Utilizing machine learning to facilitate the early diagnosis of posterior circulation stroke.

BACKGROUND: Posterior Circulation Syndrome (PCS) presents a diagnostic challenge characterized by its variable and nonspecific symptoms. Timely and accurate diagnosis is crucial for improving patient outcomes. This study aims to enhance the early diagnosis of PCS by employing clinical and demographic data and machine learning. This approach targets a significant research gap in the field of stroke diagnosis and management. METHODS: We collected and analyzed data from a large national Stroke Registry spanning from January 2014 to July 2022. The dataset included 15,859 adult patients admitted with a primary diagnosis of stroke. Five machine learning models were trained: XGBoost, Random Forest, Support Vector Machine, Classification and Regression Trees, and Logistic Regression. Multiple performance metrics, such as accuracy, precision, recall, F1-score, AUC, Matthew's correlation coefficient, log loss, and Brier score, were utilized to evaluate model performance. RESULTS: The XGBoost model emerged as the top performer with an AUC of 0.81, accuracy of 0.79, precision of 0.5, recall of 0.62, and F1-score of 0.55. SHAP (SHapley Additive exPlanations) analysis identified key variables associated with PCS, including Body Mass Index, Random Blood Sugar, ataxia, dysarthria, and diastolic blood pressure and body temperature. These variables played a significant role in facilitating the early diagnosis of PCS, emphasizing their diagnostic value. CONCLUSION: This study pioneers the use of clinical data and machine learning models to facilitate the early diagnosis of PCS, filling a crucial gap in stroke research. Using simple clinical metrics such as BMI, RBS, ataxia, dysarthria, DBP, and body temperature will help clinicians diagnose PCS early. Despite limitations, such as data biases and regional specificity, our research contributes to advancing PCS understanding, potentially enhancing clinical decision-making and patient outcomes early in the patient's clinical journey. Further investigations are warranted to elucidate the underlying physiological mechanisms and validate these findings in broader populations and healthcare settings.

Machine learning-based prognostication of mortality in stroke patients.

Objectives: Predicting stroke mortality is crucial for personalized care. This study aims to design and evaluate a machine learning model to predict one-year mortality after a stroke. Materials and methods: Data from the National Multiethnic Stroke Registry was utilized. Eight machine learning (ML) models were trained and evaluated using various metrics. SHapley Additive exPlanations (SHAP) analysis was used to identify the influential predictors. Results: The final analysis included 9840 patients diagnosed with stroke were included in the study. The XGBoost algorithm exhibited optimal performance with high accuracy (94.5%) and AUC (87.3%). Core predictors encompassed National Institutes of Health Stroke Scale (NIHSS) at admission, age, hospital length of stay, mode of arrival, heart rate, and blood pressure. Increased NIHSS, age, and longer stay correlated with higher mortality. Ambulance arrival and lower diastolic blood pressure and lower body mass index predicted poorer outcomes. Conclusions: This model's predictive capacity emphasizes the significance of NIHSS, age, hospital stay, arrival mode, heart rate, blood pressure, and BMI in stroke mortality prediction. Specific findings suggest avenues for data quality enhancement, registry expansion, and real-world validation. The study underscores machine learning's potential for early mortality prediction, improving risk assessment, and personalized care. The potential transformation of care delivery through robust ML predictive tools for Stroke outcomes could revolutionize patient care, allowing for personalized plans and improved preventive strategies for stroke patients. However, it is imperative to conduct prospective validation to evaluate its practical clinical effectiveness and ensure its successful adoption across various healthcare environments.

Accreditation of stroke programs at the MENA + region; between aspiration and reality.

INTRODUCTION: Despite global progress in stroke care, challenges persist, especially in Low- and Middle-Income countries (LMIC). The Middle East and North Africa Stroke and Interventional Neurotherapies Organization (MENA-SINO) Stroke Program Accreditation Initiative aims to improve stroke care regionally. MATERIAL & METHOD: A 2022 survey assessed stroke unit readiness in the Middle East and North Africa (MENA) + region, revealing significant regional disparities in stroke care between high-income and low-income countries. Additionally, it demonstrated interest in the accreditation procedure and suggested that regional stroke program accreditation will improve stroke care for the involved centers. CONCLUSION: An accreditation program that is specifically tailored to the regional needs in the MENA + countries might be the solution. In this brief review, we will discuss potential challenges faced by such a program and we will put forward a well-defined 5-step accreditation process, beginning with a letter of intent, through processing the request and appointment of reviewers, the actual audit, the certification decisions, and culminating in granting a MIENA-SINO tier-specific certificate with recertification every 5 years.

Incidence, clinical features, and outcomes of posterior circulation ischemic stroke: insights from a large multiethnic stroke database.

Background: Posterior cerebral circulation ischemic stroke (PCS) comprises up to 25% of all strokes. It is characterized by variable presentation, leading to misdiagnosis and morbidity and mortality. We aim to describe PCS in large multiethnic cohorts. Methods: A retrospective review of a large national stroke database from its inception on the 1st of January 2014 till 31 December 2020. Incidence per 100,000 adult population/year, demographics, clinical features, stroke location, and outcomes were retrieved. We divided the cohort into patients from MENA (Middle East and North Africa) and others. Results: In total, 1,571 patients were identified. The incidence of PCS was observed to be rising and ranged from 6.3 to 13.2/100,000 adult population over the study period. Men were 82.4% of the total. The mean age was 54.9 ± 12.7 years (median 54 years, IQR 46, 63). MENA patients comprised 616 (39.2%) while others were 954 (60.7%); of these, the majority (80.5%) were from South Asia. Vascular risk factors were prevalent with 1,230 (78.3%) having hypertension, 970 (61.7%) with diabetes, and 872 (55.5%) having dyslipidemia. Weakness (944, 58.8%), dizziness (801, 50.5%), and slurred speech (584, 36.2%) were the most commonly presenting symptoms. The mean National Institute of Health Stroke Score (NIHSS) score was 3.8 ± 4.6 (median 3, IQR 1, 5). The overall most frequent stroke location was the distal location (568, 36.2%). The non-MENA cohort was younger, less vascularly burdened, and had more frequent proximal stroke location (p < 0.05). Dependency or death at discharge was seen in 39.5% and was associated with increasing age, and proximal and multilocation involvement; while at 90 days it was 27.4% and was associated with age, male sex, and having a MENA nationality (p < 0.05). Conclusion: In a multiethnic cohort of posterior circulation stroke patients from the MENA region and South Asia, we noted a rising incidence over time, high prevalence of vascular risk factors, and poor outcomes in older men from the MENA region. We also uncovered considerable disparities between the MENA and non-MENA groups in stroke location and outcome. These disparities are crucial factors to consider when tailoring individualized patient care plans. Further research is needed to thoroughly investigate the underlying reasons for these variations.

Predicting 90-day prognosis for patients with stroke: a machine learning approach.

Background: Stroke is a significant global health burden and ranks as the second leading cause of death worldwide. Objective: This study aims to develop and evaluate a machine learning-based predictive tool for forecasting the 90-day prognosis of stroke patients after discharge as measured by the modified Rankin Score. Methods: The study utilized data from a large national multiethnic stroke registry comprising 15,859 adult patients diagnosed with ischemic or hemorrhagic stroke. Of these, 7,452 patients satisfied the study's inclusion criteria. Feature selection was performed using the correlation and permutation importance methods. Six classifiers, including Random Forest (RF), Classification and Regression Tree, Linear Discriminant Analysis, Support Vector Machine, and k-Nearest Neighbors, were employed for prediction. Results: The RF model demonstrated superior performance, achieving the highest accuracy (0.823) and excellent discrimination power (AUC 0.893). Notably, stroke type, hospital acquired infections, admission location, and hospital length of stay emerged as the top-ranked predictors. Conclusion: The RF model shows promise in predicting stroke prognosis, enabling personalized care plans and enhanced preventive measures for stroke patients. Prospective validation is essential to assess its real-world clinical performance and ensure successful implementation across diverse healthcare settings.

Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning.

(1) Objective: This study aimed to construct a machine learning model for predicting the prognosis of ischemic stroke patients who underwent thrombolysis, assessed through the modified Rankin Scale (mRS) score 90 days after discharge. (2) Methods: Data were sourced from Qatar's stroke registry covering January 2014 to June 2022. A total of 723 patients with ischemic stroke who had received thrombolysis were included. Clinical variables were examined, encompassing demographics, stroke severity indices, comorbidities, laboratory results, admission vital signs, and hospital-acquired complications. The predictive capabilities of five distinct machine learning models were rigorously evaluated using a comprehensive set of metrics. The SHAP analysis was deployed to uncover the most influential predictors. (3) Results: The Support Vector Machine (SVM) model emerged as the standout performer, achieving an area under the curve (AUC) of 0.72. Key determinants of patient outcomes included stroke severity at admission; admission systolic and diastolic blood pressure; baseline comorbidities, notably hypertension (HTN) and coronary artery disease (CAD); stroke subtype, particularly strokes of undetermined origin (SUO); and hospital-acquired urinary tract infections (UTIs). (4) Conclusions: Machine learning can improve early prognosis prediction in ischemic stroke, especially after thrombolysis. The SVM model is a promising tool for empowering clinicians to create individualized treatment plans. Despite limitations, this study contributes to our knowledge and encourages future research to integrate more comprehensive data. Ultimately, it offers a pathway to improve personalized stroke care and enhance the quality of life for stroke survivors.

COVID-19-associated neurological sequelae: A case series on cerebral microbleeds and encephalopathy.

BACKGROUND: Critical illness-associated cerebral microbleeds and leukoencephalopathy connected to coronavirus disease 2019 (COVID-19) infection are emerging areas of concern in current medical literature. METHODS: We reviewed cases of patients with COVID-19-associated neurological manifestations to study the prevalence and outcome of such conditions. Case Series Findings: Our review yielded seven distinct patients. Six exhibited cerebral microbleeds, primarily localized in the juxtacortical white matter and the corpus callosum. In contrast, one individual presented with leukoencephalopathy. Tragically, of these patients, five succumbed to their ailments. One was discharged with mild cognitive impairments, while another underwent a tracheostomy and was subsequently moved to a long-term care establishment. CONCLUSION: Our findings underscore the significance of neuro-radiological observations in those grappling with severe manifestations of COVID-19, drawing attention to the possible neurological repercussions of the virus.

Rescue Stent Placement for Acute Ischemic Stroke with Large Vessel Occlusion Refractory to Mechanical Thrombectomy: A Multiethnic Middle Eastern/African/Asian Cohort.

PURPOSE: To investigate the safety and effectiveness of rescue stent placement in patients who experienced acute stroke in whom mechanical thrombectomy failed. METHODS: This was a retrospective review of a multiethnic stroke database. After stent placement, an aggressive antiplatelet protocol was followed with glycoprotein IIb/IIIa infusion. The primary outcomes were incidence of intracerebral hemorrhage (ICH), recanalization score, and favorable prognosis (modified Rankin score ≤ 2) at 90 days. A comparison was made between patients from the Middle East and North Africa (MENA) region and those from other regions. RESULTS: Fifty-five patients were included, with 87% being men. The mean age was 51.3 years (SD ±11.8); 32 patients (58%) were from South Asia, 12 (22%) from MENA, 9 (16%) from Southeast Asia, and 2 (4%) from elsewhere. Successful recanalization (modified Thrombolysis in Cerebral Infarction score = 2b/3) was achieved in 43 patients (78%), and symptomatic ICH occurred in 2 patients (4%). A favorable outcome at 90 days was seen in 26 of the 55 patients (47%). Apart from significantly older age-mean, 62.8 years (SD ±13; median, 69 years) versus 48.1 years (SD ±9.3; median, 49 years)-and coronary artery disease burden-4 (33%) versus 1 (2%) (P < .05), patients from MENA had risk factors, stroke severity, recanalization rates, ICH rates, and 90-day outcomes similar to those from South and Southeast Asia. CONCLUSION: Rescue stent placement showed good outcomes and a low risk of clinically significant bleeding in a multiethnic cohort of patients from MENA and South and Southeast Asia, similar to that in published literature.

Stroke services in the Middle East and adjacent region: A survey of 34 hospital-based stroke services.

Background: Acute stroke care is complex and requires multidisciplinary networking. There are insufficient data on stroke care in the Middle East and adjacent regions in Asia and Africa. Objective: Evaluate the state of readiness of stroke programs in the Middle East North Africa and surrounding regions (MENA+) to treat acute stroke. Method: Online questionnaire survey on the evaluation of stroke care across hospitals of MENA+ region between April 2021 and January 2022. Results: The survey was completed by 34/50 (68%) hospitals. The median population serviced by participating hospitals was 2 million. The median admission of patients with stroke/year was 600 (250-1,100). The median length of stay at the stroke units was 5 days. 34/34 (100%) of these hospitals have 24/7 CT head available. 17/34 (50%) have emergency guidelines for prehospital acute stroke care. Mechanical thrombectomy with/without IVT was available in 24/34 (70.6%). 51% was the median (IQR; 15-75%) of patients treated with IVT within 60 min from arrival. Thirty-five minutes were the median time to reverse warfarin-associated ICH. Conclusion: This is the first large study on the availability of resources for the management of acute stroke in the MENA+ region. We noted the disparity in stroke care between high-income and low-income countries. Concerted efforts are required to improve stroke care in low-income countries. Accreditation of stroke programs in the region will be helpful.

The impact of enteric coating of aspirin on aspirin responsiveness in patients with suspected or newly diagnosed ischemic stroke: prospective cohort study: results from the (ECASIS) study.

BACKGROUND AND PURPOSE: Uncertainty remains regarding the impact of enteric-coated aspirin (EC-ASA) on secondary prevention of ischemic stroke compared to plain aspirin (P-ASA). Hence, this study was designed to investigate the effect of EC formulation on ASA response via evaluating thromboxane B2 (TXB2) levels in patients with suspected or newly diagnosed stroke. METHODS: A prospective cohort study on suspected or newly diagnosed ischemic stroke patients who are aspirin-naive was conducted. Patients were received either EC aspirin or plain aspirin for at least 3 days. The primary outcome was the proportion of aspirin non-responsiveness between two groups (level of residual serum TXB2 associated with elevated thrombotic risk (< 99.0% inhibition or TXB2 > 3.1 ng/ml) within 72 h after three daily aspirin doses, while secondary outcomes were the incidence of early gastrointestinal tract (GIT) bleeding with the various aspirin preparations. (Trial registration: Clinicaltrials.gov NCT04330872 registered on 02 April 2020). RESULTS: Of 42 patients, ischemic strokes were confirmed in both P-ASA (81%) and EC-ASA (67%) arms. ASA non-responsiveness showed no significant difference between the two formulations (P-ASA vs. EC-ASA; 28.6% vs 23.8%; P = 0.726). Univariate and multivariate logistic regression analysis showed that patients treated with EC-ASA were more likely to have a lower rate of non-responders compared to P-ASA (unadjusted OR 0.78; 95% CI 0.20, 3.11); with the risk highest in type 2 diabetic patients with HBA1c > 6.5% (adjusted OR 6; 95% CI 1.02, 35.27; P = 0.047). No incidence of GIT bleeding observed throughout the study. CONCLUSION: A significant proportion of ASA non-responsiveness was recorded regardless of ASA formulation administered. The increased risk of ASA non-responsiveness in diabetic patients needs further exploration by larger prospective studies.

Ischemic stroke in patients that recover from COVID-19: Comparisons to historical stroke prior to COVID-19 or stroke in patients with active COVID-19 infection.

BACKGROUND AND PURPOSE: Understanding the relationship of COVID-19 to stroke is important. We compare characteristics of pre-pandemic historical stroke (Pre-C), cases in acute COVID infection (Active-C) and in patients who have recovered from COVID-19 infection (Post-C). METHODS: We interrogated the Qatar stroke database for all stroke admissions between Jan 2019 and Feb 2020 (Pre-C) to Active-C (Feb2020-Feb2021) and Post-C to determine how COVID-19 affected ischemic stroke sub-types, clinical course, and outcomes prior to, during and post-pandemic peak. We used the modified Rankin Scale (mRS) to measure outcome at 90-days (mRS 0-2 good recovery and mRS 3-6 as poor recovery). For the current analysis, we compared the clinical features and prognosis in patients with confirmed acute ischemic stroke. RESULTS: There were 1413 cases admitted (pre-pandemic: 1324, stroke in COVID-19: 46 and recovered COVID-19 stroke: 43). Patients with Active-C were significantly younger, had more severe symptoms, fever on presentation, more ICU admissions and poor stroke recovery at discharge when compared to Pre-C and Post-C. Large vessel disease and cardioembolic disease was significantly more frequent in Active-C compared to PRE-C or post-C. CONCLUSIONS: Stroke in Post-C has characteristics similar to Pre-C with no evidence of lasting effects of the virus on the short-term. However, Active-C is a more serious disease and tends to be more severe and have a poor prognosis.

Acute ischemic stroke and convexity subarachnoid hemorrhage in large vessel atherosclerotic stenosis: Case series and review of the literature.

Atraumatic convexity subarachnoid hemorrhage (cSAH) is a rare non-aneurysmal SAH, commonly due to ipsilateral internal carotid artery (ICA) stenosis. It is unusual for the cSAH to occur contralaterally to the infarct. We report two cases of acute ischemic stroke associated with contralateral and ipsilateral cSAH that had different presentations.

Stroke in Airplane Passengers: A Study from a Large International Hub.

INTRODUCTION: Stroke in air travelers is being increasingly recognized. We report on stroke among passengers arriving at or transiting through a busy air travel hub. METHODS: The stroke database of the sole tertiary care center for stroke in a large busy international hub was interrogated. Demographic data of transit passengers, their stroke risk factors, stroke severity, National Institutes of Health Stroke Scale (NIHSS), acute stroke interventions, discharge status and outcome utilizing the Modified Raking scale (mRS) were retrieved and compared between passengers and non-passenger controls. RESULTS: Forty-three flight-related stroke patients were compared to 2564 non-passenger stroke patients. The mean age in the flight-related stroke group was 59.53±10.83 years, 30/43 (69.8%) were males. The stroke subtypes were ischemic in 30 (69.8%) patients, hemorrhagic in 9 (20.9%), and transient ischemic attack in 3 (7.0%), with one cerebral sinus venous thrombosis (2.3%). The mean NIHSS score was 7.79±6.44 in passengers, demonstrating moderate severity. Ten patients (23.3%) received thrombolysis, one (2.3%) received thrombectomy, and one (2.3%) received both thrombolysis and thrombectomy. Outcomes, 54.8% had a good outcome (mRS 0-2), and 45.2% had dependence/death (mRS 3-6). CONCLUSION: Air passengers with stroke were found to be older with more severe strokes and a higher probability of receiving acute stroke treatment compared to non-passengers. Increased awareness with appropriate and timely recognition and triaging of transit passengers with stroke is warranted.

Trends in stroke admissions before, during and post-peak of the COVID-19 pandemic: A one-year experience from the Qatar stroke database.

BACKGROUND: Several reports document a decrease in the rates of stroke hospital admissions during the covid-19 pandemic. There is very little information whether the admission rates will change as the infection is controlled. We report on our rates of admissions before, during and following the peak of covid-19 infections in a prospective database from Qatar. METHODS AND RESULTS: The stroke admissions in the six months prior to COVID-19 pandemic averaged 229/month. There was a decrease to 157/month in March-June during the peak of the pandemic. In the 6 months following the peak, as covid-19 numbers began to decrease, the average numbers increased back to 192/month. There was an increase in severe ischemic strokes and decreased in functional recovery. The decreased admissions were mainly driven by fewer stroke mimics. Patients presenting with ischemic stroke or cerebral hemorrhage remained unchanged. CONCLUSIONS: Fewer stroke mimics presenting to the hospital can explain the fewer admissions and poor outcome at the height of the covid-19 pandemic. The continued decrease in the number of ischemic stroke and stroke mimic admissions following the pandemic peak requires more study.

Characteristics and comparisons of acute stroke in "recovered" to "active COVID-19 and "pre-pandemic" in Qatar database.

Understanding the relationship of COVID-19 to stroke is important. We compare characteristics of pre-pandemic stroke (PPS), cases in acute COVID infection (CS) and in patients who have recovered from COVID-19 infection (RCS). We interrogated the Qatar stroke database for all stroke admissions between Jan 2020 and Feb 2021 (PPS) to CS and RCS to determine how COVID-19 affected ischemic stroke sub-types, clinical course, and outcomes prior to, during and post-pandemic peak. There were 3264 cases admitted (pre-pandemic: 3111, stroke in COVID-19: 60 and recovered COVID-19 stroke: 93). Patients with CS were significantly younger, had more severe symptoms, fever on presentation, more ICU admissions and poor stroke recovery at discharge when compared to PPS and RCS. Large vessel disease and cardioembolic disease was significantly higher in CS compared to PPS or RCS. There was a significant decline in stroke mimics in CS. Stroke in RCS has characteristics similar to PPS with no evidence of lasting effects of the virus on the short-term. However, CS is a more serious disease and tends to be more severe and have a poor prognosis.

Carotid web stent for the prevention of recurrent stroke: Case report and literature review.

Carotid web has been identified as one of the missed causes of recurrent stroke. The diagnosis and management of such cases impose a challenge to medical practitioners. This etiology should be kept in mind, especially in case of recurrence of stroke in a similar cerebral territory.

Complicated Neurotuberculosis with sinus venous thrombosis: A case-report.

INTRODUCTION: Neurotuberculosis comprises around 6% of systemic tuberculosis. It targets a younger population, and it often leads to severe neurological complications or death. CASE REPORT: We report a young gentleman with a clinically defined tuberculous meningitis (TBM) and multiple neurological complication associated with TBM occurring simultaneously. This includes hydrocephalus requiring a ventriculoperitoneal shunt, vasculitic infarcts, cranial nerve palsies, TB granuloma and cerebral venous thrombosis. The cerebrospinal fluid polymerase chain reaction for tuberculosis as well as cultures remained negative repeatedly. The patient was treated with anti-tuberculous medication in addition to steroids based on validated scoring systems suggestive of TBM and made a good recovery. CONCLUSION: This report highlights the different complication seen with TBM and the importance of using clinical criteria to guide management plan particularly when cultures are negative.

Cytomegalovirus meningoencephalitis in a case of severe COVID-19 Pneumonia. A case report.

The use of steroids and other immune modulatory therapies in the treatment of severe COVID-19 pneumonia predisposes patients to the reemergence of opportunistic infections. Cytomegalovirus (CMV) reactivation can be one of them. A 55-year-old gentleman with severe COVID-19 pneumonia and hypoxic respiratory failure who was ventilated and received steroids but no other immunomodulatory drugs; had altered sensorium and multiple episodes of seizures in the later course of his illness. Brain MRI showed leptomeningeal enhancement and encephalopathy changes, electroencephalography (EEG) was suggestive of diffuse encephalopathy and his cerebrospinal fluid (CSF) analysis revealed high Cytomegalovirus PCR DNA titers (103,614). The patient made a complete recovery after treatment with Ganciclovir. Altered sensorium in cases of COVID-19 can be multifactorial. High index of suspicion for reactivation of dormant infections is warranted. CMV meningoencephalitis is one of the differential diagnoses. We believe this is the first case reported of CMV meningoencephalitis in the setting of severe COVID-19 infection.

Impact of COVID-19 pandemic on stroke admissions in Qatar.

INTRODUCTION: The COVID-19 pandemic has resulted in a dramatic unexplained decline in hospital admissions due to acute coronary syndromes and stroke. Several theories have emerged aiming to explain this decline, mostly revolving around the fear of contracting the disease and thus avoiding hospital visits. AIMS: In this study, we aim to examine the impact of the COVID-19 pandemic on stroke admissions to a tertiary care centre in Qatar. METHODS: The Hamad General Hospital stroke database was interrogated for stroke admissions between September 2019 and May 2020. The number of stroke admissions, stroke subtypes and short-term outcomes was compared between the 'pre-COVID-19' period (September 2019 to February 2020) and the COVID-19 pandemic period (March to May 2020). RESULTS: We observed a significant decline in monthly admissions in March (157), April (128) and May (135) compared with the pre-COVID-19 6-month average (229) (p=0.024). The reduction in admissions was most evident in functional stroke mimics. The average admissions decreased from 87 to 34 per month (p=0.0001). Although there were no significant differences in admissions due to ischaemic stroke (IS), intracranial haemorrhage or transient ischaemic attacks between the two periods, we noted a relative decrease in IS due to small vessel disease and an increase in those due to large vessel atherosclerosis in March to May 2020. CONCLUSIONS: The decline in overall stroke admissions during the COVID-19 pandemic is most likely related to concerns of contracting the infection, evidenced mainly by a decline in admissions of stroke mimics. However, a relative increase in large vessel occlusions raises suspicion of pathophysiological effects of the virus, and requires further investigation.

Characteristics and Comparison of 32 COVID-19 and Non-COVID-19 Ischemic Strokes and Historical Stroke Patients.

OBJECTIVES: The presence of COVID-19 infection may increase the risk of thrombotic events including ischemic strokes. Whilst a number of recent reports suggest that COVID-19 associated stroke tends to be severe, there is limited data on the effects of COVID-19 in prospective registries. MATERIAL AND METHODS: To determine how COVID-19 infection may affect cerebrovascular disease, we evaluated the ischemic stroke sub-types, clinical course and outcomes prior to and during the pandemic in Qatar. The Hamad General Hospital (HGH) stroke database was interrogated for stroke admissions during the last 4 months of 2019 and January-May 2020. RESULTS: In Qatar the number of confirmed cases of COVID-19 increased from only 2 in February to 779 in March, 12,628 in April and 45,501 in May. Stroke admissions to HGH declined marginally from an average of 97/month for six pre-COVID months to 72/month in March-May. There were 32 strokes that were positive for COVID-19. When compared to non-COVID-19 stroke during the three months of the pandemic, COVID-19 patients were younger with significantly lower rates of hypertension, diabetes and dyslipidemia. COVID-19 positive patients had more cortical strokes (34.4% vs 5.6%; p = 0.001), severe disease (NIHSS >10: 34.4% vs 16.7%; p = 0.001) prolonged hospitalization and fewer with good recovery (mRS 0-2: 28.1% vs 51.9%; p = 0.001). CONCLUSIONS: When compared to six pre-COVID-19 months, the number of ischemic stroke admissions during the three months of the pandemic declined marginally. COVID-19 positive patients were more likely to have a large cortical stroke with severe symptoms and poor outcome.