# Preoperative deep vein thrombosis in tibial plateau fractures: development and internal validation of an interpretable multivariable machine-learning diagnostic model

## TL;DR

A machine learning model was developed to predict preoperative deep vein thrombosis in tibial plateau fracture patients using routine clinical data.

## Contribution

An interpretable XGBoost model was developed and validated for preoperative DVT risk prediction in TPF patients.

## Key findings

- The model achieved an AUROC of 0.840 with good calibration and clinical utility.
- D-dimer and age were identified as the most influential predictors of preoperative DVT.
- The model showed net clinical benefit but requires external validation for broader use.

## Abstract

Preoperative deep vein thrombosis (DVT) is common in tibial plateau fractures (TPF), yet few tools target this window with calibration and clinical utility reporting.

Single-center retrospective cohort (2019–2024) of adults undergoing surgery for isolated TPF. Outcome: duplex ultrasonography–confirmed DVT before initiation of therapeutic anticoagulation. Candidate predictors included demographics; injury features (Schatzker type/side, injury-to-surgery interval); and coagulation, inflammatory, and nutritional–immune indices. Features were selected by the intersection of LASSO and Boruta. Data were split 7:3 into training/validation; seven algorithms were tuned with 5-fold cross-validation. Validation assessed AUROC (95% confidence interval), Brier score, calibration, and decision-curve analysis (DCA). Model interpretability was assessed using SHAP (Shapley Additive Explanations).

Among 894 patients, 299 (33.4%) had preoperative DVT. Nine predictors were retained: D-dimer, age, erythrocyte sedimentation rate, prognostic nutritional index, C-reactive protein, lymphocyte count, Schatzker type, neutrophil count, and smoking. XGBoost performed best (AUROC 0.840, 95% confidence interval 0.790–0.884; accuracy 0.787; sensitivity 0.640; specificity 0.860; F1 score 0.667; Brier 0.149) and provided net clinical benefit on DCA. Tree-ensemble models showed training–validation performance gaps, indicating overfitting. SHAP ranked D-dimer and age as dominant with non-linear effects; higher C-reactive protein and erythrocyte sedimentation rate, lower prognostic nutritional index, advanced Schatzker types, and smoking increased risk.

An interpretable XGBoost model based on routine preoperative variables identifies TPF patients at high risk of preoperative DVT and may guide ultrasound triage and perioperative management. External (multicenter and temporal) validation with recalibration and prospective impact assessment are required.