Force-Gated Thrombosis (FGT): A Non-Equilibrium Mechanical Theory of Shear-Induced Blood Clot Initiation

Arterial thrombosis is initiated when mechanical forces in flowing blood exceed the activation thresholds of platelets and von Willebrand factor (vWF). Despite extensive experimental characterization of shear-induced platelet aggregation, a unified theoretical framework that maps hemodynamic forcing onto clot nucleation is lacking. Here we present Force-Gated Thrombosis (FGT), a non-equilibrium mechanical theory that treats thrombus formation as a continuous phase transition driven by an effective mechanical forcing Σ = σ + α |∇ σ | + βε , which combines local wall shear stress σ , shear gradient |∇ σ |, and extensional strain rate ε . We introduce a dimensionless Thrombosis Number Θ = (Σ / Σ _c )( P/P ₀ ) ^m ( C/C ₀ ) ⁿ , which incorporates platelet concentration P and coagulation factor concentration C , and governs the transition between stable flow (Θ < 1) and active clot growth (Θ > 1). The thrombus density is represented by a scalar order parameter φ whose dynamics follow a Ginzburg– Landau free energy functional. For a simplified stenosed artery we derive an analytic closed-form thrombosis onset criterion and a critical flow rate , where δ is stenosis severity. Linear stability analysis shows that perturbations grow at rate ω ( k ) = Λ(Θ) − D _φ k ² , becoming unstable when Θ > 1. Near threshold the clot volume fraction scales as φ ∼ (Θ − 1) ^{1 / 2} , a mean-field critical exponent consistent with Ginzburg– Landau theory. Systematic comparison with fifteen published experimental and computational datasets spanning shear rates from 100 to 15,000 s ⁻¹ confirms that FGT correctly predicts the existence, location, and approximate severity of pathological thrombus formation across diverse vascular geometries. The theory provides a quantitative bridge between single-molecule mechanobiology and macroscale clinical thrombosis, and yields experimentally testable predictions distinguishing FGT from purely biochemical models.