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.

Posterior Circulation Endovascular Thrombectomy for Large Vessels Occlusion in Patients Presenting with NIHSS Score ≤ 10

“…Nonetheless, first-pass complete reperfusion should be the goal of every MT procedure [31], since it is linked to a better clinical outcome, a lower mortality, and fewer procedural adverse events. This is probably related to the faster procedure time and to a minor risk of endothelial vascular trauma and its resulting complications [32][33][34][35][36].…”

Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis

“…The mean or median ages of the study participants ranged from 64.0 to 86.0 years, and the proportion of male participants ranged from 35.0 to 65.9%. Only one US study ( 24 ) specifically described the self-reported ethnicity of the patients (63.0–69.0% European ancestry); the other studies reported the place of patient recruitment [USA: 1 ( 32 ); Europe: 10 ( 22 , 23 , 26 – 29 , 31 , 33 – 35 ); Asia: 4 ( 25 , 30 , 36 , 37 )]. The training sample sizes ranged widely, from 109 to 1,401.…”

“…Tree models ( 22 , 24 , 31 ), random forests ( 23 , 26 , 27 ), and support vector machines ( 28 , 30 , 33 ) were each proposed by three studies, regularized logistic regression by two studies ( 25 , 32 ), and artificial neural networks by one study ( 29 ). To accommodate missing values, two studies used multiple imputation ( 23 , 29 ) and one used singular imputation ( 31 ), while other studies excluded participants with missing data in either predictive or outcome variables (complete-case analysis) ( 22 , 24 – 28 , 30 , 32 , 33 ). The number of predictive variables used for model construction varied from 4 ( 32 ) to 53 ( 23 ).…”

“…To accommodate missing values, two studies used multiple imputation ( 23 , 29 ) and one used singular imputation ( 31 ), while other studies excluded participants with missing data in either predictive or outcome variables (complete-case analysis) ( 22 , 24 – 28 , 30 , 32 , 33 ). The number of predictive variables used for model construction varied from 4 ( 32 ) to 53 ( 23 ). The National Institutes of Health Stroke Scale and age were commonly ranked as the important predictors.…”

“…Three ML-based studies ( 23 , 29 , 31 ) and one DL-based study ( 34 ) were considered at low risk of bias in all domains ( Supplementary Table S5 ). The remaining studies were at high risk of bias in at least one domain ( 22 , 24 – 28 , 30 , 32 , 33 , 35 – 37 ). Risk of bias mostly occurred in handling missing data.…”