Changes in Regional Ventilation-Perfusion Match Following Percutaneous Transluminal Angioplasty for Arteriovenous Graft Thrombosis: A Prospective Observation Pilot Study
Changes in Regional Ventilation-Perfusion Match Following Percutaneous Transluminal Angioplasty for Arteriovenous Graft Thrombosis: A Prospective Observation Pilot Study
Patients with end-stage renal disease (ESRD) often require arteriovenous grafts (AVG) for hemodialysis. AVG thrombosis is a common complication, usually managed by percutaneous transluminal angioplasty (PTA) to restore blood flow. PTA achieves patency by balloon-mediated compression and fragmentation of thrombus. Small thrombus fragments may enter the venous circulation and cause transient pulmonary microembolism, leading to ventilation-perfusion (V/Q) mismatch. This study uses electrical impedance tomography (EIT) to noninvasively monitor short-term changes in regional ventilation and perfusion during and after PTA, exploring the immediate pulmonary physiological consequences of thrombus fragmentation and revascularization in dialysis patients.
Arteriovenous graft (AVG) thrombosis is a major source of vascular access failure in ESRD. PTA restores patency through balloon dilation and mechanical thrombus compression. Minor embolic debris can reach the pulmonary bed, transiently disturbing perfusion distribution and V/Q matching. Because the pulmonary circulation is sensitive to sudden peripheral hemodynamic shifts, the PTA period provides a unique opportunity to observe lung perfusion response dynamically.
This single-center, prospective, observational pilot study will continuously record regional ventilation and perfusion by EIT at six time points: 10 min before PTA, at recanalization, and 10, 20, 30 min after, and at procedure completion. Measured variables include: V/Q matching index (primary), physiological dead space fraction (Vd/Vt), intrapulmonary shunt fraction, SPO₂/FiO₂ ratio, and hemodynamic data (balloon pressure, recanalization time, blood flow recovery). Results will be analyzed using repeated-measures ANOVA or mixed-effects modeling.
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