Fully connected neural network model for predicting postthrombotic syndrome in patients with deep vein thrombosis

Postthrombotic syndrome (PTS) is the most common complication of deep vein thrombosis (DVT) and, despite anticoagulation, affects 20-50% of patients with DVT. Prediction models, primarily based on linear regression, struggle with the complex, nonlinear relationships in clinical data. This study thus aimed to develop and validate a PTS risk prediction model incorporating a fully connected neural network (FCNN) in a large DVT patient dataset to support clinical decision-making.

This retrospective study used data from lower-extremity DVT cases at a single vascular surgery center. An FCNN model for predicting PTS risk was constructed via TensorFlow (Google), with feature selection, model building, and validation phases being completed. The model was evaluated according to metrics of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), as well as fivefold cross-validation.

Out of 212 enrolled patients with DVT, 67 (31.6%) developed PTS within 6 months of the initial DVT diagnosis. The FCNN model predicted PTS with 80.43% accuracy, demonstrating high stability and reliability. On the test set (n=46), the model achieved a sensitivity of 78.6%, a specificity of 78.1%, and an AUC of 0.82.

The FCNN-based PTS risk prediction model is a promising artificial intelligence tool for vascular surgery and can aid in early intervention and treatment decisions in patients with DVT. Future work should include data integration from multiple centers and external validation of the model to enhance its universality and accuracy.