Related Experiment Video
Updated: Jan 28, 2026

Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
Published on: January 18, 2018
Evaluating Outcome Prediction Models in Endovascular Stroke Treatment Using Baseline, Treatment, and Posttreatment
Johanna M Ospel1,2,3, Aravind Ganesh3, Manon Kappelhof4
1Department of Clinical Neurosciences University of Calgary Calgary Alberta Canada.
Background:
Statistical models to predict outcomes after endovascular therapy for acute ischemic stroke often incorporate baseline (pretreatment) variables only. We assessed the performance of stroke outcome prediction models for endovascular therapy in stroke in an iterative fashion using baseline, treatment-related, and posttreatment variables.
Methods:
Data from the ESCAPE-NA1 (Safety and Efficacy of Nerinetide [NA-1] in Subjects Undergoing Endovascular Thrombectomy for Stroke) trial were used to build 4 outcome prediction models using multivariable logistic regression: model 1 included baseline variables available before treatment decision making, model 2 included additional treatment-related variables, model 3 additional posttreatment variables that become available early (within 24-48 hours), and model 4 later (beyond 48 hours) after endovascular therapy. The primary outcome was functional independence (90-day Modified Rankin Scale score 0-2). Model performance was compared using the area under the receiver operating characteristic curve (AUC). Shapley values were used to determine marginal contributions of variables to outcome variance in the regression models.
Results:
Among 1105 patients, functional independence was achieved by 666 (60.3%). When using baseline variables only (model 1), the AUC was 0.74 (95% CI, 0.71-0.77); this iteratively improved when treatment and posttreatment variables were added to the models (model 2: AUC, 0.77; 95% CI, 0.74-0.80; model 3: AUC, 0.80; 95% CI, 0.77-0.83; model 4: AUC, 0.82; 95% CI, 0.79-0.85). With baseline variables alone, 26% of patients who achieved functional independence were erroneously classified as not achieving functional independence. Even with the most comprehensive model, 19.8% of patients were misclassified as such. Patient age contributed most to outcome variance (Shapley value, 0.28), followed by severe adverse events including pneumonia (0.16) and intracranial hemorrhage at 24-hours imaging (0.13).
Conclusions:
A substantial contribution to outcomes after endovascular therapy comes from factors unrelated to currently collected baseline patient variables. One-fifth of patients achieving functional independence were misclassified as not achieving independence, even with the most comprehensive model. Our findings suggest that the achievable accuracy of current outcome prediction models is limited, and caution should be used when applying them in clinical practice.
More Related Videos
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
06:54A Model for Encephalomyosynangiosis Treatment after Middle Cerebral Artery Occlusion-Induced Stroke in Mice
Published on: June 22, 2022
Related Concept Videos
Predicting Reaction Outcomes
Parkinson's Disease: Treatment
Parkinson's Disease is primarily a result of the loss of dopaminergic neurons in the substantia nigra pars compacta. The cornerstone of...
Alzheimer's Disease: Treatment
Open Angle Glaucoma: Treatment
Drugs such as carbonic anhydrase inhibitors, α2- and...
Treatment Resistant Cancers
Myasthenia Gravis: Overview and Treatment
These antibodies interfere with the function of the nicotinic receptors in three ways: by binding to the receptor and disrupting acetylcholine binding; by causing cross-linking of receptors which...