Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning

Abstract

Background and Purpose: This study assessed the predictive performance and relative importance of clinical, multimodal imaging, and angiographic characteristics for predicting the clinical outcome of endovascular treatment for acute ischemic stroke. Methods: A consecutive series of 246 patients with acute ischemic stroke and large vessel occlusion in the anterior circulation who underwent endovascular treatment between April 2014 and January 2018 was analyzed. Clinical, conventional imaging (electronic Alberta Stroke Program Early CT Score, acute ischemic volume, site of vessel occlusion, and collateral score), and advanced imaging characteristics (CT-perfusion with quantification of ischemic penumbra and infarct core volumes) before treatment as well as angiographic (interval groin puncture-recanalization, modified Thrombolysis in Cerebral Infarction score) and postinterventional clinical (National Institutes of Health Stroke Scale score after 24 hours) and imaging characteristics (electronic Alberta Stroke Program Early CT Score, final infarction volume after 18–36 hours) were assessed. The modified Rankin Scale (mRS) score at 90 days (mRS-90) was used to measure patient outcome (favorable outcome: mRS-90 ≤2 versus unfavorable outcome: mRS-90 >2). Machine-learning with gradient boosting classifiers was used to assess the performance and relative importance of the extracted characteristics for predicting mRS-90. Results: Baseline clinical and conventional imaging characteristics predicted mRS-90 with an area under the receiver operating characteristics curve of 0.740 (95% CI, 0.733–0.747) and an accuracy of 0.711 (95% CI, 0.705–0.717). Advanced imaging with CT-perfusion did not improved the predictive performance (area under the receiver operating characteristics curve, 0.747 [95% CI, 0.740–0.755]; accuracy, 0.720 [95% CI, 0.714–0.727]; P =0.150). Further inclusion of angiographic and postinterventional characteristics significantly improved the predictive performance (area under the receiver operating characteristics curve, 0.856 [95% CI, 0.850–0.861]; accuracy, 0.804 [95% CI, 0.799–0.810]; P <0.001). The most important parameters for predicting mRS 90 were National Institutes of Health Stroke Scale score after 24 hours (importance =100%), premorbid mRS score (importance =44%) and final infarction volume on postinterventional CT after 18 to 36 hours (importance =32%). Conclusions: Integrative assessment of clinical, multimodal imaging, and angiographic characteristics with machine-learning allowed to accurately predict the clinical outcome following endovascular treatment for acute ischemic stroke. Thereby, premorbid mRS was the most important clinical predictor for mRS-90, and the final infarction volume was the most important imaging predictor, while the extent of hemodynamic impairment on CT-perfusion before treatment had limited importance.

Prediction of Clinical Outcomes in Acute Ischaemic Stroke Patients: A Comparative Study

“…The proposed framework has a design advantage in comparison to the existing prognostic models of stroke outcome. A recent review on predictive models of stroke outcome (18) reports that: (i) the target outcome of the predictive model is usually a categorized version of functional outcome 1 ; (ii) the variables used to model this score include prognostic parameters 2 , stroke risk factors, and baseline stroke severity measured by the NIHSS scale. An obvious limitation is that classification models predicting binarized functional outcome likely ignore the gradation of stroke severity, which is relevant information for stroke prognostication.…”

“…So far, the benefit of using true multi-modal data for stroke outcome prediction has not been investigated comprehensively. One of the few multi-modal predictive models of stroke outcome is described by Brugnara et al ( 2 ). However, clinical assessments at various timepoints are used as input features without addressing the issue of feature collinearity.…”

Stroke Side Identification in the Brain by Retinal Image Analysis

“…Previous studies have revealed the association between retinal characteristics and stroke in many aspects [17][18][19][20][21][22][23], but here we further investigated the association of the brain side with stroke. It might be an effective way to reduce the burden by specifying the treatment according to the side of the stroke.…”

Late Thrombectomy in Clinical Practice

“…The size of the squares is proportional to the number of patients in the subgroup. p ≤ 0.05 was considered significant, Wald CI Wald confidence interval, Chisq Chi-squared test K fore another important clinical factor which should be considered in the decision for or against endovascular treatment [27]. Compared to the randomized trials the overall percentage of patients > 80 years in our cohort was higher (33% in DD and NDNDnP and 41% in NDNDwP vs. 23% and 24% in DAWN and DEFUSE3), pointing out another important factor for patient triage in clinical practice.…”