DSA-NRP: No-Reflow Prediction from Angiographic Perfusion Dynamics in Stroke EVT

TL;DR

This study presents a machine learning framework that predicts no-reflow in stroke patients immediately after EVT using intra-procedural DSA sequences, outperforming traditional clinical features.

Contribution

First ML approach leveraging intra-procedural DSA sequences for real-time no-reflow prediction in stroke EVT patients.

Findings

ML model achieved AUC of 0.7703, outperforming baseline.

Real-time DSA features encode microvascular hypoperfusion insights.

Method enables immediate prediction, potentially guiding clinical decisions.

Abstract

Following successful large-vessel recanalization via endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), some patients experience a complication known as no-reflow, defined by persistent microvascular hypoperfusion that undermines tissue recovery and worsens clinical outcomes. Although prompt identification is crucial, standard clinical practice relies on perfusion magnetic resonance imaging (MRI) within 24 hours post-procedure, delaying intervention. In this work, we introduce the first-ever machine learning (ML) framework to predict no-reflow immediately after EVT by leveraging previously unexplored intra-procedural digital subtraction angiography (DSA) sequences and clinical variables. Our retrospective analysis included AIS patients treated at UCLA Medical Center (2011-2024) who achieved favorable mTICI scores (2b-3) and underwent pre- and post-procedure MRI. No-reflow was defined as persistent hypoperfusion (Tmax > 6 s) on post-procedural imaging. From DSA sequences (AP and lateral views), we extracted statistical and temporal perfusion features from the target downstream territory to train ML classifiers for predicting no-reflow. Our novel method significantly outperformed a clinical-features baseline(AUC: 0.7703 $\pm$ 0.12 vs. 0.5728 $\pm$ 0.12; accuracy: 0.8125 $\pm$ 0.10 vs. 0.6331 $\pm$ 0.09), demonstrating that real-time DSA perfusion dynamics encode critical insights into microvascular integrity. This approach establishes a foundation for immediate, accurate no-reflow prediction, enabling clinicians to proactively manage high-risk patients without reliance on delayed imaging.