ArticleStroke (Hoboken, N.J.)2021
Evaluating Outcome Prediction Models in Endovascular Stroke Treatment Using Baseline, Treatment, and Posttreatment Variables.
Article in Stroke (Hoboken, N.J.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Association of Brain Atrophy With Functional Outcome and Recovery Trajectories After Thrombectomy: Post Hoc Analysis of the ESCAPE-NA1 Trial.Neurology · 2023Trial
- Do we still need the crystal ball to predict the outcome of acute stroke?European radiology · 2026Article
- A CT imaging-based prediction model of functional outcome and benefit of endovascular thrombectomy for ischemic stroke.European radiology · 2026Article
- Diaschisis as Cerebello-Cortical Loop Dysfunction in Acute Ischemic Stroke: A Network Framework for Outcome Variability.Brain sciences · 2026Review
- A simple risk index predicts endovascular treatment outcomes in acute ischemic stroke: prognostic value of SPAN-100.Frontiers in neurology · 2025Article
- How Do Quantitative Tissue Imaging Outcomes in Acute Ischemic Stroke Relate to Clinical Outcomes?Journal of stroke · 2024Article
- Association of White Matter Disease With Functional Recovery and 90-Day Outcome After EVT: Beyond Chronological Age.Stroke (Hoboken, N.J.) · 2023Article
- A Review on Adjunctive Therapies for Endovascular Treatment in Acute Ischemic Stroke.Journal of neuroendovascular therapy · 2023Review
- Multivariable Prediction Model for Futile Recanalization Therapies in Patients With Acute Ischemic Stroke.Neurology · 2022Article
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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