Multivariable Prediction Model for Futile Recanalization Therapies in Patients With Acute Ischemic Stroke

Abstract

Background and objectives: Very poor outcome despite IV thrombolysis (IVT) and mechanical thrombectomy (MT) occurs in approximately 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 (FRTs) in patients undergoing those therapies.

Methods: Patients from a prospectively collected observational registry of a single academic stroke center treated with MT and/or IVT were included. The data set was split into a training (N = 1,808, 80%) and internal validation (N = 453, 20%) cohort. We used gradient boosted decision tree machine learning models after k-nearest neighbor imputation of 32 variables available at admission to predict FRT defined as modified Rankin scale 5-6 at 3 months. We report feature importance, ability for discrimination, calibration, and decision curve analysis.

Results: A total of 2,261 patients with a median (interquartile range) age of 75 years (64-83 years), 46% female, median NIH Stroke Scale 9 (4-17), 34% IVT alone, 41% MT alone, and 25% bridging were included. Overall, 539 (24%) had FRT, more often in MT alone (34%) as compared with IVT alone (11%). Feature importance identified clinical variables (stroke severity, age, active cancer, prestroke disability), laboratory values (glucose, C-reactive protein, 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 (area under the curve 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. Although it might be particularly useful in low resource healthcare settings, incorporation of further multifaceted variables is necessary to further increase the predictive performance.

Figure 1. Intended Use of the Model in a Stroke Workflow

IVT = IV thrombolysis; ML = machine learning; mRS = modified Rankin scale; MT = mechanical thrombectomy. The ML model outputs a probability (risk score) for mRS 5–6 based on variables available ahead of recanalization therapies. This information is provided to the treating physicians after selection of patient for recanalization and could serve as a marker for futile recanalization. Workflow derived from Meier et al. with permission.

Figure 2. Shapley Values Feature Importance

Computed for the XGBoost (mean values of 10 feature permutations and 20 random initializations). (A) All patients, (B) only patients with detectable vessel occlusion, and (C) only patients without detectable vessel occlusion. See the Supplement (links.lww.com/WNL/C129) for full model and features. Frame indicates the 10 most important variables. NIHSS = NIH Stroke Scale.

Figure 3. Decision Curve Analysis for XGBClassifier

The blue line indicates the mean over 20 random initializations. The rug plot shows the samples used for computation of the decision curve. No risk denotes the trivial strategy of always predicting mRS 0–4, and All risk denotes the strategy of always predicting mRS 5–6. mRS = modified Rankin scale.