Development and Validation of Prediction Models for Severe Complications After Acute Ischemic Stroke: A Study Based on the Stroke Registry of Northwestern Germany

Background The treatment of stroke has been undergoing rapid changes. As treatment options progress, prediction of those under risk for complications becomes more important. Available models have, however, frequently been built based on data no longer representative of today's care, in particular with respect to acute stroke management. Our aim was to build and validate prediction models for 4 clinically important, severe outcomes after stroke. Methods and Results We used German registry data from 152 710 patients with acute ischemic stroke obtained in 2016 (development) and 2017 (validation). We took into account potential predictors that were available at admission and focused on in-hospital mortality, intracranial mass effect, secondary intracerebral hemorrhage, and deep vein thrombosis as outcomes. Validation cohort prediction and calibration performances were assessed using the following 4 statistical approaches: logistic regression with backward selection, l1-regularized logistic regression, k-nearest neighbor, and gradient boosting classifier. In-hospital mortality and intracranial mass effects could be predicted with high accuracy (both areas under the curve, 0.90 [95% CI, 0.90-0.90]), whereas the areas under the curve for intracerebral hemorrhage (0.80 [95% CI, 0.80-0.80]) and deep vein thrombosis (0.73 [95% CI, 0.73-0.73]) were considerably lower. Stroke severity was the overall most important predictor. Models based on gradient boosting achieved better performances than those based on logistic regression for all outcomes. However, area under the curve estimates differed by a maximum of 0.02. Conclusions We validated prediction models for 4 severe outcomes after acute ischemic stroke based on routinely collected, recent clinical data. Model performance was superior to previously proposed approaches. These predictions may help to identify patients at risk early after stroke and thus facilitate an individualized level of care

Clinical Diffusion Mismatch to Select Pediatric Patients for Embolectomy 6 to 24 Hours After Stroke An Analysis of the Save ChildS Study

Objective To determine whether thrombectomy is safe in children up to 24 hours after onset of symptoms when selected by mismatch between clinical deficit and infarct. Methods A secondary analysis of the Save ChildS Study (January 2000-December 2018) was performed, including all pediatric patients (<18 years) diagnosed with arterial ischemic stroke who underwent endovascular recanalization at 27 European and United States stroke centers. Patients were included if they had a relevant mismatch between clinical deficit and infarct. Results Twenty children with a median age of 10.5 (interquartile range [IQR] 7-14.6) years were included. Of those, 7 were male (35%), and median time from onset to thrombectomy was 9.8 (IQR 7.8-16.2) hours. Neurologic outcome improved from a median Pediatric NIH Stroke Scale score of 12.0 (IQR 8.8-20.3) at admission to 2.0 (IQR 1.2-6.8) at day 7. Median modified Rankin Scale (mRS) score was 1.0 (IQR 0-1.6) at 3 months and 0.0 (IQR 0-1.0) at 24 months. One patient developed transient peri-interventional vasospasm; no other complications were observed. A comparison of the mRS score to the mRS score in the DAWN and DEFUSE 3 trials revealed a higher proportion of good outcomes in the pediatric compared to the adult study population. Conclusions Thrombectomy in pediatric ischemic stroke in an extended time window of up to 24 hours after onset of symptoms seems safe and neurologic outcomes are generally good if patients are selected by a mismatch between clinical deficit and infarct. Classification of Evidence This study provides Class IV evidence that for children with acute ischemic stroke with a mismatch between clinical deficit and infarct size, thrombectomy is safe