# Hyperuricemia-Informed Survival Machine-Learning Prediction of Post-Thrombotic Syndrome After Unprovoked DVT: A Dual-Center Prospective Study

## TL;DR

This study uses machine learning to predict post-thrombotic syndrome after unprovoked DVT, finding that hyperuricemia improves model accuracy.

## Contribution

The study introduces hyperuricemia as a novel predictor in survival machine-learning models for post-thrombotic syndrome.

## Key findings

- GBM and RSF models showed high 9-month AUCs (0.992 and 0.982) in training data.
- RSF performed best in the test set with an AUC of 0.948.
- Hyperuricemia improved model calibration and risk group separation.

## Abstract

Background/Objectives: Post-thrombotic syndrome (PTS) following unprovoked deep vein thrombosis (DVT) lacks readily available, calibrated risk estimates at defined follow-up horizons. Building on signals that thrombus burden, care processes, and a form of metabolic–inflammatory tone influence outcomes, we prospectively evaluated survival machine-learning models, explicitly including hyperuricemia while excluding what we consider major inflammatory confounders. Methods: Adults with first-episode unprovoked lower-extremity DVT were enrolled at two centers (July 2024–September 2025). PTS (Villalta) was assessed at 3, 6, 9, and 12 months. The cohort was split 70/30 into training and test sets. Eight learners (RSF, GBM, LASSO + Cox, CoxBoost, survivalsvm, XGBoost-Cox, superpc, and plsRcox) were tuned using 10-fold cross-validation in training and once evaluated in the independent test set. Performance metrics included all time-dependent AUCs, fixed-time ROC AUCs with bootstrap 95% CIs, C-index, various forms of calibration, decision-curve analysis, and simple Kaplan–Meier risk group separation. Results: 193 patients were analyzed (PTS in 64%). High 9-month AUCs were seen in training: GBM (0.992) and RSF (0.982) being the strongest; by 12 months, both remained near constant. Test set performance followed a similar pattern, with RSF again favored (AUC 0.948) and XGBoost/GBM close behind. Calibration was satisfactory, net benefit from decision curves positive, and to a large extent, risk groups were separated as expected. Conclusions: Survival machine-learning models, at least in this dual-center prospective cohort, produced a clinically useful risk of PTS. Hyperuricemia, or any metabolically based signal, is a valuable addition to the “anatomy and care” of DVT. External validation is still required.