Implementation of low-intensity thrombolysis monitoring care in routine practice: process evaluation of the Optimal Post rtPA-IV Monitoring in Acute Ischemic Stroke (OPTIMISTmain) study in the United States.

INTRODUCTION: The ongoing OPTIMISTmain study, an international, multicenter, stepped-wedge cluster randomized trial, aims to determine effectiveness and safety of low-intensity versus standard monitoring in thrombolysis-treated patients with mild-to-moderate acute ischemic stroke (AIS). An embedded process evaluation explored integration and impact of the intervention on care processes at participating US sites. METHODS: A mixed-methods approach with quantitative and qualitative data were collected between September 2021 and November 2022. Implementer surveys were undertaken at pre- and post-intervention phases to understand the perceptions of low-intensity monitoring strategy. A sample of stroke care nurses were invited to participate in semi-structured interviews at an early stage of post-intervention. Qualitative data were analyzed deductively using the normalization process theory; quantitative data were tabulated. RESULTS: Interviews with 21 nurses at 8 hospitals have shown low-intensity monitoring was well accepted, as there were less time constraints and reduced workload for each patient. There were initial safety concerns over missing deteriorating patients and difficulties in changing established routines. Proper training, education, and communication, and changing the habits and culture of care, were key elements to successfully adopting the new monitoring care into routine practice. Similar results were found in the post-intervention survey (42 nurses from 13 hospitals). Nurses reported time being freed up to provide patient education (56%), daily living care (50%), early mobilization (26%), mood/cognition assessment (44%), and other aspects (i.e. communication, family support). CONCLUSIONS: Low-intensity monitoring for patients with mild-to-moderate acute ischemic stroke, facilitated by appropriate education and organizational support, appears feasible and acceptable at US hospitals.

The main Optimal Post rTpa-Iv Monitoring in Ischemic Stroke Trial (OPTIMISTmain): protocol for a pragmatic, stepped wedge, cluster randomized controlled trial.

Introduction Careful monitoring of patients who receive intravenous thrombolysis (IVT) for acute ischemic stroke (AIS) is resource-intensive, and potentially less relevant in those with mild degrees of neurological impairment who are at low-risk of symptomatic intracerebral hemorrhage (sICH) and other complications. \ Methods OPTIMISTmain is an international, multicenter, prospective, stepped wedge, cluster randomized, blinded outcome assessed trial aims to determine whether a less-intensity monitoring protocol is at least as effective, safe and efficient as standard post-IVT monitoring in patients with mild deficits post-AIS. Clinically-stable adult patients with mild AIS (defined by a NIHSS <10) who do not require intensive care within 2 hours post-IVT are recruited at hospitals in Australia, Chile, China, Malaysia, Mexico, UK, US and Vietnam. An average of 15 patients recruited per period (overall 60 patient participants) at 120 sites for a total of 7200 IVT-treated AIS patients will provide 90% power (one-sided α 0.025). The initiation of eligible hospitals is based on a rolling process whenever ready, stratified by country. Hospitals are randomly allocated using permuted blocks into 3 sequences of implementation, stratified by country and the projected number of patients to be recruited over 12 months. These sequences have four periods that dictate the order in which they are to switch from control (usual care) to intervention (implementation of low intensity monitoring protocol) to different clusters of patients in a stepped manner. Compared to standard monitoring, the low-intensity monitoring protocol includes assessments of neurological and vital signs every 15 minutes for 2 hours, 2 hourly (versus every 30 minutes) for 8 hours, and 4 hourly (versus every 1 hour) until 24 hours, post-IVT. The primary outcome measure is functional recovery, defined by the modified Rankin scale (mRS) at 90 days, a seven-point ordinal scale (0 [no residual symptom] to 6 [death]). Secondary outcomes include death or dependency, length of hospital stay, and health-related quality of life, sICH and serious adverse events. Conclusion OPTIMISTmain will provide Level I evidence for the safety and effectiveness of a low-intensity post-IVT monitoring protocol in patients with mild severity of AIS.

Assessment of a 16-Channel Ambulatory Dry Electrode EEG for Remote Monitoring.

Ambulatory EEGs began emerging in the healthcare industry over the years, setting a new norm for long-term monitoring services. The present devices in the market are neither meant for remote monitoring due to their technical complexity nor for meeting clinical setting needs in epilepsy patient monitoring. In this paper, we propose an ambulatory EEG device, OptiEEG, that has low setup complexity, for the remote EEG monitoring of epilepsy patients. OptiEEG's signal quality was compared with a gold standard clinical device, Natus. The experiment between OptiEEG and Natus included three different tests: eye open/close (EOC); hyperventilation (HV); and photic stimulation (PS). Statistical and wavelet analysis of retrieved data were presented when evaluating the performance of OptiEEG. The SNR and PSNR of OptiEEG were slightly lower than Natus, but within an acceptable bound. The standard deviations of MSE for both devices were almost in a similar range for the three tests. The frequency band energy analysis is consistent between the two devices. A rhythmic slowdown of theta and delta was observed in HV, whereas photic driving was observed during PS in both devices. The results validated the performance of OptiEEG as an acceptable EEG device for remote monitoring away from clinical environments.

Altered Tryptophan-Kynurenine Pathway in Delirium: A Review of the Current Literature.

Delirium, a common form of acute brain dysfunction, is associated with increased morbidity and mortality, especially in older patients. The underlying pathophysiology of delirium is not clearly understood, but acute systemic inflammation is known to drive delirium in cases of acute illnesses, such as sepsis, trauma, and surgery. Based on psychomotor presentations, delirium has three main subtypes, such as hypoactive, hyperactive, and mixed subtype. There are similarities in the initial presentation of delirium with depression and dementia, especially in the hypoactive subtype. Hence, patients with hypoactive delirium are frequently misdiagnosed. The altered kynurenine pathway (KP) is a promising molecular pathway implicated in the pathogenesis of delirium. The KP is highly regulated in the immune system and influences neurological functions. The activation of indoleamine 2,3-dioxygenase, and specific KP neuroactive metabolites, such as quinolinic acid and kynurenic acid, could play a role in the event of delirium. Here, we collectively describe the roles of the KP and speculate on its relevance in delirium.

Lipopolysaccharide-Induced Delirium-like Behaviour in a Rat Model of Chronic Cerebral Hypoperfusion Is Associated with Increased Indoleamine 2,3-Dioxygenase Expression and Endotoxin Tolerance.

Indoleamine 2,3-dioxygenase (IDO) and the tryptophan-kynurenine pathway (TRP-KP) are upregulated in ageing and could be implicated in the pathogenesis of delirium. This study evaluated the role of IDO/KP in lipopolysaccharide (LPS)-induced delirium in an animal model of chronic cerebral hypoperfusion (CCH), a proposed model for delirium. CCH was induced by a permanent bilateral common carotid artery ligation (BCCAL) in Sprague Dawley rats to trigger chronic neuroinflammation-induced neurodegeneration. Eight weeks after permanent BCCAL, the rats were treated with a single systemic LPS. The rats were divided into three groups: (1) post-BCCAL rats treated with intraperitoneal (i.p.) saline, (2) post-BCCAL rats treated with i.p. LPS 100 µg/kg, and (3) sham-operated rats treated with i.p. LPS 100 µg/kg. Each group consisted of 10 male rats. To elucidate the LPS-induced delirium-like behaviour, natural and learned behaviour changes were assessed by a buried food test (BFT), open field test (OFT), and Y-maze test at 0, 24-, 48-, and 72 h after LPS treatment. Serum was collected after each session of behavioural assessment. The rats were euthanised after the last serum collection, and the hippocampi and cerebral cortex were collected. The TRP-KP neuroactive metabolites were measured in both serum and brain tissues using ELISA. Our data show that LPS treatment in CCH rats was associated with acute, transient, and fluctuated deficits in natural and learned behaviour, consistent with features of delirium. These behaviour deficits were mild compared to the sham-operated rats, which exhibited robust behaviour impairments. Additionally, heightened hippocampal IDO expression in the LPS-treated CCH rats was associated with reduced serum KP activity together with a decrease in the hippocampal quinolinic acid (QA) expression compared to the sham-operated rats, suggested for the presence of endotoxin tolerance through the immunomodulatory activity of IDO in the brain. These data provide new insight into the underlying mechanisms of delirium, and future studies should further explore the role of IDO modulation and its therapeutic potential in delirium.

A Classification Model of EEG Signals Based on RNN-LSTM for Diagnosing Focal and Generalized Epilepsy.

Epilepsy is a chronic neurological disorder caused by abnormal neuronal activity that is diagnosed visually by analyzing electroencephalography (EEG) signals. BACKGROUND: Surgical operations are the only option for epilepsy treatment when patients are refractory to treatment, which highlights the role of classifying focal and generalized epilepsy syndrome. Therefore, developing a model to be used for diagnosing focal and generalized epilepsy automatically is important. METHODS: A classification model based on longitudinal bipolar montage (LB), discrete wavelet transform (DWT), feature extraction techniques, and statistical analysis in feature selection for RNN combined with long short-term memory (LSTM) is proposed in this work for identifying epilepsy. Initially, normal and epileptic LB channels were decomposed into three levels, and 15 various features were extracted. The selected features were extracted from each segment of the signals and fed into LSTM for the classification approach. RESULTS: The proposed algorithm achieved a 96.1% accuracy, a 96.8% sensitivity, and a 97.4% specificity in distinguishing normal subjects from subjects with epilepsy. This optimal model was used to analyze the channels of subjects with focal and generalized epilepsy for diagnosing purposes, relying on statistical parameters. CONCLUSIONS: The proposed approach is promising, as it can be used to detect epilepsy with satisfactory classification performance and diagnose focal and generalized epilepsy.

Knowledge of Acute Stroke Management Among Healthcare Professionals: Development and Validation of Acute Stroke Management Questionnaire (ASMaQ).

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.

Machine Learning Application: A Bibliometric Analysis From a Half-Century of Research on Stroke.

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.

Machine Learning Models for Predicting Stroke Mortality in Malaysia: An Application and Comparative Analysis.

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.

Qualitative electroencephalogram and its predictors in the diagnosis of stroke.

Introduction: Stroke is a typical medical emergency that carries significant disability and morbidity. The diagnosis of stroke relies predominantly on the use of neuroimaging. Accurate diagnosis is pertinent for management decisions of thrombolysis and/or thrombectomy. Early identification of stroke using electroencephalogram (EEG) in the clinical assessment of stroke has been underutilized. This study was conducted to determine the relevance of EEG and its predictors with the clinical and stroke features. Methods: A cross-sectional study was carried out where routine EEG assessment was performed in 206 consecutive acute stroke patients without seizures. The demographic data and clinical stroke assessment were collated using the National Institutes of Health Stroke Scale (NIHSS) score with neuroimaging. Associations between EEG abnormalities and clinical features, stroke characteristics, and NIHSS scores were evaluated. Results: The mean age of the study population was 64.32 ± 12 years old, with 57.28% consisting of men. The median NIHSS score on admission was 6 (IQR 3-13). EEG was abnormal in more than half of the patients (106, 51.5%), which consisted of focal slowing (58, 28.2%) followed by generalized slowing (39, 18.9%) and epileptiform changes (9, 4.4%). NIHSS score was significantly associated with focal slowing (13 vs. 5, p < 0.05). Type of stroke and imaging characteristics were significantly associated with EEG abnormalities (p < 0.05). For every increment in NIHSS score, there are 1.08 times likely for focal slowing (OR 1.089; 95% CI 1.033, 1.147, p = 0.002). Anterior circulation stroke has 3.6 times more likely to have abnormal EEG (OR 3.628; 95% CI 1.615, 8.150, p = 0.002) and 4.55 times higher to exhibit focal slowing (OR 4.554; 95% CI 1.922, 10.789, p = 0.01). Conclusion: The type of stroke and imaging characteristics are associated with EEG abnormalities. Predictors of focal EEG slowing are NIHSS score and anterior circulation stroke. The study emphasized that EEG is a simple yet feasible investigational tool, and further plans for advancing stroke evaluation should consider the inclusion of this functional modality.

Neurological manifestations in SARS-CoV-2 infection: A single-center cross-sectional study in Malaysia.

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 < 0.001), slurred speech (p < 0.001)]; and encephalitis or encephalopathy [confusion (p < 0.001), forgetfulness (p = 0.006) and seizure (p = 0.019)]. Unvaccinated patients had a 4.25-fold increased risk of having neurological complications (adjusted OR = 4.25; 95% CI: 1.02, 17.71, p = 0.047). Anosmia and dysgeusia were less associated with neurological complications (adjusted OR = 0.22; 95% CI: 0.05, 0.96, p = 0.044). The odds of neurological complications were increased by 18% in patients with leukocytosis (adjusted OR = 1.18, 95% CI: 1.003, p = 0.0460). Conclusions: Stroke, encephalitis and encephalopathy were the common neurological complications from our study. Diabetes mellitus, presence of symptoms of stroke, symptoms of encephalitis or encephalopathy, leukocytosis, and being unvaccinated against COVID-19 were the associated risk factors of developing neurological complications.

Direct Medical Cost of Stroke and the Cost-Effectiveness of Direct Oral Anticoagulants in Atrial Fibrillation-Related Stroke: A Cross-Sectional Study.

BACKGROUND: Stroke has significant direct medical costs, and direct oral anticoagulants (DOACs) are better alternatives to warfarin for stroke prevention in atrial fibrillation (AF). This study aimed to determine the direct medical costs of stroke, with emphasis on AF stroke and the cost-effectiveness of DOACs among stroke patients in a tertiary hospital in Malaysia. METHODS: This study utilised in-patient data from the case mix unit of Universiti Kebangsaan Malaysia Medical Centre (UKMMC) between 2011 and 2018. Direct medical costs of stroke were determined using a top-down costing approach and factors associated with costs were identified. Incremental cost effectiveness ratio (ICER) was calculated to compare the cost-effectiveness between DOACs and warfarin. RESULTS: The direct medical cost of stroke was MYR 11,669,414.83 (n = 3689). AF-related stroke cases had higher median cost of MYR 2839.73 (IQR 2269.79-3101.52). Regression analysis showed that stroke type (AF versus non-AF stroke) (p = 0.013), stroke severity (p = 0.010) and discharge status (p < 0.001) significantly influenced stroke costs. DOACs were cost-effective compared to warfarin with an ICER of MYR 19.25. CONCLUSIONS: The direct medical cost of stroke is substantial, with AF-stroke having a higher median cost per stroke care. DOACs were cost effective in the treatment of AF-related stroke in UKMMC.

Knowledge of acute stroke management and the predictors among Malaysian healthcare professionals.

Background: Despite rapid advances in acute ischaemic stroke (AIS) management, many healthcare professionals (HCPs) might not be aware of the latest recommended management of AIS patients. Therefore, we aimed to determine the level and factors associated with AIS management knowledge among Malaysian HCPs. Methods: This cross-sectional online questionnaire study was conducted nationwide among 627 HCPs in Malaysia using the Acute Stroke Management Questionnaire (ASMaQ). Multiple logistic regression was used to predict the relationship between the independent variables (age, gender, years of service, profession, work setting, work sector, seeing stroke patients in daily practice, and working with specialists) and the outcome variable (good vs poor knowledge). Results: Approximately 76% (95% CI [73-79%]) of HCPs had good overall knowledge of stroke. The highest proportion of HCPs with good knowledge was noted for General Stroke Knowledge (GSK) [88.5% (95% CI [86-91%])], followed by Advanced Stroke Management (ASM) [61.2% (95% CI [57-65%])] and Hyperacute Stroke Management (HSM) [58.1% (95% CI [54-62%])]. The odds of having poor knowledge of stroke were significantly higher among non-doctor HCPs [adjusted OR = 3.46 (95% CI [1.49-8.03]), P = 0.004]; among those not seeing stroke patients in daily practice [adjusted OR = 2.67 (95% CI [1.73-4.10]), P < 0.001]; and among those working without specialists [adjusted OR = 2.41 (95% CI [1.38-4.18]), P = 0.002]. Conclusions: Stroke education should be prioritised for HCPs with limited experience and guidance. All HCPs need to be up-to-date on the latest AIS management and be able to make a prompt referral to an appropriate facility. Therefore, more stroke patients will benefit from advanced stroke care.

Current Update on the Clinical Utility of MMSE and MoCA for Stroke Patients in Asia: A Systematic Review.

OBJECTIVE: Primary care clinicians in Asia employed the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) to aid dementia diagnosis post-stroke. Recent studies questioned their clinical utility in stroke settings for relying on verbal abilities and education level, as well as lack of consideration for aphasia and neglect. We aimed to review the clinical utility of the MMSE and MoCA for stroke patients in Asia and provide recommendations for clinical practice. METHODS: PubMed, Scopus, Web of Science, and Science Direct were searched for relevant articles. Included studies were assessed for risk of bias. RevMan 5.4 was used for data synthesis (sensitivity and specificity) and covariates were identified. RESULTS: Among the 48 full-text articles reviewed, 11 studies were included with 3735 total subjects; of these studies, 7 (77%) were conducted in China, 3 (27%) in Singapore, and 1 (9%) in South Korea. Both the MMSE and MoCA generally showed adequate sensitivity and specificity. Education was identified as a covariate that significantly affected detection accuracy. Due to heterogeneity in cutoff scores, methodologies, and languages, it was not feasible to suggest a single cutoff score. One additional point is recommended for MoCA for patients with <6 years of education. CONCLUSION: Clinicians in Asia are strongly recommended to consider the education level of stroke patients when interpreting the results of the MMSE and MoCA. Further studies in other Asian countries are needed to understand their clinical value in stroke settings.