
■我们根据2021年6月至2023年6月接受乳腺癌手术的患者的单中心电子健康记录数据,回顾性收集了临床数据,以开发用于上肢淋巴水肿早期风险预测的模型。对于预测模型构建,70%和30%的数据被随机分成训练集和测试集,分别。然后,我们使用机器学习算法开发了上肢淋巴水肿预测模型,其中包括随机森林模型(RFM),广义逻辑回归模型(GLRM),和人工神经网络模型(ANNM)。为了评估模型的性能,我们使用接受者工作特征曲线下面积(AUROC),校准曲线比较不同的模型。通过使用决策曲线分析(DCA)的净收益方法评估最佳阈值下最佳模型的潜在临床有用性。
■在筛选合格的3201名患者中,招募了3160名参与者用于预测模型。其中,身体质量指数(BMI),高血压,TNM,病变部位,淋巴结清扫术(LNMD),治疗,和护士是上肢淋巴水肿的独立危险因素,并被列为基于ML的预测模型的候选变量。RFM算法,结合七个候选变量,在训练集和内部验证集中展示了最高的预测效率,曲线下面积(AUC)分别为0.894和0.889,95%置信区间(CI)分别为0.839-0.949和0.834-0.944。其他两种类型的预测模型在AUC之间的预测效率分别为0.731和0.819,95%CIs分别为0.674-0.789和0.762-0.876。
■可解释的预测模型可帮助医生更准确地预测接受乳腺癌手术的患者的上肢淋巴水肿风险。特别是对于RFM,这种新建立的基于机器学习的模型对区分上肢淋巴水肿的高风险显示出良好的预测能力,这可以促进未来的临床决策,医院管理,并改善结果。
UNASSIGNED: Upper limb lymphedema is one of the most common adverse events related to surgery owing to the large gap between guideline implementation and the intended clinical outcomes. However, the monitoring of limb lymphedema remains challenging because of vague clinical presentations. This study aimed to develop and validate practical predictive models for upper limb lymphedema through machine learning.
UNASSIGNED: We retrospectively collected clinical data to develop models for early risk prediction of upper limb lymphedema based on a single-center electronic health record data from patients who underwent breast cancer surgery from June 2021 through June 2023. For prediction model building, 70% and 30% of the data were randomly split into training and testing sets, respectively. We then developed an upper limb lymphedema prediction model using machine learning algorithms, which included random forest model (RFM), generalized logistic regression model (GLRM), and artificial neural network model (ANNM). For evaluating the model\'s performance, we used the area under the receiver operating characteristic curve (AUROC), calibration curve to compare different models. The potential clinical usefulness of the best model at the best threshold was assessed through a net benefit approach using a decision curve analysis (DCA).
UNASSIGNED: Of the 3201 patients screened for eligibility, 3160 participants were recruited for the prediction model. Among these, Body Mass Index (BMI), hypertension, TNM, lesion site, level of lymph node dissection(LNMD), treatment, and nurse were independent risk factors for upper limb lymphedema and were listed as candidate variables of ML-based prediction models. The RFM algorithm, in combination with seven candidate variables, demonstrated the highest prediction efficiency in both the training and internal verification sets, with an area under the curve (AUC) of 0.894 and 0.889 and a 95% confidence interval (CI) of 0.839-0.949 and 0.834-0.944, respectively. The other two types of prediction models had prediction efficiencies between AUCs of 0.731 and 0.819 and 95% CIs of 0.674-0.789 and 0.762-0.876, respectively.
UNASSIGNED: The interpretable predictive model helps physicians more accurately predict the upper limb lymphedema risk in patients undergoing breast cancer surgery. Especially for the RFM, this newly established machine learning-based model has shown good predictive ability for distinguishing high risk of upper limb lymphedema, which could facilitate future clinical decisions, hospital management, and improve outcomes.