Integrating vascular access surveillance with clinical monitoring for stenosis prediction

Background: Arteriovenous fistula and arteriovenous graft are the most common types of vascular access for dialysis; stenosis and thrombosis are major complications leading to access failure and to an incresed risk of mortality. The aim of the present study was to assess the results of integrating strict vascular access blood flow surveillance with routine clinical monitoring for predicting vascular access stenosis in chronic hemodialysis patients.

Methods: In this retrospective study, chronic dialysis patients with arteriovenous fistula or arteriovenous graft were included from a setting in which all patients underwent quarterly blood flow surveillance in 2017. The results of blood flow surveillance were confirmed by thorough physical examination. Predictive performance of blood flow surveillance models in detecting stenosis in patients with arteriovenous fistula or arteriovenous graft was evaluated. The predictive performance of the quarterly blood flow surveillance model was described by confusion matrix. Differences in accuracy, positive predictive value (PPV), and negative predictive value (NPV) between blood flow surveillance models with distinct blood flow thresholds were evaluated.

Results: Of 397 included patients, 336 had an arteriovenous fistula and 61 had an arteriovenous graft. In 2017, 106 percutaneous transluminal angioplasty procedures were performed in patients with an arteriovenous fistula, and 63 in patients with an arteriovenous graft. The results revealed similar predictive performance of surveillance models using an absolute blood flow threshold of < 500 or < 400 mL/min in predicting stenosis in patients with arteriovenous fistula. Blood flow surveillance models for patients with an arteriovenous fistula had significantly higher accuracy than those for patients with an arteriovenous graft. Furthermore, the use of a relative threshold, defined as blood flow < 1000 mL/min and a 25% decline in blood flow, did not affect the predictive performance of blood flow surveillance models.

Conclusion: Blood flow surveillance models using thresholds of < 400 and < 600 mL/min, followed by thorough physical examination, showed an accuracy of 91.54% and 72.15% in predicting stenosis in patients with arteriovenous fistula and arteriovenous graft, respectively. These two blood flow surveillance models may be integrated with routine clinical monitoring to improve early detection and treatment of stenosis in hemodialysis patients.