Abstract
Background: We developed a potential useful alternative prediction model based on the support vector machine (SAM) algorithm to predict the risk of preoperative deep vein thrombosis (DVT) in non-fractured patients awaiting total joint arthroplasty (TJA). Methods: From March 2015 to August 2020, a retrospective review of the preoperative ultrasound examination findings of lower extremity venous vessels was performed on non-fractured patients of 369 elective TJA. Based on the ultrasound examination findings of preoperative lower extremely venous vessels, these patients were divided into two groups: the DVT group and the Non-DVT group. We collected the clinical, imaging, and laboratory findings from an electronic medical record system. These variables were imported into univariate, multivariate and logistic regression analysis to identify the risk factor for preoperative DVT. According to published literature and clinical experience, a series of variables were selected to construct a prediction model based on the SVM machine learning algorithm. Results: Among the 369 patients, preoperative DVT was observed in 21 patients (5.7%). The Multivariate regression analysis showed the following 5 independent factors associated with preoperative DVT: preoperative fibrinogen odds ratio [OR] = 7.306), age (OR = 1.133), history of hypertension (OR = 3.848), preoperative hematocrit (OR = 0.315), and D-dimer (OR = 2.032). The SVM model achieved a maximum and average area under the receiver operating characteristic curve (AUC) of 0.94 and 0.77 in the 10-fold cross-validation. Meanwhile, the accuracy, precision, and recall of the model were 0.98, 0.92, and 0.93, respectively. Additionally, the confusion matrix showed the classification results of the discriminant analysis.Conclusions: SVM machine modeling is a promising method for the prediction of the risk of DVT in non-fractured patients awaiting TJA. However, future external validation is needed.
Thromboprophylaxis for Deep Vein Thrombosis and Pulmonary Embolism after Total Joint Arthroplasty in a Low Incidence Population
