PDF(Pubmed)
Abstract：
OBJECTIVE: This study aims to explore the risk factors of vascular complications following free flap reconstruction and to develop a clinical auxiliary assessment tool for predicting vascular complications in patients undergoing free flap reconstruction leveraging machine learning methods.
METHODS: We reviewed the medical data of patients who underwent free flap reconstruction at the Affiliated Hospital of Zunyi Medical University retrospectively from January 1, 2019, to December 31, 2021. Statistical analysis was used to screen risk factors. A training data set was generated and augmented using the synthetic minority oversampling technique. Logistic regression, random forest and neural network, models were trained, using this dataset. The performance of these three predictive models was then evaluated and compared using a test set, with four metrics, area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.
RESULTS: A total of 570 patients who underwent free flap reconstruction were included in this study, 46 of whom developed postoperative vascular complications. Among the models tested, the neural network model exhibited superior performance on the test set, achieving an AUC of 0.828. Multivariate logistic regression analysis identified that preoperative hemoglobin levels, preoperative fibrinogen levels, operation duration, smoking history, the number of anastomoses, and peripheral vascular injury as statistically significant independent risk factors for vascular complications post-free flap reconstruction. The top five predictive factors in the neural network were fibrinogen content, operation duration, donor site, body mass index (BMI), and platelet count.
CONCLUSIONS: Hemoglobin levels, fibrinogen levels, operation duration, smoking history, and anastomotic veins are independent risk factors for vascular complications following free flap reconstruction. These risk factors enhance the ability of machine learning models to predict the occurrence of vascular complications and identify high-risk patients. The neural network model outperformed the logistic regression and random forest models, suggesting its potential to aid clinicians in early identification of high-risk patients thereby mitigating patient suffering and improving prognosis.
METHODS: We reviewed the medical data of patients who underwent free flap reconstruction at the Affiliated Hospital of Zunyi Medical University retrospectively from January 1, 2019, to December 31, 2021. Statistical analysis was used to screen risk factors. A training data set was generated and augmented using the synthetic minority oversampling technique. Logistic regression, random forest and neural network, models were trained, using this dataset. The performance of these three predictive models was then evaluated and compared using a test set, with four metrics, area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.
RESULTS: A total of 570 patients who underwent free flap reconstruction were included in this study, 46 of whom developed postoperative vascular complications. Among the models tested, the neural network model exhibited superior performance on the test set, achieving an AUC of 0.828. Multivariate logistic regression analysis identified that preoperative hemoglobin levels, preoperative fibrinogen levels, operation duration, smoking history, the number of anastomoses, and peripheral vascular injury as statistically significant independent risk factors for vascular complications post-free flap reconstruction. The top five predictive factors in the neural network were fibrinogen content, operation duration, donor site, body mass index (BMI), and platelet count.
CONCLUSIONS: Hemoglobin levels, fibrinogen levels, operation duration, smoking history, and anastomotic veins are independent risk factors for vascular complications following free flap reconstruction. These risk factors enhance the ability of machine learning models to predict the occurrence of vascular complications and identify high-risk patients. The neural network model outperformed the logistic regression and random forest models, suggesting its potential to aid clinicians in early identification of high-risk patients thereby mitigating patient suffering and improving prognosis.
摘要：
目的：本研究旨在探讨游离皮瓣重建后血管并发症的危险因素，并利用机器学习方法开发一种预测游离皮瓣重建患者血管并发症的临床辅助评估工具。
方法：回顾性分析2019年1月1日至2021年12月31日遵义医学院附属医院行游离皮瓣重建患者的医疗资料。采用统计学分析筛选危险因素。使用合成少数过采样技术生成并增强了训练数据集。Logistic回归，随机森林和神经网络，模型被训练，使用此数据集。然后使用测试集评估和比较这三个预测模型的性能，有四个指标，接收器工作特性曲线下面积（AUC），准确度,灵敏度,和特异性。
结果：本研究共纳入570例接受游离皮瓣重建的患者，其中46人发生术后血管并发症。在测试的模型中，神经网络模型在测试集上表现出优越的性能，AUC为0.828。多因素logistic回归分析确定术前血红蛋白水平，术前纤维蛋白原水平，操作持续时间,吸烟史,吻合的数量，和外周血管损伤是游离皮瓣重建后血管并发症的独立危险因素。神经网络中排名前五位的预测因素是纤维蛋白原含量，操作持续时间,捐赠网站,体重指数（BMI），和血小板计数。
结论：血红蛋白水平，纤维蛋白原水平，操作持续时间,吸烟史,吻合静脉是游离皮瓣重建后血管并发症的独立危险因素。这些风险因素增强了机器学习模型预测血管并发症发生和识别高危患者的能力。神经网络模型优于逻辑回归和随机森林模型，提示其可能帮助临床医生早期识别高危患者，从而减轻患者痛苦并改善预后。
方法：回顾性分析2019年1月1日至2021年12月31日遵义医学院附属医院行游离皮瓣重建患者的医疗资料。采用统计学分析筛选危险因素。使用合成少数过采样技术生成并增强了训练数据集。Logistic回归，随机森林和神经网络，模型被训练，使用此数据集。然后使用测试集评估和比较这三个预测模型的性能，有四个指标，接收器工作特性曲线下面积（AUC），准确度,灵敏度,和特异性。
结果：本研究共纳入570例接受游离皮瓣重建的患者，其中46人发生术后血管并发症。在测试的模型中，神经网络模型在测试集上表现出优越的性能，AUC为0.828。多因素logistic回归分析确定术前血红蛋白水平，术前纤维蛋白原水平，操作持续时间,吸烟史,吻合的数量，和外周血管损伤是游离皮瓣重建后血管并发症的独立危险因素。神经网络中排名前五位的预测因素是纤维蛋白原含量，操作持续时间,捐赠网站,体重指数（BMI），和血小板计数。
结论：血红蛋白水平，纤维蛋白原水平，操作持续时间,吸烟史,吻合静脉是游离皮瓣重建后血管并发症的独立危险因素。这些风险因素增强了机器学习模型预测血管并发症发生和识别高危患者的能力。神经网络模型优于逻辑回归和随机森林模型，提示其可能帮助临床医生早期识别高危患者，从而减轻患者痛苦并改善预后。
