Meinel, Thomas; Lerch, Christine; Fischer, Urs; Beyeler, Morin; Mujanovic, Adnan; Kurmann, Christoph; Siepen, Bernhard; Scutelnic, Adrian; Müller, Madlaine; Goeldlin, Martina; Belachew, Nebiyat Filate; Dobrocky, Tomas; Gralla, Jan; Seiffge, David; Jung, Simon; Arnold, Marcel; Wiest, Roland; Meier, Raphael; Kaesmacher, Johannes (2022). Multivariable Prediction Model for Futile Recanalization Therapies in Patients With Acute Ischemic Stroke. Neurology, 99(10), e1009-e1018. American Academy of Neurology 10.1212/WNL.0000000000200815
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BACKGROUND AND OBJECTIVES
Very poor outcome despite intravenous thrombolysis (IVT) and mechanical thrombectomy (MT) occurs in about 1 of 4 patients with ischemic stroke and is associated with a high logistic and economic burden. We aimed to develop and validate a multivariable prognostic model to identify futile recanalization therapies (FRT) in patients undergoing those therapies.
MATERIALS AND METHODS
Patients from a prospectively collected observational registry of a single academic stroke center treated with MT and/or IVT were included. The dataset was split into a training (N=1808, 80%) and internal validation (N=453, 20%) cohort. We used gradient boosted decision tree machine-learning models after k-NN imputation of 32 variables available at admission to predict FRT defined as modified Rankin-Scale (mRS) 5-6 at 3 months. We report feature importance, ability for discrimination, calibration and decision curve analysis.
RESULTS
2261 patients with a median (IQR) age 75 years (64-83), 46% female, median NIHSS 9 (4-17), 34% IVT alone, 41% MT alone, 25% bridging were included. Overall 539 (24%) had FRT, more often in MT alone (34%) as compared to IVT alone (11%). Feature importance identified clinical variables (stroke severity, age, active cancer, prestroke disability), laboratory values (glucose, CRP, creatinine), imaging biomarkers (white matter hyperintensities) and onset-to-admission time as the most important predictors. The final model was discriminatory for predicting 3-month FRT (AUC 0.87, 95% CI 0.87-0.88) and had good calibration (Brier 0.12, 0.11-0.12). Overall performance was moderate (F1-score 0.63 ± 0.004) and decision curve analyses suggested higher mean net benefit at lower thresholds of treatment (up to 0.8).
CONCLUSIONS
This FRT prediction model can help inform shared decision making and identify the most relevant features in the emergency setting. While it might be particularly useful in low resource healthcare settings, incorporation of further multifaceted variables is necessary to further increase the predictive performance.
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