Search

Background and purposeMechanical thrombectomy helps prevent disability in patients with acute ischemic stroke involving occlusion of a large cerebral vessel. Thrombectomy requires procedural expertise and not all hospitals have the staff to perform this intervention. Few population-wide data exist regarding access to mechanical thrombectomy.MethodsWe examined access to thrombectomy for ischemic stroke using discharge data from calendar years 2016 to 2018 from all nonfederal emergency departments and acute care hospitals across 11 US states encompassing 80 million residents. Facilities were classified as hubs if they performed mechanical thrombectomy, gateways if they transferred patients who ultimately underwent mechanical thrombectomy, and gaps otherwise. We used standard descriptive statistics and unadjusted logistic regression models in our primary analyses.ResultsAmong 205 681 patients with ischemic stroke, 100 139 (48.7% [95% CI, 48.5%–48.9%]) initially received care at a thrombectomy hub, 72 534 (35.3% [95% CI, 35.1%–35.5%]) at a thrombectomy gateway, and 33 008 (16.0% [95% CI, 15.9%–16.2%]) at a thrombectomy gap. Patients who initially received care at thrombectomy gateways were substantially less likely to ultimately undergo thrombectomy than patients who initially received care at thrombectomy hubs (odds ratio, 0.27 [95% CI, 0.25–0.28]). Rural patients had particularly limited access: 27.7% (95% CI, 26.9%–28.6%) of such patients initially received care at hubs versus 69.5% (95% CI, 69.1%–69.9%) of urban patients. For 93.8% (95% CI, 93.6%–94.0%) of patients with stroke at gateways, their initial facility was capable of delivering intravenous thrombolysis, compared with 76.3% (95% CI, 75.8%–76.7%) of patients at gaps. Our findings were unchanged in models adjusted for demographics and comorbidities and persisted across multiple sensitivity analyses, including analyses adjusting for estimated stroke severity.ConclusionsWe found that a substantial proportion of patients with ischemic stroke across the United States lacked access to thrombectomy even after accounting for interhospital transfers. US systems of stroke care require further development to optimize thrombectomy access.

Background and Objectives: Cancer is associated with an increased risk of acute ischemic stroke (AIS) and venous thromboembolism. The role of a cardiac right-to-left shunt (RLS) as a surrogate parameter for paradoxical embolism in cancer-related strokes is uncertain. We sought to investigate the relationship between the presence of an RLS and cancer in AIS patients. Methods: We included consecutive AIS patients hospitalized at our tertiary stroke center between January 2015 and December 2020 with available RLS status as detected on transesophageal echocardiography (TEE). Active cancers were retrospectively identified and the association with RLS was assessed with multivariable logistic regression and inverse probability of treatment weighting to minimize the ascertainment bias of having a TEE obtained. Results: Of the 2236 AIS patients included, 103 (4.6%) had active cancer, of whom 24 (23%) were diagnosed with RLS. An RLS was present in 774 out of the 2133 AIS patients without active cancer (36%). After adjustment and weighting, the absence of RLS was associated with active cancer (adjusted odds ratio (aOR) 2.29; 95% confidence interval (CI), 1.14–4.58). When analysis was restricted to patients younger than 60 years of age or those with a high-risk RLS (Risk of Paradoxical Embolism Score ⩾ 6), there was no association between RLS and cancer (aOR, 3.07; 95% CI, 0.79–11.88 and aOR, 0.56; 95% CI, 0.10–3.10, respectively). Conclusion: RLS was diagnosed less frequently in AIS patients with cancer than in cancer-free patients, suggesting that arterial sources may play a larger role in cancer-related strokes than paradoxical venous embolization. Future studies are needed to validate these findings and evaluate potential therapeutic implications, such as the general indication, or lack thereof, for patent foramen ovale (PFO) closure in this patient population. [ABSTRACT FROM AUTHOR]

Objective: Approximately half of ischemic strokes (IS) in cancer patients are cryptogenic, with many presumed cardioembolic. We evaluated whether there were specific miRNA and mRNA transcriptome architectures in peripheral blood of IS patients with and without comorbid cancer, and between cardioembolic versus noncardioembolic IS etiologies in comorbid cancer. Methods: We studied patients with cancer and IS (CS; n = 42), stroke only (SO; n = 41), and cancer only (n = 28), and vascular risk factor‐matched controls (n = 30). mRNA‐Seq and miRNA‐Seq data, analyzed with linear regression models, identified differentially expressed genes in CS versus SO and in cardioembolic versus noncardioembolic CS, and miRNA‐mRNA regulatory pairs. Network‐level analyses identified stroke etiology‐specific responses in CS. Results: A total of 2,085 mRNAs and 31 miRNAs were differentially expressed between CS and SO. In CS, 122 and 35 miRNA‐mRNA regulatory pairs, and 5 and 3 coexpressed gene modules, were associated with cardioembolic and noncardioembolic CS, respectively. Complement, growth factor, and immune/inflammatory pathways showed differences between IS etiologies in CS. A 15‐gene biomarker panel assembled from a derivation cohort (n = 50) correctly classified 81% of CS and 71% of SO participants in a validation cohort (n = 33). Another 15‐gene panel correctly identified etiologies for 13 of 13 CS‐cardioembolic and 11 of 11 CS‐noncardioembolic participants upon cross‐validation; 11 of 16 CS‐cryptogenic participants were predicted cardioembolic. Interpretation: We discovered unique mRNA and miRNA transcriptome architecture in CS and SO, and in CS with different IS etiologies. Cardioembolic and noncardioembolic etiologies in CS showed unique coexpression networks and potential master regulators. These may help distinguish CS from SO and identify IS etiology in cryptogenic CS patients. ANN NEUROL 2024;96:565–581 [ABSTRACT FROM AUTHOR]

In this study, models based on quantitative imaging biomarkers of post-stroke structural connectome disruption were used to predict six-month outcomes in various domains. Demographic information and clinical MRIs were collected from 40 ischemic stroke subjects (age: 68.1 ± 13.2 years, 17 female, NIHSS: 6.8 ± 5.6). Diffusion-weighted images were used to create lesion masks, which were uploaded to the Network Modification (NeMo) Tool. The NeMo Tool, using only clinical MRIs, allows estimation of connectome disruption at three levels: whole brain, individual gray matter regions and between pairs of gray matter regions. Partial Least Squares Regression models were constructed for each level of connectome disruption and for each of the three six-month outcomes: applied cognitive, basic mobility and daily activity. Models based on lesion volume were created for comparison. Cross-validation, bootstrapping and multiple comparisons corrections were implemented to minimize over-fitting and Type I errors. The regional disconnection model best predicted applied cognitive (R(2)  = 0.56) and basic mobility outcomes (R(2)  = 0.70), while the pairwise disconnection model best predicted the daily activity measure (R(2)  = 0.72). These results demonstrate that models based on connectome disruption metrics were more accurate than ones based on lesion volume and that increasing anatomical specificity of disconnection metrics does not always increase model accuracy, likely due to statistical adjustments for concomitant increases in data dimensionality. This work establishes that the NeMo Tool's measures of baseline connectome disruption, acquired using only routinely collected MRI scans, can predict 6-month post-stroke outcomes in various functional domains including cognition, motor function and daily activities. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.

The aim of this work was to quantitatively model cross-sectional relationships between structural connectome disruptions caused by cerebral infarction and measures of clinical performance. Imaging biomarkers of 41 ischemic stroke patients (72.0 ± 12.0 years, 20 female) were related to their baseline performance in 18 cognitive, physical and daily life activity assessments. Individual estimates of structural connectivity disruption in gray matter regions were computed using the Change in Connectivity (ChaCo) score. ChaCo scores were utilized because they can be calculated using routinely collected clinical magnetic resonance imagings. Partial Least Squares Regression (PLSR) was used to predict various acute impairment and activity measures from ChaCo scores and patient demographics. Statistical methods of cross-validation, bootstrapping and multiple comparisons correction were implemented to minimize over-fitting and Type I errors. Multiple linear regression models based on lesion volume and lateralization information were constructed for comparison. All models based on connectivity disruption had lower Akaike Information Criterion and almost all had better goodness-of-fit values (R(2) : 0.26-0.92) than models based on lesion characteristics (R(2) : 0.06-0.50). Confidence intervals of PLSR coefficients identified brain regions important in predicting each clinical assessment. Appropriate mapping of eloquent functions, that is, language and motor, and replication of results across pathologies provided validation of this method. Models of complex functions provided new insights into brain-behavior relationships. In addition to the potential applications in prognostication and rehabilitation development, this quantitative approach provides insight into the structural networks underlying complex functions like activities of daily living and cognition. Quantitative analysis of big data will be invaluable in understanding complex brain-behavior relationships.

Study objectiveWe evaluate the incidence of potentially incorrect emergency department (ED) diagnoses of Bell's palsy and identify factors associated with identification of a serious alternative diagnosis on follow-up.MethodsWe performed a retrospective cohort study from California's Office of Statewide Health Planning and Development for 2005 to 2011. Subjects were adult patients discharged from the ED with a diagnosis of Bell's palsy. Information related to demographics, imaging use, and comorbidities was collected. Our outcome was one of the following diagnoses made within 90 days of the index ED visit: stroke, intracranial hemorrhage, subarachnoid hemorrhage, brain tumor, central nervous system infection, Guillain-Barré syndrome, Lyme disease, otitis media/mastoiditis, or herpes zoster. We report hazard ratios (HRs) and 95% confidence intervals (CIs) for factors associated with misdiagnosis.ResultsA total of 43,979 patients were discharged with a diagnosis of Bell's palsy. Median age was 45 years. On 90-day follow-up, 356 patients (0.8%) received an alternative diagnosis, and 39.9% were made within 7 days. Factors associated with the receiving alternative diagnosis included increasing age (HR 1.11, 95% CI 1.01 to 1.21, every 10 years), black race (HR 1.68; 95% CI 1.13 to 2.48), diabetes (HR 1.46; 95% CI 1.10 to 1.95), and computed tomography or magnetic resonance imaging use (HR 1.43; 95% CI 1.10 to 1.85). Private insurance was negatively associated with an alternative diagnosis (HR 0.65; 95% CI 0.46 to 0.93). Stroke, herpes zoster, Guillain-Barré, and otitis media accounted for 85.4% of all alternative diagnoses.ConclusionEmergency providers have a very low rate of misdiagnosing Bell's palsy. The association between imaging use and misdiagnosis is likely confounded by patient acuity. Increasing age and diabetes are modest risk factors for misdiagnosis.

Background and purposeThe Network Modification (NeMo) Tool uses a library of brain connectivity maps from normal subjects to quantify the amount of structural connectivity loss caused by focal brain lesions. We hypothesized that the Network Modification Tool could predict remote brain tissue loss caused by poststroke loss of connectivity.MethodsBaseline and follow-up MRIs (10.7±7.5 months apart) from 26 patients with acute ischemic stroke (age, 74.6±14.1 years, initial National Institutes of Health Stroke Scale, 3.1±3.1) were collected. Lesion masks derived from diffusion-weighted images were superimposed on the Network Modification Tool's connectivity maps, and regional structural connectivity losses were estimated via the Change in Connectivity (ChaCo) score (ie, the percentage of tracks connecting to a given region that pass through the lesion mask). ChaCo scores were correlated with subsequent atrophy.ResultsStroke lesions' size and location varied, but they were more frequent in the left hemisphere. ChaCo scores, generally higher in regions near stroke lesions, reflected this lateralization and heterogeneity. ChaCo scores were highest in the postcentral and precentral gyri, insula, middle cingulate, thalami, putamen, caudate nuclei, and pallidum. Moderate, significant partial correlations were found between baseline ChaCo scores and measures of subsequent tissue loss (r=0.43, P=4.6×10(-9); r=0.61, P=1.4×10(-18)), correcting for the time between scans.ConclusionsChaCo scores varied, but the most affected regions included those with sensorimotor, perception, learning, and memory functions. Correlations between baseline ChaCo and subsequent tissue loss suggest that the Network Modification Tool could be used to identify regions most susceptible to remote degeneration from acute infarcts.