Dataset Information

Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms.

ABSTRACT: Background: Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables. Methods: We included patients from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry, an observational cohort of LVO patients treated with EVT. We applied the following machine learning algorithms: Random Forests, Support Vector Machine, Neural Network, and Super Learner and compared their predictive value with classic logistic regression models using various variable selection methodologies. Outcome variables were good reperfusion (post-mTICI ? 2b) and functional independence (modified Rankin Scale ?2) at 3 months using (1) only baseline variables and (2) baseline and treatment variables. Area under the ROC-curves (AUC) and difference of mean AUC between the models were assessed. Results: We included 1,383 EVT patients, with good reperfusion in 531 (38%) and functional independence in 525 (38%) patients. Machine learning and logistic regression models all performed poorly in predicting good reperfusion (range mean AUC: 0.53-0.57), and moderately in predicting 3-months functional independence (range mean AUC: 0.77-0.79) using only baseline variables. All models performed well in predicting 3-months functional independence using both baseline and treatment variables (range mean AUC: 0.88-0.91) with a negligible difference of mean AUC (0.01; 95%CI: 0.00-0.01) between best performing machine learning algorithm (Random Forests) and best performing logistic regression model (based on prior knowledge). Conclusion: In patients with LVO machine learning algorithms did not outperform logistic regression models in predicting reperfusion and 3-months functional independence after endovascular treatment. For all models at time of admission radiological outcome was more difficult to predict than clinical outcome.

SUBMITTER: van Os HJA

PROVIDER: S-EPMC6167479 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms.

van Os Hendrikus J A HJA Ramos Lucas A LA Hilbert Adam A van Leeuwen Matthijs M van Walderveen Marianne A A MAA Kruyt Nyika D ND Dippel Diederik W J DWJ Steyerberg Ewout W EW van der Schaaf Irene C IC Lingsma Hester F HF Schonewille Wouter J WJ Majoie Charles B L M CBLM Olabarriaga Silvia D SD Zwinderman Koos H KH Venema Esmee E Marquering Henk A HA Wermer Marieke J H MJH

Frontiers in neurology 20180925

<b>Background:</b> Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables. <b>Methods:</b> We included patients from the Multicenter Randomized Clinical Trial of Endova ...[more]

PMID: 30319525

Similar Datasets

Project description:Background and Purpose- We determined the effect of sex on outcome after endovascular stroke thrombectomy in acute ischemic stroke, including lifelong disability outcomes. Methods- We analyzed patients treated with the Solitaire stent retriever in the combined SWIFT (Solitaire FR With the Intention for Thrombectomy), STAR (Solitaire FR Thrombectomy for Acute Revascularization), and SWIFT PRIME (Solitaire FR With the Intention for Thrombectomy as Primary Endovascular Treatment) cohorts. Ordinal and logistic regression were used to examine known factors influencing outcome after endovascular stroke thrombectomy and study the effect of sex on the association between these factors and outcomes, including age and time to reperfusion. Years of optimal life after thrombectomy were defined as disability-adjusted life years and calculated by projecting disability through adjusted poststroke life expectancy by sex. Results- Among 389 patients treated with endovascular stroke thrombectomy, 55% were females, and median National Institutes of Health Stroke Scale was 17 (interquartile range, 8-28). There were no differences between females versus males in presenting deficit severity (National Institutes of Health Stroke Scale score, 17 versus 17, P=0.21), occlusion location (69% versus 64% M1, P=0.62), presenting infarct extent (Alberta Stroke Program Early CT Score 8 versus 8, P=0.24), rate of substantial reperfusion (Thrombolysis in Cerebral Infarction 2b/3, 87% versus 83%, P=0.37), onset to reperfusion time (294 versus 302 minutes, P=0.46). Despite older ages (69 versus 64, P<0.001) and higher rate of atrial fibrillation (45% versus 30%, P=0.002) for females compared with males, adjusted rates of functional independence at 90 days were similar (odds ratio, 1.0; 95% CI, 0.6-1.6). After adjusting for age at presentation and stroke severity, females had more years of optimal life (disability-adjusted life year) after endovascular stroke thrombectomy, 10.6 versus 8.5 years (P<0.001). Conclusions- Despite greater age and higher rate of atrial fibrillation, females experienced comparable functional outcomes and greater years of optimal life after intervention compared with males.

Project description: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.

Dataset Information

Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms.

Publications

Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets