Feasibility study of Syngo iFlow in predicting hemodynamic improvement post-endovascular procedure in peripheral artery disease.

ABSTRACT

OBJECTIVE: This study endeavors to examine the feasibility of predicting the clinical outcomes of patients suffering from peripheral artery disease (PAD) who undergo endovascular intervention, by employing the Syngo 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 (ROI) 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, multivariate logistic regression and the C-statistic were utilized to assess the predictive value of distinct parameters for clinical success. RESULTS: Endovascular procedure 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, multivariate logistic regression demonstrated the statistical significance of ankle joint TTP in post-procedure iFlow images of the endovascular procedure for clinical success evaluation (OR 7.21; 95% CI 1.68, 35.21; P = 0.010), with a C-statistic of 0.612. CONCLUSION: Syngo iFlow color-encoded imagery holds practical value in assessing the technical success of post-endovascular procedures, offering comprehensive lower limb arterial perfusion visualization. Its quantifiable parameters exhibit promising potential for prognosticating clinical success.