Predicting outcome in acute stroke with large vessel occlusion—application and validation of MR PREDICTS in the ESCAPE-NA1 population
Bibliographic record
Abstract
BackgroundPredicting outcome after endovascular treatment for acute ischemic stroke is challenging. We aim to investigate differences between predicted and observed outcomes in patients with acute ischemic stroke treated with endovascular treatment and to evaluate the performance of a validated outcome prediction score.Patients and methodsMR PREDICTS is an outcome prediction tool based on a logistic regression model designed to predict the treatment benefit of endovascular treatment based on the MR CLEAN and HERMES populations. ESCAPE-NA1 is a randomized trial of nerinetide vs. placebo in patients with acute stroke and large vessel occlusion. We applied MR PREDICTS to patients in the control arm of ESCAPE-NA1. Model performance was assessed by calculating its discriminative ability and calibration.ResultsOverall, 556/1105 patients (50.3%) in the ESCAPE-NA1-trial were randomized to the control arm, 435/556 (78.2%) were treated within 6 h of symptom onset. Good outcome (modified Rankin scale 0-2) at 3 months was achieved in 275/435 patients (63.2%), the predicted probability of good outcome was 52.5%. Baseline characteristics were similar in the study and model derivation cohort except for age (ESCAPE-NA1: mean: 70 y vs. HERMES: 66 y), hypertension (72% vs. 57%), and collaterals (good collaterals, 15% vs. 44%). Compared to HERMES we observed higher rates of successful reperfusion (TICI 2b-3, ESCAPE-NA1: 87% vs. HERMES: 71%) and faster times from symptom onset to reperfusion (median: 201 min vs. 286 min). Model performance was good, indicated by a c-statistic of 0.76 (95%confidence interval: 0.71-0.81).ConclusionOutcome-prediction using models created from HERMES data, based on information available in the emergency department underestimated the actual outcome in patients with acute ischemic stroke and large vessel occlusion receiving endovascular treatment despite overall good model performance, which might be explained by differences in quality of and time to reperfusion. These findings underline the importance of timely and successful reperfusion for functional outcomes in acute stroke patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".