Can Emergency Physicians Perform Carotid Artery Point-of-Care Ultrasound to Detect Stenosis in Patients with TIA and Stroke? A Pilot Study

Introduction: Patients with severe, symptomatic carotid stenosis can have their subsequent stroke risk reduced by surgical intervention if performed soon after a transient ischemic attack (TIA) or stroke. Patients presenting to an emergency department (ED) without computed tomography angiography (CTA) with TIA/stroke, may require transfer to another hospital for imaging to rule out carotid artery stenosis. The objective of this study was to determine the test characteristics of carotid artery point-of-care ultrasound (POCUS) in detecting greater than 50% stenosis in patients presenting with TIA/stroke.Methods: We conducted a prospective cohort study on a convenience sample of adult patients presenting to a comprehensive stroke centre with TIA or stroke between June–October 2017. Carotid POCUS was performed. Primary outcome measure, stenosis ≥ 50%, was determined by the final radiology report of CTA. A blinded POCUS expert separately reviewed the archived carotid POCUS scans. We calculated sensitivity and specificity for stenosis ≥ 50%.Results: We conducted POCUS on 75 patients, of which 70 were included in our analyses. Of those 70, 14.3% were diagnosed with greater than 50% stenosis. Carotid POCUS performed as follows: sensitivity 70.0% (95% confidence interval [CI], 34.8%-93.3%); specificity 86.7% (95% CI, 75.4%-94.1%); positive likelihood ratio (LR +) 5.3 (95% CI, 1.2-9.3); negative likelihood ratio (LR -) 0.4 (95% CI, 0.0-0.7). The inter-rater reliability between POCUS performer interpretation and expert interpretation had moderate agreement (k = 0.68). Scans took a mean 6.2 ± 2.2 minutes to complete.Conclusion: Carotid POCUS has low to moderate association with CTA for detection of carotid artery stenosis ≥ 50%. Further research and investigation is needed prior to widespread use of carotid POCUS in patients with acute cerebral ischemia. Additionally, external validity is likely affected by availability of training, maintenance of competency, and experience in more rural centres

EEG Biofeedback as a Treatment for Substance Use Disorders: Review, Rating of Efficacy, and Recommendations for Further Research

Electroencephalographic (EEG) biofeedback has been employed in substance use disorder (SUD) over the last three decades. The SUD is a complex series of disorders with frequent comorbidities and EEG abnormalities of several types. EEG biofeedback has been employed in conjunction with other therapies and may be useful in enhancing certain outcomes of therapy. Based on published clinical studies and employing efficacy criteria adapted by the Association for Applied Psychophysiology and Biofeedback and the International Society for Neurofeedback and Research, alpha theta training—either alone for alcoholism or in combination with beta training for stimulant and mixed substance abuse and combined with residential treatment programs, is probably efficacious. Considerations of further research design taking these factors into account are discussed and descriptions of contemporary research are given

Minimally Invasive Subcortical Parafascicular Transsulcal Access for Clot Evacuation (Mi SPACE) for Intracerebral Hemorrhage

Background. Spontaneous intracerebral hemorrhage (ICH) is common and causes significant mortality and morbidity. To date, optimal medical and surgical intervention remains uncertain. A lack of definitive benefit for operative management may be attributable to adverse surgical effect, collateral tissue injury. This is particularly relevant for ICH in dominant, eloquent cortex. Minimally invasive surgery (MIS) offers the potential advantage of reduced collateral damage. MIS utilizing a parafascicular approach has demonstrated such benefit for intracranial tumor resection. Methods. We present a case of dominant hemisphere spontaneous ICH evacuated via the minimally invasive subcortical parafascicular transsulcal access clot evacuation (Mi SPACE) model. We use this report to introduce Mi SPACE and to examine the application of this novel MIS paradigm. Case Presentation. The featured patient presented with a left temporal ICH and severe global aphasia. The hematoma was evacuated via the Mi SPACE approach. Postoperative reassessments showed significant improvement. At two months, bedside language testing was normal. MRI tractography confirmed limited collateral injury. Conclusions. This case illustrates successful application of the Mi SPACE model to ICH in dominant, eloquent cortex and subcortical regions. MRI tractography illustrates collateral tissue preservation. Safety and feasibility studies are required to further assess this promising new therapeutic paradigm

Synthetic data in cancer and cerebrovascular disease research: A novel approach to big data.

ObjectivesSynthetic datasets are artificially manufactured based on real health systems data but do not contain real patient information. We sought to validate the use of synthetic data in stroke and cancer research by conducting a comparison study of cancer patients with ischemic stroke to non-cancer patients with ischemic stroke.Designretrospective cohort study.SettingWe used synthetic data generated by MDClone and compared it to its original source data (i.e. real patient data from the Ottawa Hospital Data Warehouse).Outcome measuresWe compared key differences in demographics, treatment characteristics, length of stay, and costs between cancer patients with ischemic stroke and non-cancer patients with ischemic stroke. We used a binary, multivariable logistic regression model to identify risk factors for recurrent stroke in the cancer population.ResultsUsing synthetic data, we found cancer patients with ischemic stroke had a lower prevalence of hypertension (52.0% in the cancer cohort vs 57.7% in the non-cancer cohort, pConclusionWe demonstrated the utility of synthetic data in stroke and cancer research and provided key differences between cancer and non-cancer patients with ischemic stroke. Synthetic data is a powerful tool that can allow researchers to easily explore hypothesis generation, enable data sharing without privacy breaches, and ensure broad access to big data in a rapid, safe, and reliable fashion

Baseline demographics, treatment characteristics, and outcomes for cancer patients diagnosed with ischemic stroke within a 2-year period compared to patients with ischemic stroke and no history of malignancy.

Baseline demographics, treatment characteristics, and outcomes for cancer patients diagnosed with ischemic stroke within a 2-year period compared to patients with ischemic stroke and no history of malignancy.</p

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ObjectivesSynthetic datasets are artificially manufactured based on real health systems data but do not contain real patient information. We sought to validate the use of synthetic data in stroke and cancer research by conducting a comparison study of cancer patients with ischemic stroke to non-cancer patients with ischemic stroke.Designretrospective cohort study.SettingWe used synthetic data generated by MDClone and compared it to its original source data (i.e. real patient data from the Ottawa Hospital Data Warehouse).Outcome measuresWe compared key differences in demographics, treatment characteristics, length of stay, and costs between cancer patients with ischemic stroke and non-cancer patients with ischemic stroke. We used a binary, multivariable logistic regression model to identify risk factors for recurrent stroke in the cancer population.ResultsUsing synthetic data, we found cancer patients with ischemic stroke had a lower prevalence of hypertension (52.0% in the cancer cohort vs 57.7% in the non-cancer cohort, pConclusionWe demonstrated the utility of synthetic data in stroke and cancer research and provided key differences between cancer and non-cancer patients with ischemic stroke. Synthetic data is a powerful tool that can allow researchers to easily explore hypothesis generation, enable data sharing without privacy breaches, and ensure broad access to big data in a rapid, safe, and reliable fashion.</div

A comparison of the baseline characteristics and outcomes between the synthetic dataset generated from MDClone and real patient dataset for a cohort of stroke patients with a diagnosis of cancer at the Ottawa Hospital.

A comparison of the baseline characteristics and outcomes between the synthetic dataset generated from MDClone and real patient dataset for a cohort of stroke patients with a diagnosis of cancer at the Ottawa Hospital.</p

Synthetic deidentified study dataset–non-cancer patients.

Synthetic deidentified study dataset–non-cancer patients.</p

Synthetic deidentified study dataset–cancer patients.

Synthetic deidentified study dataset–cancer patients.</p

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Histograms of the distribution of age at cancer diagnosis in the synthetic dataset (A) and the original dataset (B).</p