Revascularization outcome prediction for a direct aspiration-first pass technique (Adapt) from pre-treatment imaging and machine learning

Abstract

A direct aspiration-first pass technique (ADAPT) has recently gained popularity for the treatment of large vessel ischemic stroke. Here, we sought to create a machine learning-based model that uses pre-treatment imaging metrics to predict successful outcomes for ADAPT in middle cerebral artery (MCA) stroke cases. In 119 MCA strokes treated by ADAPT, we calculated four imaging parameters—clot length, perviousness, distance from the internal carotid artery (ICA) and angle of interaction (AOI) between clot/catheter. We determined treatment success by first pass effect (FPE), and performed univariate analyses. We further built and validated multivariate machine learning models in a random train-test split (75%:25%) of our data. To test model stability, we repeated the machine learning procedure over 100 randomizations, and reported the average performances. Our results show that perviousness (p = 0.002) and AOI (p = 0.031) were significantly higher and clot length (p = 0.007) was significantly lower in ADAPT cases with FPE. A logistic regression model achieved the highest accuracy (74.2%) in the testing cohort, with an AUC = 0.769. The models had similar performance over the 100 train-test randomizations (average testing AUC = 0.768 ± 0.026). This study provides feasibility of multivariate imaging-based predictors for stroke treatment outcome. Such models may help operators select the most adequate thrombectomy approach.