Neuroanatomy and severity of stroke in patients with type A aortic dissection.

BACKGROUND: Strokes are a longstanding complication of acute type A aortic dissection (ATAAD) repair. Understanding the neuroanatomy, mechanism, and severity of stroke will facilitate efforts to improve prediction, prevention, and treatment strategies. METHODS: Retrospective review of patients who sustained stroke from a consecutive series of patients undergoing ATAAD repair. Neuroimaging was interpreted by two stroke neurologists blinded to clinical results. Severity of stroke was assessed by the National Institutes of Health Stroke Scale (NIHSS). Residual disability at 30 days was assessed using the modified Rankin Scale (mRS). RESULTS: Twenty percent (38/189) of patients undergoing repair for ATAAD had stroke (unilateral 58%, bi-hemispheric 42% [p = .33]). All strokes were ischemic. No significant lateralization (right vs. left) was noted with unilateral strokes (26% vs. 32%, p = .67). Etiology of stroke was embolic (58%), hypoperfusion (26%), mixed (11%), and unknown (5%). There were no intraoperative variables that correlated with the neuroanatomy or mechanism of stroke. Preoperative carotid dissection was seen in 40% (n = 15), while postoperatively 10% (n = 4) sustained intracranial large vessel occlusion (LVO). Strokes were moderate or severe (NIHSS ≥ 9) in 97% of cases, with 66% incidence of moderate residual disability (mRS ≥ 3) at 1 month postoperatively. CONCLUSIONS: Strokes associated with ATAAD are severe at presentation resulting in significant disability. One in 10 strokes is due to LVO and potentially amenable to endovascular treatment. Heterogeneity in both location and etiology of stroke makes prevention challenging. Future trials may evaluate the role of early neuroimaging and simultaneous treatment of stroke given advancements in endovascular therapy.

Machine Learning for Detecting Early Infarction in Acute Stroke with Non-Contrast-enhanced CT.

Background Identifying the presence and extent of infarcted brain tissue at baseline plays a crucial role in the treatment of patients with acute ischemic stroke (AIS). Patients with extensive infarction are unlikely to benefit from thrombolysis or thrombectomy procedures. Purpose To develop an automated approach to detect and quantitate infarction by using non-contrast-enhanced CT scans in patients with AIS. Materials and Methods Non-contrast-enhanced CT images in patients with AIS (<6 hours from symptom onset to CT) who also underwent diffusion-weighted (DW) MRI within 1 hour after AIS were obtained from May 2004 to July 2009 and were included in this retrospective study. Ischemic lesions manually contoured on DW MRI scans were used as the reference standard. An automatic segmentation approach involving machine learning (ML) was developed to detect infarction. Randomly selected nonenhanced CT images from 157 patients with the lesion labels manually contoured on DW MRI scans were used to train and validate the ML model; the remaining 100 patients independent of the derivation cohort were used for testing. The ML algorithm was quantitatively compared with the reference standard (DW MRI) by using Bland-Altman plots and Pearson correlation. Results In 100 patients in the testing data set (median age, 69 years; interquartile range [IQR]: 59-76 years; 59 men), baseline non-contrast-enhanced CT was performed within a median time of 48 minutes from symptom onset (IQR, 27-93 minutes); baseline MRI was performed a median of 38 minutes (IQR, 24-48 minutes) later. The algorithm-detected lesion volume correlated with the reference standard of expert-contoured lesion volume in acute DW MRI scans (r = 0.76, P < .001). The mean difference between the algorithm-segmented volume (median, 15 mL; IQR, 9-38 mL) and the DW MRI volume (median, 19 mL; IQR, 5-43 mL) was 11 mL (P = .89). Conclusion A machine learning approach for segmentation of infarction on non-contrast-enhanced CT images in patients with acute ischemic stroke showed good agreement with stroke volume on diffusion-weighted MRI scans. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Nael in this issue.

Timing of Spot Sign Appearance, Spot Sign Volume, and Leakage Rate among Phases of Multiphase CTA Predict Intracerebral Hemorrhage Growth.

BACKGROUND AND PURPOSE: The presence of spot sign is associated with a high risk of hematoma growth. Our aim was to investigate the timing of the appearance, volume, and leakage rate of the spot sign for predicting hematoma growth in acute intracerebral hemorrhage using multiphase CTA. MATERIALS AND METHODS: In this single-center retrospective study, multiphase CTA in 3 phases was performed in acute intracerebral hemorrhage (defined as intraparenchymal ± intraventricular hemorrhages). Phases of the spot sign first appearance, spot sign volumes (microliter), and leakage rates among phases (microliter/second) were measured. Associations between baseline clinical and imaging variables including spot sign volume parameters (volume and leakage rate divided by median) and hematoma growth (>6 mL) were investigated using regression models. Receiver operating characteristic analysis was used as appropriate. RESULTS: Two hundred seventeen patients (131 men; median age, 70 years) were included. The spot sign was detected in 21.7%, 30.0%, and 29.0% in the first, second, and third phases, respectively, with median volumes of 19.7, 31.4, and 34.8 µl in these phases. Hematoma growth was seen in 44 patients (20.3%). By means of modeling, the following variables, namely the spot sign appearing in the first phase, first phase spot sign volume, spot sign appearing in the second or third phase, and spot sign positive and negative leakage rates, were associated with hematoma growth. Among patients with a spot sign, the absolute leakage rate accounting for both positive and negative leakage rates was also associated with hematoma growth (per 1-µl/s increase; OR, 1.26; 95% CI, 1.04-1.52). Other hematoma growth predictors were stroke history, baseline NIHSS score, onset-to-imaging time, and baseline hematoma volume (all P values < .05). CONCLUSIONS: The timing of the appearance of the spot sign, volume, and leakage rate were all associated with hematoma growth. Development of automated software to generate these spot sign volumetric parameters would be an important next step to maximize the potential of temporal intracerebral hemorrhage imaging such as multiphase CTA for identifying those most at risk of hematoma growth.

Virtual Behavioural Medicine Program: A Novel Model of Care for Neuropsychiatric Symptoms in Dementia1.

BACKGROUND: Patients with severe neuropsychiatric symptoms (NPS) due to dementia are often uprooted from their familiar environments in long-term care or the community and transferred to emergency departments, acute care hospitals, or specialized behavioral units which can exacerbate NPS. To address this issue, we developed the Virtual Behavioural Medicine Program (VBM), an innovative model of virtual care designed to support management of patients with NPS in their own environment. OBJECTIVE: To determine efficacy of VBM in reducing admission to a specialized inpatient neurobehavioral unit for management of NPS. METHODS: We reviewed outcomes in the first consecutive 95 patients referred to VBM. Referrals were classified into two groups. In one group, patients were referred to VBM with a simultaneous application to an inpatient Behavioural Neurology Unit (BNU). The other group was referred only to VBM. The primary outcome was reduction in proportion of patients requiring admission to the BNU regardless of whether they were referred to the BNU or to VBM alone. RESULTS: For patients referred to VBM plus the BNU, the proportion needing admission to the BNU was reduced by 60.42%. For patients referred to VBM alone, it was 68.75%. CONCLUSION: VBM is a novel virtual neurobehavioral unit for treatment of NPS. Although the sample size was relatively small, especially for the VBM group, the data suggest that this program is a game changer that can reduce preventable emergency department visits and acute care hospital admissions. VBM is a scalable model of virtual care that can be adopted worldwide.

Botulinum toxin as early intervention for spasticity after stroke or non-progressive brain lesion: A meta-analysis.

Spasticity is a functionally limiting disorder that commonly occurs following stroke or severe brain injury, and may lead to disability and pain. In tandem with neurorehabilitation, botulinum toxin type A (BoNT-A) is the recommended first-line treatment for spasticity and, to date, the majority of trials have reported BoNT-A use in patients >6months after ictus. The present meta-analysis aimed to evaluate the effects of early BoNT-A injection for post-stroke spasticity on improvements in hypertonicity, disability, function and associated pain. A literature search yielded six studies reporting the effects of BoNT-A treatment within 3months post-stroke; three in the upper limb and three in the lower limb. All six studies permitted concomitant rehabilitation. Reduction in hypertonicity was compared in all six studies and revealed a significant treatment effect (P=0.0002) on the most affected joints between weeks 4 and 12 following injection. However, no significant effects of treatment were observed for improvement in disability at week 4 or improvement in function at weeks 4 and 20-24. A trend towards reduction in spasticity-related pain at week 4 following BoNT-A treatment (P=0.13) was also observed. These results demonstrate the beneficial effects of BoNT-A treatment on improving hypertonicity within 3months post-stroke and emphasise the importance of concomitant neurorehabilitation therapy.