A predictive model for vascular complications of free flap transplantation based on machine learning

Abstract

Abstract Objective: Exploring the risk factors for vascular complications after free flap transplantation and establishing a clinical auxiliary assessment tool for vascular complications in patients undergoing free flap transplantation based on machine learning methods. Methods:Collecting patients who underwent free flap transplantation at a tertiary hospital in Guizhou Province from January 1, 2019 to December 31, 2021, using synthetic minority oversampling technique to oversample the training set, and constructing Logistic regression model, random forest model and neural network model three machine learning models and verifying them using the sampled data. Using the test set to evaluate the performance of the model through the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity indices. Results: A total of 570 patients who underwent free flap transplantation were included in this study, of which 46 patients developed postoperative vascular complications. The neural network model performed best in the test set, with an AUC of 0.828. Multivariate logistic regression analysis showed that preoperative hemoglobin, preoperative fibrinogen, operation time, smoking history, number of anastomoses, and peripheral vascular injury were statistically significant independent risk factors for vascular complications after free flap transplantation. The top five predictive factors in the neural network were fibrinogen content, operation time, donor site, BMI, and platelet count. Conclusion: The predictive model for vascular complications of free flap transplantation constructed in this study has good predictive ability, which can provide reference for medical personnel to take preventive measures to prevent vascular complications in high-risk patients undergoing free flap transplantation.