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.

Cerebral small vessel disease (SVD) is the major cause of intracerebral hemorrhage (ICH). There is no comprehensive, easily applicable classification of ICH subtypes according to the presumed underlying SVD using MRI. We developed an MRI-based classification for SVD-related ICH. We performed a retrospective study in the prospectively collected Swiss Stroke Registry (SSR, 2013-2019) and the Stroke InvestiGation in North And central London (SIGNAL) cohort. Patients with nontraumatic, SVD-related ICH and available MRI within 3 months were classified as Cerebral Amyloid angiopathy (CAA), Deep perforator arteriopathy (DPA), Mixed CAA-DPA, or Undetermined SVD using hemorrhagic and nonhemorrhagic MRI markers (CADMUS classification). The primary outcome was inter-rater reliability using Gwet's AC1. Secondary outcomes were recurrent ICH/ischemic stroke at 3 months according to the CADMUS phenotype. We performed Firth penalized logistic regressions and competing risk analyses. The SSR cohort included 1,180 patients (median age [interquartile range] 73 [62-80] years, baseline NIH Stroke Scale 6 [2-12], 45.6% lobar hematoma, systolic blood pressure on admission 166 [145-185] mm Hg). The CADMUS phenotypes were as follows: mixed CAA-DPA (n = 751 patients, 63.6%), undetermined SVD (n = 203, 17.2%), CAA (n = 154, 13.1%), and DPA (n = 72, 6.3%), with a similar distribution in the SIGNAL cohort (n = 313). Inter-rater reliability was good (Gwet's AC1 for SSR/SIGNAL 0.69/0.74). During follow-up, 56 patients had 57 events (28 ICH, 29 ischemic strokes). Three-month event rates were comparable between the CADMUS phenotypes. CADMUS, a novel MRI-based classification for SVD-associated ICH, is feasible and reproducible and may improve the classification of ICH subtypes in clinical practice and research.

Background We investigated outcomes in patients with intracerebral haemorrhage (ICH) according to prior anticoagulation treatment with Vitamin K antagonists (VKAs), direct oral anticoagulants (DOACs) or no anticoagulation. Methods This is an individual patient data study combining two prospective national stroke registries from Switzerland and Norway (2013–2019). We included all consecutive patients with ICH from both registries. The main outcomes were favourable functional outcome (modified Rankin Scale 0–2) and mortality at 3 months. Results Among 11 349 patients with ICH (mean age 73.6 years; 47.6% women), 1491 (13.1%) were taking VKAs and 1205 (10.6%) DOACs (95.2% factor Xa inhibitors). The median percentage of patients on prior anticoagulation was 23.7 (IQR 22.6–25.1) with VKAs decreasing (from 18.3% to 7.6%) and DOACs increasing (from 3.0% to 18.0%) over time. Prior VKA therapy (n=209 (22.3%); adjusted ORs (aOR), 0.64; 95% CI, 0.49 to 0.84) and prior DOAC therapy (n=184 (25.7%); aOR, 0.64; 95% CI, 0.47 to 0.87) were independently associated with lower odds of favourable outcome compared with patients without anticoagulation (n=2037 (38.8%)). Prior VKA therapy (n=720 (49.4%); aOR, 1.71; 95% CI, 1.41 to 2.08) and prior DOAC therapy (n=460 (39.7%); aOR, 1.28; 95% CI, 1.02 to 1.60) were independently associated with higher odds of mortality compared with patients without anticoagulation (n=2512 (30.2%)). Conclusions The spectrum of anticoagulation-associated ICH changed over time. Compared with patients without prior anticoagulation, prior VKA treatment and prior DOAC treatment were independently associated with lower odds of favourable outcome and higher odds of mortality at 3 months. Specific reversal agents unavailable during the study period might improve outcomes of DOAC-associated ICH in the future.

Background: Muscle MRI is of increasing importance for neuromuscular patients to detect changes in muscle volume, fat‐infiltration, and edema. We developed a method for semi‐automated segmentation of muscle MRI datasets.Methods: An active contour‐evolution algorithm implemented within the ITK‐SNAP software was used to segment T1‐weighted MRI, and to quantify muscle volumes of neuromuscular patients (n = 65).Results: Semi‐automated compared with manual segmentation was shown to be accurate and time‐efficient. Muscle volumes and ratios of thigh/lower leg volume were lower in myopathy patients than in controls (P < .0001; P < .05). We found a decrease of lower leg muscle volume in neuropathy patients compared with controls (P < .01), which correlated with clinical parameters. In myopathy patients, muscle volume showed a positive correlation with muscle strength (r_left = 0.79, p_left < .0001). Muscle volumes were independent of body mass index and age.Conclusions: Our method allows for exact and time‐efficient quantification of muscle volumes with possible use as a biomarker in neuromuscular patients.