[Intracerebral haemorrhage - acute event and chronic disease]. / Die intrazerebrale Hirnblutung: Akutes Ereignis und chronische Erkrankung.

Intracerebral haemorrhage - acute event and chronic disease Abstract. Intracerebral hemorrhage accounts for 10-15% of all strokes and approximately 1'500-2'000 patients per year in Switzerland. Acute treatment by multi-disciplinary experts at certified stroke units and stroke centers is important to provide optimal care. A simple ABC-care bundle (revert anticoagulation, control blood pressure, inform neurosurgeon) decreases poor outcome. Despite a high mortality, one third of patients are functionally independent after intracerebral hemorrhage contradicting widespread pessimism. About 80% of all intracerebral hemorrhage are attributable to different types of cerebral small vessel disease. Relative and absolute risks of recurrent hemorrhage and ischemic stroke differ significantly. Patients with intracerebral hemorrhage are vascular high-risk patients with chronic cerebrovascular disease. Long-term outpatient management should include neurovascular specialists to deal with important decisions (blood pressure management, antithrombotic therapy including anticoagulation, specialized neurorehabilitation to improve neurocognitive deficits, therapy of possible complications such as epilepsy) to provide optimal and individual care to patients. Currently ongoing randomized controlled trials will provide important results in the next years further improving treatment of intracerebral hemorrhage.

Semi-automated volumetry of MRI serves as a biomarker in neuromuscular patients.

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 (rleft = 0.79, pleft < .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.

Domain-specific data augmentation for segmenting MR images of fatty infiltrated human thighs with neural networks.

BACKGROUND: Fat-fraction has been established as a relevant marker for the assessment and diagnosis of neuromuscular diseases. For computing this metric, segmentation of muscle tissue in MR images is a first crucial step. PURPOSE: To tackle the high degree of variability in combination with the high annotation effort for training supervised segmentation models (such as fully convolutional neural networks). STUDY TYPE: Prospective. SUBJECTS: In all, 41 patients consisting of 20 patients showing fatty infiltration and 21 healthy subjects. Field Strength/Sequence: The T1 -weighted MR-pulse sequences were acquired on a 1.5T scanner. ASSESSMENT: To increase performance with limited training data, we propose a domain-specific technique for simulating fatty infiltrations (i.e., texture augmentation) in nonaffected subjects' MR images in combination with shape augmentation. For simulating the fatty infiltrations, we make use of an architecture comprising several competing networks (generative adversarial networks) that facilitate a realistic artificial conversion between healthy and infiltrated MR images. Finally, we assess the segmentation accuracy (Dice similarity coefficient). STATISTICAL TESTS: A Wilcoxon signed rank test was performed to assess whether differences in segmentation accuracy are significant. RESULTS: The mean Dice similarity coefficients significantly increase from 0.84-0.88 (P < 0.01) using data augmentation if training is performed with mixed data and from 0.59-0.87 (P < 0.001) if training is conducted with healthy subjects only. DATA CONCLUSION: Domain-specific data adaptation is highly suitable for facilitating neural network-based segmentation of thighs with feasible manual effort for creating training data. The results even suggest an approach completely bypassing manual annotations. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 3.

Andexanet alfa versus non-specific treatments for intracerebral hemorrhage in patients taking factor Xa inhibitors - Individual patient data analysis of ANNEXA-4 and TICH-NOAC.

BACKGROUND: Data comparing the specific reversal agent andexanet alfa with non-specific treatments in patients with non-traumatic intracerebral hemorrhage (ICH) associated with factor-Xa inhibitor (FXaI) use are scarce. AIM: The study aimed to determine the association between the use of andexanet alfa compared with non-specific treatments with the rate of hematoma expansion and thromboembolic complications in patients with FXaI-associated ICH. METHODS: We performed an individual patient data analysis combining two independent, prospective studies: ANNEXA-4 (180 patients receiving andexanet alfa, NCT02329327) and TICH-NOAC (63 patients receiving tranexamic acid or placebo ± prothrombin complex concentrate, NCT02866838). The primary efficacy outcome was hematoma expansion on follow-up imaging. The primary safety outcome was any thromboembolic complication (ischemic stroke, myocardial infarction, pulmonary embolism, or deep vein thrombosis) at 30 days. We used binary logistic regression models adjusted for baseline hematoma volume, age, calibrated anti-Xa activity, times from last intake of FXaI, and symptom onset to treatment, respectively. RESULTS: Among 243 participants included, the median age was 80 (IQR 75-84) years, baseline hematoma volume was 9.1 (IQR 3.4-21) mL and anti-Xa activity 118 (IQR 78-222) ng/mL. Times from last FXaI intake and symptom onset to treatment were 11 (IQR 7-16) and 4.7 (IQR 3.0-7.6) h, respectively. Overall, 50 patients (22%) experienced hematoma expansion (ANNEXA-4: n=24 (14%); TICH-NOAC: n=26 (41%)). After adjusting for pre-specified confounders (baseline hematoma volume, age, calibrated anti-Xa activity, times from last intake of FXaI, and symptom onset to treatment, respectively), treatment with andexanet alfa was independently associated with decreased odds for hematoma expansion (aOR 0.33, 95% CI 0.13-0.80, p = 0.015). Overall, 26 patients (11%) had any thromboembolic complication within 30 days (ANNEXA-4: n=20 (11%); TICH-NOAC: n=6 (10%)). There was no association between any thromboembolic complication and treatment with andexanet alfa (aOR 0.70, 95% CI 0.16-3.12, p = 0.641). CONCLUSION: The use of andexanet alfa compared to any other non-specific treatment strategy was associated with decreased odds for hematoma expansion, without increased odds for thromboembolic complications.

Baseline characteristics and outcome of stroke patients after endovascular therapy according to previous symptomatic vascular disease and sex.

Aim: The aim of this study was to investigate baseline characteristics and outcome of patients after endovascular therapy (EVT) for acute large vessel occlusion (LVO) in relation to their history of symptomatic vascular disease and sex. Methods: Consecutive EVT-eligible patients with LVO in the anterior circulation admitted to our stroke center between 04/2015 and 04/2020 were included in this observational cohort study. All patients were treated according to a standardized acute ischaemic stroke (AIS) protocol. Baseline characteristics and successful reperfusion, recurrent/progressive in-hospital ischaemic stroke, symptomatic in-hospital intracranial hemorrhage, death at discharge and at 3 months, and functional outcome at 3 months were analyzed according to previous symptomatic vascular disease and sex. Results: 995 patients with LVO in the anterior circulation (49.4% women, median age 76 years, median admission NIHSS score 14) were included. Patients with multiple vs. no previous vascular events showed higher mortality at discharge (20% vs. 9.3%, age/sex - adjustedOR = 1.43, p = 0.030) and less independency at 3 months (28.8% vs. 48.8%, age/sex - adjustedOR = 0.72, p = 0.020). All patients and men alone with one or multiple vs. patients and men with no previous vascular events showed more recurrent/progressive in-hospital ischaemic strokes (19.9% vs. 6.4% in all patients, age/sex - adjustedOR = 1.76, p = 0.028) (16.7% vs. 5.8% in men, age-adjustedOR = 2.20, p = 0.035). Men vs. women showed more in-hospital symptomatic intracranial hemorrhage among patients with one or multiple vs. no previous vascular events (23.7% vs. 6.6% in men and 15.4% vs. 5.5% in women, OR = 2.32, p = 0.035/age - adjustedOR = 2.36, p = 0.035). Conclusions: Previous vascular events increased the risk of in-hospital complications and poorer outcome in the analyzed patients with EVT-eligible LVO-AIS. Our findings may support risk assessment in these stroke patients and could contribute to the design of future studies.

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

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.

Image-to-Image Translation for Simplified MRI Muscle Segmentation.

Deep neural networks recently showed high performance and gained popularity in the field of radiology. However, the fact that large amounts of labeled data are required for training these architectures inhibits practical applications. We take advantage of an unpaired image-to-image translation approach in combination with a novel domain specific loss formulation to create an "easier-to-segment" intermediate image representation without requiring any label data. The requirement here is that the task can be translated from a hard to a related but simplified task for which unlabeled data are available. In the experimental evaluation, we investigate fully automated approaches for segmentation of pathological muscle tissue in T1-weighted magnetic resonance (MR) images of human thighs. The results show clearly improved performance in case of supervised segmentation techniques. Even more impressively, we obtain similar results with a basic completely unsupervised segmentation approach.

Semi-Automatic MRI Muscle Volumetry to Diagnose and Monitor Hereditary and Acquired Polyneuropathies.

With emerging treatment approaches, it is crucial to correctly diagnose and monitor hereditary and acquired polyneuropathies. This study aimed to assess the validity and accuracy of magnet resonance imaging (MRI)-based muscle volumetry.Using semi-automatic segmentations of upper- and lower leg muscles based on whole-body MRI and axial T1-weighted turbo spin-echo sequences, we compared and correlated muscle volumes, and clinical and neurophysiological parameters in demyelinating Charcot-Marie-Tooth disease (CMT) (n = 13), chronic inflammatory demyelinating polyneuropathy (CIDP) (n = 27), and other neuropathy (n = 17) patients.The muscle volumes of lower legs correlated with foot dorsiflexion strength (p < 0.0001), CMT Neuropathy Score 2 (p < 0.0001), early gait disorders (p = 0.0486), and in CIDP patients with tibial nerve conduction velocities (p = 0.0092). Lower (p = 0.0218) and upper (p = 0.0342) leg muscles were significantly larger in CIDP compared to CMT patients. At one-year follow-up (n = 15), leg muscle volumes showed no significant decrease.MRI muscle volumetry is a promising method to differentiate and characterize neuropathies in clinical practice.

A comprehensive study on automated muscle segmentation for assessing fat infiltration in neuromuscular diseases.

Severity and progression of degenerative neuromuscular diseases can be sensitively captured by evaluating the fat infiltration of muscle tissue in T1-weighted MRI scans of human limbs. For computing the fat fraction, the original muscle needs to be first separated from other tissue. Five conceptionally different approaches were investigated and evaluated with respect to the segmentation of muscles of human thighs. Besides a rather basic thresholding approach, local (level set) as well as global (graph cut) energy-minimizing segmentation approaches with and without a shape prior energy term were examined. For experimental evaluations, a dataset containing 37 subjects was divided into four classes according to the degree of fat infiltration. Results show that the choice of the best method depends on the severity of fat infiltration. In severe cases, the best results were obtained with shape prior based graph cuts, whereas in marginal cases thresholding was sufficient. With the best approach, the worst-case error in fat fraction computation was always below 11% and on average between 2% for tissue showing no fat infiltrations and 6% for heavily infiltrated tissue. The obtained Dice similarity coefficients, measuring the segmentation quality, were on average between 0.85 and 0.92. Although segmentation of heavily infiltrated muscle tissue is extremely difficult, an approach for reasonably segmenting these image data was identified. Especially the negative impact on the calculated fat fraction can be reduced significantly.