A Digital Twin Strategy to Predict Thrombotic Recurrence in Antiphospholipid Syndrome Patients Treated with Direct Oral Anticoagulants vs. Vitamin K Antagonists Using Data from Real-World Populations

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Background/Objectives: The role of direct oral anticoagulants (DOACs) vs. vitamin K antagonists (VKAs) in preventing recurrent thrombosis in patients with antiphospholipid syndrome (APS) remains uncertain. Using real-world data, we aimed to evaluate the effectiveness and internal validity of a digital twin (DT) approach for modeling thrombotic recurrence risk in APS patients treated with DOACs or VKAs. Methods: We conducted a multicenter observational study that included thrombotic APS patients treated with DOACs or VKAs. Clinical data were used to generate DT via conditional generative adversarial networks (CGANs), incorporating a directed acyclic graph (DAG) to preserve causal relationships. Validation metrics included absolute standardized mean differences (ASMD), mean ASMD (MASMD), and Spearman correlation matrices to assess structural fidelity. Treatment effects were estimated in a CGAN-conditioned cohort matched on key covariates. Results: Eighty-nine thrombotic APS patients were included: 70 (78.7%) received VKAs and 19 (21.3%) received DOACs. Thrombotic recurrences occurred in 5 DOAC patients (26.3%) and 17 AVK patients (24.3%). The CGAN-generated synthetic cohort closely mirrored the original data (MASMD = 0.073 ± 0.041), with 85.4% of pairwise correlations differing by <0.1 in absolute value. In the conditioned DT cohort, predicted recurrence was 24.2% for DOACs and 19.9% for VKAs. Recurrence risk increased with antibody burden, reaching 41.3% in triple-positive patients and 46.8% in those with index arterial thrombosis treated with DOACs. Conclusions: DT technology accurately replicated the clinical structure of APS patients, supporting its application for simulating counterfactual scenarios and estimating individualized treatment effects.

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