Feasibility Study of Sygno iFlow in Predicting Post-Percutaneous Transluminal Angioplasty Hemodynamic Improvement in Peripheral Artery Disease

Abstract

Abstract Objective: The present investigation aims to scrutinize the feasibility of prognosticating clinical outcomes during the percutaneous transluminal angioplasty (PTA) procedures applied to patients afflicted with peripheral artery disease (PAD), utilizing the Sygno iFlow technology. Methods:Retrospectively enrolling 76 patients from December 2021 to May 2023, yielding a total of 77 affected limbs, this study employs clinical outcomes (improvement or otherwise) as the gold standard. Two physicians conducted visual assessments on both DSA and iFlow images to gauge patient improvement and assessed inter-observer consistency for each image modality. The Time to Peak (TTP) of regions of interest (ROIs) at the femoral head, knee joint, and ankle joint was measured. Differences in pre- and post-procedure TTP were juxtaposed, and statistically significant parameter cutoff values were identified via ROC analysis. Employing these cutoffs for TTP classification, multiple logistic regression and the C-statistic were utilized to assess the predictive value of distinct parameters for clinical success. Results: PTA exhibited technical and clinical success rates of 82.58% and 75.32%, respectively. Diagnostic performance of iFlow image visual assessment surpassed that of DSA images. Inter-observer agreement for iFlow and DSA image evaluations was equivalent (κ = 0.48 vs 0.50). Post-classification using cutoff values, multiple logistic regression demonstrated the statistical significance of ankle joint TTP in PTA's post-procedure iFlow images for clinical success evaluation (OR 7.21; 95% CI 1.68, 35.21; P=0.010), with a C-statistic of 0.612. Conclusion: Sygno iFlow color-encoded imagery holds practical value in appraising post-PTA technical success, offering comprehensive lower limb arterial perfusion visualization. Its quantifiable parameters exhibit promising potential for prognosticating clinical success.