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Abstract WP9: Improving Selection of Patients for Endovascular Treatment of Acute Ischemic Stroke:External Validation of a Clinical Decision Tool in Data from the Hermes Collaboration. Stroke 2019. [DOI: 10.1161/str.50.suppl_1.wp9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background:
Benefit of endovascular treatment (EVT) varies between individual patients with acute ischemic stroke. The MR PREDICTS decision tool, previously developed in the MR CLEAN trial, predicts outcome with and without EVT based on baseline patient characteristics and imaging characteristics (www.mrpredicts.com). We externally validated this model with data from recent EVT trials.
Methods:
Individual patient data was derived from the six other randomized controlled trials within the HERMES collaboration (ESCAPE, REVASCAT, SWIFT-PRIME, EXTEND-IA, THRACE and PISTE). Outcome of the ordinal logistic regression model was the modified Rankin Scale (mRS) at 90 days after stroke. Treatment benefit was defined as the difference between the predicted probability of achieving functional independence (mRS score 0-2) with and without EVT. Model performance was evaluated according to discrimination (measured with the c-statistic which ranges from 0.5 to 1) and calibration. Model coefficients were updated after calibration.
Results:
We included 1243 patients (633 assigned to EVT, 610 assigned to control). The c-statistic was 0.67 (95% confidence interval [CI] 0.65-0.69) for the ordinal mRS and 0.73 (95% CI 0.70-0.76) for functional independence, similar to previous performance. Outcomes were systematically better than predicted (calibration slope 0.89 and intercept 0.52). The observed probability of functional independence was higher than predicted for both treated patients (54% vs 40%) and controls (35% vs 26%), but the observed treatment benefit was similar (19% and 14%). Figure 1 shows a screenshot of the decision tool for use in clinical practice.
Conclusion:
Our model predicted outcome in a large representative trial population with discriminative value comparable to other well-known prediction tools. The decision tool can be used to support clinical decision making in ischemic stroke by selection of patients for EVT.
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