Comparing Poor and Favorable Outcome Prediction With Machine Learning After Mechanical Thrombectomy in Acute Ischemic Stroke

Abstract

Background and PurposeOutcome prediction after mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and large vessel occlusion (LVO) is commonly performed by focusing on favorable outcome (modified Rankin Scale, mRS 0–2) after 3 months but poor outcome representing severe disability and mortality (mRS 5 and 6) might be of equal importance for clinical decision-making.MethodsWe retrospectively analyzed patients with AIS and LVO undergoing MT from 2009 to 2018. Prognostic variables were grouped in baseline clinical (A), MRI-derived variables including mismatch [apparent diffusion coefficient (ADC) and time-to-maximum (Tmax) lesion volume] (B), and variables reflecting speed and extent of reperfusion (C) [modified treatment in cerebral ischemia (mTICI) score and time from onset to mTICI]. Three different scenarios were analyzed: (1) baseline clinical parameters only, (2) baseline clinical and MRI-derived parameters, and (3) all baseline clinical, imaging-derived, and reperfusion-associated parameters. For each scenario, we assessed prediction for favorable and poor outcome with seven different machine learning algorithms.ResultsIn 210 patients, prediction of favorable outcome was improved after including speed and extent of recanalization [highest area under the curve (AUC) 0.73] compared to using baseline clinical variables only (highest AUC 0.67). Prediction of poor outcome remained stable by using baseline clinical variables only (highest AUC 0.71) and did not improve further by additional variables. Prediction of favorable and poor outcomes was not improved by adding MR-mismatch variables. Most important baseline clinical variables for both outcomes were age, National Institutes of Health Stroke Scale, and premorbid mRS.ConclusionsOur results suggest that a prediction of poor outcome after AIS and MT could be made based on clinical baseline variables only. Speed and extent of MT did improve prediction for a favorable outcome but is not relevant for poor outcome. An MR mismatch with small ischemic core and larger penumbral tissue showed no predictive importance.

Utilizing imaging parameters for functional outcome prediction in acute ischemic stroke: A machine learning study

“…Similarly, Mutke et al. were able to predict favorable functional outcome (mRS 0‐2) in AIS patients with anterior circulation LVOs who were treated with mechanical thrombectomy with an AUROC of 0.73, once again utilizing the predictors such as age and NIHSS score 38 . Other studies that used conventional predictors to predict the favorable functional outcome (mRS 0‐2) in AIS patients with LVOs had AUROCs of 0.856, 0.86, 0.91, 0.90, and 0.82 18,19,39–41 .…”

Use of Machine Learning in Stroke Rehabilitation: A Narrative Review

“…This model may significantly help doctors diagnose the hemorrhage transformation of patients with acute ischemic stroke. In 2022, Mutke et al [ 49 ] collected MR images and clinical data of 210 patients with acute ischemic stroke and large vessel occlusion for whom mechanical thrombectomy was performed. The data were used to predict favorable and poor outcomes after mechanical thrombectomy.…”

Development and performance assessment of novel machine learning models for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage patients: external validation in MIMIC-IV

“…In addition, although previous studies have suggested that conventional LR can provide a clinical prediction model that is easy to interpret, when conventional LR is used for complex multivariate non-linear relationships, complex transformations are often required owing to low robustness and multicollinearity between variables ( 14 ). Therefore, recent work has highlighted the potential of machine learning (ML) algorithms for stroke-related complications in stroke patients ( 15 , 16 ). Savarraj et al suggested that ML models significantly outperform conventional LR in predicting functional outcomes and has the potential to improve SAH management ( 17 ).…”

“…Savarraj et al suggested that ML models significantly outperform conventional LR in predicting functional outcomes and has the potential to improve SAH management ( 17 ). The ML models possess the potential to outperform conventional linear or logistic regression models due to their exceptional ability in identifying intricate and nonlinear relationships among a multitude of prognostic variables ( 16 ). Thus, in this study, we conducted five ML prediction model, which contains support vector machine (SVM), logistic regression (LR), random forest (RF), multilayer perceptron (MLP), K-nearest neighbor (KNN) and extreme gradient boosting (XGBoost) for the prediction of POP within 30 days in aSAH patients after surgical treatment.…”