Intracerebral hemorrhage is a devastating disease. Despite its clinical importance, the pathophysiology of intracerebral hemorrhage is not well understood. Hematoma expansion occurs in a large subset ...of patients and is a predictor of poor outcomes. Since hematoma growth provides a potential opportunity for therapeutic intervention, a thorough understanding of its biological mechanisms is of key importance. After vessel rupture, an initial hematoma forms. Following this initial phase, accumulating evidence suggests that the mass effect causes secondary vessel rupture, which contributes to the hematoma and may trigger an avalanche of further vessel ruptures. The circumstances under which this occurs and to what extent secondary hemorrhage contributes to final hematoma volume remain unknown, however. To address these questions, a translational approach seems most suitable. Current experimental models include intracranial injections of collagenase or autologous blood. Each has individual strengths and weaknesses in its ability to simulate human intracerebral hemorrhage. The ultimate goal for improved understanding and modeling of the pathophysiology of hematoma expansion is to identify new treatment approaches.
Spreading depression (SD) is an intense and prolonged depolarization in the central nervous systems from insect to man. It is implicated in neurological disorders such as migraine and brain injury. ...Here, using an in vivo mouse model of focal neocortical seizures, we show that SD may be a fundamental defense against seizures. Seizures induced by topical 4-aminopyridine, penicillin or bicuculline, or systemic kainic acid, culminated in SDs at a variable rate. Greater seizure power and area of recruitment predicted SD. Once triggered, SD immediately suppressed the seizure. Optogenetic or KCl-induced SDs had similar antiseizure effect sustained for more than 30 min. Conversely, pharmacologically inhibiting SD occurrence during a focal seizure facilitated seizure generalization. Altogether, our data indicate that seizures trigger SD, which then terminates the seizure and prevents its generalization.
Background:
Rebound phenomena after discontinuation of different treatments for relapsing–remitting multiple sclerosis (RRMS) have previously been described. Systematic database research in PubMed ...did not show any report with relapse directly associated with dimethyl fumarate (DMF) cessation.
Case presentation:
Here, we report on a 38-year-old Caucasian male patient suffering from a relatively mild course of RRMS who developed a fulminant clinical rebound 2 months after discontinuation of DMF therapy. Radiological alterations presented impressively with primarily spinal involvement. The patient received intensive care and multiple immunomodulating therapies.
Conclusion:
We report on this case to raise neurologist’s awareness of complications of basic therapy discontinuation in RRMS.
Recurrent waves of spreading depolarization (SD) occur in brain injury and are thought to affect outcomes. What triggers SD in intracerebral hemorrhage is poorly understood. We employed intrinsic ...optical signaling, laser speckle flowmetry, and electrocorticography to elucidate the mechanisms triggering SD in a collagenase model of intracortical hemorrhage in mice. Hematoma growth, SD occurrence, and cortical blood flow changes were tracked. During early hemorrhage (0–4 h), 17 out of 38 mice developed SDs, which always originated from the hematoma. No SD was detected at late time points (8–52 h). Neither hematoma size, nor peri-hematoma perfusion were associated with SD occurrence. Further, arguing against ischemia as a trigger factor, normobaric hyperoxia did not inhibit SD occurrence. Instead, SDs always occurred during periods of rapid hematoma growth, which was two-fold faster immediately preceding an SD compared with the peak growth rates in animals that did not develop any SDs. Induced hypertension accelerated hematoma growth and resulted in a four-fold increase in SD occurrence compared with normotensive animals. Altogether, our data suggest that spontaneous SDs in this intracortical hemorrhage model are triggered by the mechanical distortion of tissue by rapidly growing hematomas.
We hypothesized that imaging-only-based machine learning algorithms can analyze non-enhanced CT scans of patients with acute intracerebral hemorrhage (ICH). This retrospective multicenter cohort ...study analyzed 520 non-enhanced CT scans and clinical data of patients with acute spontaneous ICH. Clinical outcome at hospital discharge was dichotomized into good outcome and poor outcome using different modified Rankin Scale (mRS) cut-off values. Predictive performance of a random forest machine learning approach based on filter- and texture-derived high-end image features was evaluated for differentiation of functional outcome at mRS 2, 3, and 4. Prediction of survival (mRS ≤ 5) was compared to results of the ICH Score. All models were tuned, validated, and tested in a nested 5-fold cross-validation approach. Receiver-operating-characteristic area under the curve (ROC AUC) of the machine learning classifier using image features only was 0.80 (95% CI 0.77; 0.82) for predicting mRS ≤ 2, 0.80 (95% CI 0.78; 0.81) for mRS ≤ 3, and 0.79 (95% CI 0.77; 0.80) for mRS ≤ 4. Trained on survival prediction (mRS ≤ 5), the classifier reached an AUC of 0.80 (95% CI 0.78; 0.82) which was equivalent to results of the ICH Score. If combined, the integrated model showed a significantly higher AUC of 0.84 (95% CI 0.83; 0.86,
P
value <0.05). Accordingly, sensitivities were significantly higher at Youden Index maximum cut-offs (77% vs. 74% sensitivity at 76% specificity,
P
value <0.05). Machine learning–based evaluation of quantitative high-end image features provided the same discriminatory power in predicting functional outcome as multidimensional clinical scoring systems. The integration of conventional scores and image features had synergistic effects with a statistically significant increase in AUC.
Background and Purpose- Mechanisms contributing to acute hematoma growth in intracerebral hemorrhage are not well understood. Neuropathological studies suggest that the initial hematoma may create ...mass effect that can tear vessels in the vicinity by shearing, causing further bleeding and hematoma growth. Methods- To test this in mice, we simulated initial intracerebral hemorrhage by intrastriatal injection of a liquid polymer that coagulates upon contact with tissue and measured the presence and volume of bleeding secondary to the mass effect using Hemoglobin ELISA 15 minutes after injection. Results- Secondary hemorrhage occurred in a volume-dependent (4, 7.5, or 15 μL of polymer) and rate-dependent (0.05, 0.5, or 5 μL/s) manner. Anticoagulation (warfarin or dabigatran) exacerbated the secondary hemorrhage volume. In a second model of hematoma expansion, we confirmed that intrastriatal whole blood injection (15 μL, 0.5 μL/s) also caused secondary bleeding, using acute Evans blue extravasation as a surrogate. Anticoagulation once again exacerbated secondary hemorrhage after intrastriatal whole blood injection. Secondary hemorrhage directly and significantly correlated with arterial blood pressures in both nonanticoagulated and anticoagulated mice, when modulated by phenylephrine or labetalol. Conclusions- Our study provides the first proof of concept for secondary vessel rupture and bleeding as a potential mechanism for intracerebral hematoma growth.
Follow-up imaging in intracerebral hemorrhage is not standardized and radiologists rely on different imaging modalities to determine hematoma growth. This study assesses the volumetric accuracy of ...different imaging modalities (MRI, CT angiography, postcontrast CT) to measure hematoma size.
28 patients with acute spontaneous intracerebral hemorrhage referred to a tertiary stroke center were retrospectively included between 2018 and 2019. Inclusion criteria were (1) spontaneous intracerebral hemorrhage (supra- or infratentorial), (2) noncontrast CT imaging performed on admission, (3) follow-up imaging (CT angiography, postcontrast CT, MRI), and (4) absence of hematoma expansion confirmed by a third cranial image within 6 days. Two independent raters manually measured hematoma volume by drawing a region of interest on axial slices of admission noncontrast CT scans as well as on follow-up imaging (CT angiography, postcontrast CT, MRI) using a semi-automated segmentation tool (Visage image viewer; version 7.1.10). Results were compared using Bland-Altman plots.
Mean admission hematoma volume was 18.79 ± 19.86 cc. All interrater and intrarater intraclass correlation coefficients were excellent (1; IQR 0.98-1.00). In comparison to hematoma volume on admission noncontrast CT volumetric measurements were most accurate in patients who received postcontrast CT (bias of - 2.47%, SD 4.67: n = 10), while CT angiography often underestimated hemorrhage volumes (bias of 31.91%, SD 45.54; n = 20). In MRI sequences intracerebral hemorrhage volumes were overestimated in T2* (bias of - 64.37%, SD 21.65; n = 10). FLAIR (bias of 6.05%, SD 35.45; n = 13) and DWI (bias of-14.6%, SD 31.93; n = 12) over- and underestimated hemorrhagic volumes.
Volumetric measurements were most accurate in postcontrast CT while CT angiography and MRI sequences often substantially over- or underestimated hemorrhage volumes.
Background and Purpose: Intracranial hemorrhage has been observed in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (COVID-19), but the clinical, imaging, and ...pathophysiological features of intracranial bleeding during COVID-19 infection remain poorly characterized. This study describes clinical and imaging characteristics of patients with COVID-19 infection who presented with intracranial bleeding in a European multicenter cohort. Methods: This is a multicenter retrospective, observational case series including 18 consecutive patients with COVID-19 infection and intracranial hemorrhage. Data were collected from February to May 2020 at five designated European special care centers for COVID-19. The diagnosis of COVID-19 was based on laboratory-confirmed diagnosis of SARS-CoV-2. Intracranial bleeding was diagnosed on computed tomography (CT) of the brain within one month of the date of COVID-19 diagnosis. The clinical, laboratory, radiologic, and pathologic findings, therapy and outcomes in COVID-19 patients presenting with intracranial bleeding were analyzed. Results: Eighteen patients had evidence of acute intracranial bleeding within 11 days (IQR 9–29) of admission. Six patients had parenchymal hemorrhage (33.3%), 11 had subarachnoid hemorrhage (SAH) (61.1%), and one patient had subdural hemorrhage (5.6%). Three patients presented with intraventricular hemorrhage (IVH) (16.7%). Conclusion: This study represents the largest case series of patients with intracranial hemorrhage diagnosed with COVID-19 based on key European countries with geospatial hotspots of SARS-CoV-2. Isolated SAH along the convexity may be a predominant bleeding manifestation and may occur in a late temporal course of severe COVID-19.
Background and Purpose: Fully automated methods for segmentation and volume quantification of intraparenchymal hemorrhage (ICH), intraventricular hemorrhage extension (IVH), and perihematomal edema ...(PHE) are gaining increasing interest. Yet, reliabilities demonstrate considerable variances amongst each other. Our aim was therefore to evaluate both the intra- and interrater reliability of ICH, IVH and PHE on ground-truth segmentation masks. Methods: Patients with primary spontaneous ICH were retrospectively included from a German tertiary stroke center (Charité Berlin; January 2016−June 2020). Baseline and follow-up non-contrast Computed Tomography (NCCT) scans were analyzed for ICH, IVH, and PHE volume quantification by two radiology residents. Raters were blinded to all demographic and outcome data. Inter- and intrarater agreements were determined by calculating the Intraclass Correlation Coefficient (ICC) for a randomly selected set of patients with ICH, IVH, and PHE. Results: 100 out of 670 patients were included in the analysis. Interrater agreements ranged from an ICC of 0.998 for ICH (95% CI 0.993; 0.997), to an ICC of 0.979 for IVH (95% CI 0.984; 0.993), and an ICC of 0.886 for PHE (95% CI 0.760; 0.938), all p-values < 0.001. Intrarater agreements ranged from an ICC of 0.997 for ICH (95% CI 0.996; 0.998), to an ICC of 0.995 for IVH (95% CI 0.992; 0.996), and an ICC of 0.980 for PHE (95% CI 0.971; 0.987), all p-values < 0.001. Conclusion Manual segmentations of ICH, IVH, and PHE demonstrate good-to-excellent inter- and intrarater reliabilities, with the highest agreement for ICH and IVH and lowest for PHE. Therefore, the degree of variances reported in fully automated quantification methods might be related amongst others to variances in ground-truth masks.
The objective of this study was to assess the performance of the first publicly available automated 3D segmentation for spontaneous intracerebral hemorrhage (ICH) based on a 3D neural network before ...and after retraining.
We performed an independent validation of this model using a multicenter retrospective cohort. Performance metrics were evaluated using the dice score (DSC), sensitivity, and positive predictive values (PPV). We retrained the original model (OM) and assessed the performance via an external validation design. A multivariate linear regression model was used to identify independent variables associated with the model's performance. Agreements in volumetric measurements and segmentation were evaluated using Pearson's correlation coefficients (r) and intraclass correlation coefficients (ICC), respectively. With 1040 patients, the OM had a median DSC, sensitivity, and PPV of 0.84, 0.79, and 0.93, compared to thoseo f 0.83, 0.80, and 0.91 in the retrained model (RM). However, the median DSC for infratentorial ICH was relatively low and improved significantly after retraining, at
< 0.001. ICH volume and location were significantly associated with the DSC, at
< 0.05. The agreement between volumetric measurements (r > 0.90,
> 0.05) and segmentations (ICC ≥ 0.9,
< 0.001) was excellent.
The model demonstrated good generalization in an external validation cohort. Location-specific variances improved significantly after retraining. External validation and retraining are important steps to consider before applying deep learning models in new clinical settings.
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