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Fulltext Stroke care in Pusat Perubatan UKM; the actual picture

Sahathevan R, Wan Yahya WNN, Tan HJ, Mohd Ibrahim N

Med J Malaysia, 2013 Apr;68(2):187-8.
PMID: 23629577
Knowledge of Acute Stroke Management Among Healthcare Professionals: Development and Validation of Acute Stroke Management Questionnaire (ASMaQ)

Sim CY, Wan Zaidi WA, Shah SA, Wan Yahya WNN, Tan HJ

J Stroke Cerebrovasc Dis, 2021 Jan;30(1):105421.
PMID: 33160125 DOI: 10.1016/j.jstrokecerebrovasdis.2020.105421

BACKGROUND: Around 15.0% of all strokes occurred in hospitalised patients and studies showed significant delay in the stroke recognition and lack of awareness on thrombolytic therapy for acute ischaemic stroke (AIS) which lead to higher mortality for in-hospital stroke. We aimed to develop and validate a new instrument known as acute stroke management questionnaire (ASMaQ) to evaluate the awareness of healthcare professionals in managing acute ischaemic stroke cases.
METHODS: This study consisted of 3 steps; the formulation of ASMaQ draft, content validation and construct validity. A total of 110 questions were drafted with 5-point Likert scale answers. From the list, 31 were selected and subsequently tested on 158 participants. The results were analysed and validated using exploratory factor analysis on SPSS. Components were extracted and questions with low factor loading were removed. The internal consistency was then measured with Cronbach's alpha.
RESULTS: Following analysis, 3 components were extracted and named as general stroke knowledge, hyperacute stroke care and advanced stroke management. Two items were deleted leaving 29 out of 31 questions for the final validated ASMaQ. Internal consistency showed high reliability with Cronbach's alpha of 0.82. Our respondents scored a total cumulative mean of 113.62 marks or 66.6%. A sub analysis by occupation showed that medical assistants scored the lowest in the group with a score of 57% whilst specialists including neurologists scored the highest at 79.4%.
CONCLUSION: The ASMaQ is a newly developed and validated questionnaire consisting of 29 questions testing the respondents' acute stroke management knowledge.
Fulltext Cerebral Lupus and Cryptococcal Meningitis in a Pregnant Woman

Khoo CS, Marzukie MM, Yap SS, Wan Yahya WNN, Tan HJ

J Neurosci Rural Pract, 2020 Jan;11(1):183-186.
PMID: 32140026 DOI: 10.1055/s-0039-3402895

Systemic lupus erythematosus (SLE) is a chronic autoimmune and multisystem disorder, which frequently affects young women. During pregnancy, SLE flares could occur up to 65%, with renal and hematological manifestations being the most common. However, reports on neuropsychiatric lupus in pregnant women are scarce. We herein report a 26-year-old lupus pregnant woman, who had cerebral lupus with concurrent cryptococcal meningitis. This case highlights the complexity in diagnosing and managing our patient to achieve the best outcome for both the mother and infant.
Fulltext Belly dancer syndrome induced by salbutamol

Wong CK, Ng CF, Tan HJ, Wan Yahya WNN

BMJ Case Rep, 2021 Feb 26;14(2).
PMID: 33637510 DOI: 10.1136/bcr-2020-241244
Fulltext Machine Learning Models for Predicting Stroke Mortality in Malaysia: An Application and Comparative Analysis

Che Nawi CMNH, Mohd Hairon S, Wan Yahya WNN, Wan Zaidi WA, Musa KI

Cureus, 2023 Dec;15(12):e50426.
PMID: 38222138 DOI: 10.7759/cureus.50426

Background Stroke is a significant public health concern characterized by increasing mortality and morbidity. Accurate long-term outcome prediction for acute stroke patients, particularly stroke mortality, is vital for clinical decision-making and prognostic management. This study aimed to develop and compare various prognostic models for stroke mortality prediction. Methods In a retrospective cohort study from January 2016 to December 2021, we collected data from patients diagnosed with acute stroke from five selected hospitals. Data contained variables on demographics, comorbidities, and interventions retrieved from medical records. The cohort comprised 950 patients with 20 features. Outcomes (censored vs. death) were determined by linking data with the Malaysian National Mortality Registry. We employed three common survival modeling approaches, the Cox proportional hazard regression (Cox), support vector machine (SVM), and random survival forest (RSF), while enhancing the Cox model with Elastic Net (Cox-EN) for feature selection. Models were compared using the concordance index (C-index), time-dependent area under the curve (AUC), and discrimination index (D-index), with calibration assessed by the Brier score. Results The support vector machine (SVM) model excelled among the four, with three-month, one-year, and three-year time-dependent AUC values of 0.842, 0.846, and 0.791; a D-index of 5.31 (95% CI: 3.86, 7.30); and a C-index of 0.803 (95% CI: 0.758, 0.847). All models exhibited robust calibration, with three-month, one-year, and three-year Brier scores ranging from 0.103 to 0.220, all below 0.25. Conclusion The support vector machine (SVM) model demonstrated superior discriminative performance, suggesting its efficacy in developing prognostic models for stroke mortality. This study enhances stroke mortality prediction and supports clinical decision-making, emphasizing the utility of the support vector machine method.
Fulltext Machine Learning Application: A Bibliometric Analysis From a Half-Century of Research on Stroke

Che Nawi CMNH, Mohd Hairon S, Wan Yahya WNN, Wan Zaidi WA, Hassan MR, Musa KI

Cureus, 2023 Aug;15(8):e44142.
PMID: 37753006 DOI: 10.7759/cureus.44142

The quick advancement of digital technology through artificial intelligence has made it possible to deploy machine learning to predict stroke outcomes. Our aim is to examine the trend of machine learning applications in stroke-related research over the past 50 years. We used search terms stroke and machine learning to search for English versions of original and review articles and conference proceedings published over the past 50 years in Scopus and Web of Science databases. The Biblioshiny web application was utilized for the analysis. The trend of publication and prominent authors and journals were analyzed and identified. The collaborative network between countries was mapped, and a thematic map was used to monitor the authors' trending keywords. In total, 10,535 publications authored by 44,990 authors from 2,212 sources were retrieved. Two distinct clusters of collaborative network nodes were observed, with the United States serving as a connecting node. Three terms - deep learning, algorithms, and neural networks - are observed in the early stages of the emerging theme. Overall, international research collaborations, the establishment of global research initiatives, the development of computational science, and the availability of big data have facilitated the pervasive use of machine learning techniques in stroke research.
The effects of intravenous infusion of autologous mesenchymal stromal cells in patients with subacute middle cerebral artery infarct: a phase 2 randomized controlled trial on safety, tolerability and efficacy

Law ZK, Tan HJ, Chin SP, Wong CY, Wan Yahya WNN, Muda AS, et al.

Cytotherapy, 2021 Sep;23(9):833-840.
PMID: 33992536 DOI: 10.1016/j.jcyt.2021.03.005

BACKGROUND AIMS: Mesenchymal stromal cells (MSCs) are characterized by paracrine and immunomodulatory functions capable of changing the microenvironment of damaged brain tissue toward a more regenerative and less inflammatory milieu. The authors conducted a phase 2, single-center, assessor-blinded randomized controlled trial to investigate the safety and efficacy of intravenous autologous bone marrow-derived MSCs (BMMSCs) in patients with subacute middle cerebral artery (MCA) infarct.
METHODS: Patients aged 30-75 years who had severe ischemic stroke (National Institutes of Health Stroke Scale [NIHSS] score of 10-35) involving the MCA territory were recruited within 2 months of stroke onset. Using permuted block randomization, patients were assigned to receive 2 million BMMSCs per kilogram of body weight (treatment group) or standard medical care (control group). The primary outcomes were the NIHSS, modified Rankin Scale (mRS), Barthel Index (BI) and total infarct volume on brain magnetic resonance imaging (MRI) at 12 months. All outcome assessments were performed by blinded assessors. Per protocol, analyses were performed for between-group comparisons.
RESULTS: Seventeen patients were recruited. Nine were assigned to the treatment group, and eight were controls. All patients were severely disabled following their MCA infarct (median mRS = 4.0 [4.0-5.0], BI = 5.0 [5.0-25.0], NIHSS = 16.0 [11.5-21.0]). The baseline infarct volume on the MRI was larger in the treatment group (median, 71.7 [30.5-101.7] mL versus 26.7 [12.9-75.3] mL, P = 0.10). There were no between-group differences in median NIHSS score (7.0 versus 6.0, P = 0.96), mRS (2.0 versus 3.0, P = 0.38) or BI (95.0 versus 67.5, P = 0.33) at 12 months. At 12 months, there was significant improvement in absolute change in median infarct volume, but not in total infarct volume, from baseline in the treatment group (P = 0.027). No treatment-related adverse effects occurred in the BMMSC group.
CONCLUSIONS: Intravenous infusion of BMMSCs in patients with subacute MCA infarct was safe and well tolerated. Although there was no neurological recovery or functional outcome improvement at 12 months, there was improvement in absolute change in median infarct volume in the treatment group. Larger, well-designed studies are warranted to confirm this and the efficacy of BMMSCs in ischemic stroke.
Fulltext Neurological manifestations in SARS-CoV-2 infection: A single-center cross-sectional study in Malaysia

Tan HJ, Goh CH, Khoo CS, Ng CF, Tan JK, Wan Zaidi WA, et al.

Neurol Clin Neurosci, 2023 Jan;11(1):17-26.
PMID: 36714457 DOI: 10.1111/ncn3.12677

BACKGROUND: Neurological involvement associated with SARS-CoV-2 infection has been reported from different regions of the world. However, data from South East Asia are scarce. We described the neurological manifestations and their associated factors among the hospitalized COVID-19 patients from an academic tertiary hospital in Malaysia.
METHODS: A cross-sectional observational study of hospitalized COVID-19 patients was conducted. The neurological manifestations were divided into the self-reported central nervous system (CNS) symptoms, stroke associated symptoms, symptoms of encephalitis or encephalopathy and specific neurological complications. Multiple logistic regression was performed using demographic and clinical variables to determine the factors associated with outcome.
RESULTS: Of 156 hospitalized COVID-19 patients with mean age of 55.88 ± 6.11 (SD) years, 23.7% developed neurological complications, which included stroke, encephalitis and encephalopathy. Patients with neurological complications were more likely to have diabetes mellitus (p = 0.033), symptoms of stroke [limb weakness (p
Fulltext Global Impact of the COVID-19 Pandemic on Cerebral Venous Thrombosis and Mortality

Nguyen TN, Qureshi MM, Klein P, Yamagami H, Abdalkader M, Mikulik R, et al.

J Stroke, 2022 May;24(2):256-265.
PMID: 35677980 DOI: 10.5853/jos.2022.00752

BACKGROUND AND PURPOSE: Recent studies suggested an increased incidence of cerebral venous thrombosis (CVT) during the coronavirus disease 2019 (COVID-19) pandemic. We evaluated the volume of CVT hospitalization and in-hospital mortality during the 1st year of the COVID-19 pandemic compared to the preceding year.
METHODS: We conducted a cross-sectional retrospective study of 171 stroke centers from 49 countries. We recorded COVID-19 admission volumes, CVT hospitalization, and CVT in-hospital mortality from January 1, 2019, to May 31, 2021. CVT diagnoses were identified by International Classification of Disease-10 (ICD-10) codes or stroke databases. We additionally sought to compare the same metrics in the first 5 months of 2021 compared to the corresponding months in 2019 and 2020 (ClinicalTrials.gov Identifier: NCT04934020).
RESULTS: There were 2,313 CVT admissions across the 1-year pre-pandemic (2019) and pandemic year (2020); no differences in CVT volume or CVT mortality were observed. During the first 5 months of 2021, there was an increase in CVT volumes compared to 2019 (27.5%; 95% confidence interval [CI], 24.2 to 32.0; P<0.0001) and 2020 (41.4%; 95% CI, 37.0 to 46.0; P<0.0001). A COVID-19 diagnosis was present in 7.6% (132/1,738) of CVT hospitalizations. CVT was present in 0.04% (103/292,080) of COVID-19 hospitalizations. During the first pandemic year, CVT mortality was higher in patients who were COVID positive compared to COVID negative patients (8/53 [15.0%] vs. 41/910 [4.5%], P=0.004). There was an increase in CVT mortality during the first 5 months of pandemic years 2020 and 2021 compared to the first 5 months of the pre-pandemic year 2019 (2019 vs. 2020: 2.26% vs. 4.74%, P=0.05; 2019 vs. 2021: 2.26% vs. 4.99%, P=0.03). In the first 5 months of 2021, there were 26 cases of vaccine-induced immune thrombotic thrombocytopenia (VITT), resulting in six deaths.
CONCLUSIONS: During the 1st year of the COVID-19 pandemic, CVT hospitalization volume and CVT in-hospital mortality did not change compared to the prior year. COVID-19 diagnosis was associated with higher CVT in-hospital mortality. During the first 5 months of 2021, there was an increase in CVT hospitalization volume and increase in CVT-related mortality, partially attributable to VITT.