Natriuretic Peptides to Classify Risk of Atrial Fibrillation Detection After Stroke: Analysis of the BIOSIGNAL and PRECISE Cohort Studies.

BACKGROUND AND OBJECTIVES: Prolonged cardiac monitoring (PCM) increases atrial fibrillation (AF) detection after ischemic stroke, but access is limited, and it is burdensome for patients. Our objective was to assess whether midregional proatrial natriuretic peptide (MR-proANP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) could classify people who are unlikely to have AF after ischemic stroke and allow better targeting of PCM. METHODS: We analyzed people from the Biomarker Signature of Stroke Aetiology (BIOSIGNAL) study with ischemic stroke, no known AF, and ≥3 days cardiac monitoring. External validation was performed in the Preventing Recurrent Cardioembolic Stroke: Right Approach, Right Patient (PRECISE) study of 28 days of cardiac monitoring in people with ischemic stroke or transient ischemic attack and no known AF. The main outcome is no AF detection. We assessed the discriminatory value of MR-proANP and NT-proBNP combined with clinical variables to identify people with no AF. A decision curve analysis was performed with combined data to determine the net reduction in people who would undergo PCM using the models based on a 15% threshold probability for AF detection. RESULTS: We included 621 people from the BIOSIGNAL study. The clinical multivariable prediction model included age, NIH Stroke Scale score, lipid-lowering therapy, creatinine, and smoking status. The area under the receiver-operating characteristic curve (AUROC) for clinical variables was 0.68 (95% CI 0.62-0.74), which improved with the addition of log10MR-proANP (0.72, 0.66-0.78; p = 0.001) or log10NT-proBNP (0.71, 0.65-0.77; p = 0.009). Performance was similar for the models with log10MR-proANP vs log10NT-proBNP (p = 0.28). In 239 people from the PRECISE study, the AUROC for clinical variables was 0.68 (0.59-0.76), which improved with the addition of log10NT-proBNP (0.73, 0.65-0.82; p < 0.001) or log10MR-proANP (0.79, 0.72-0.86; p < 0.001). Performance was better for the model with log10MR-proANP vs log10NT-proBNP (p = 0.03). The models could reduce the number of people who would undergo PCM by 30% (clinical and log10MR-proANP), 27% (clinical and log10NT-proBNP), or 20% (clinical only). DISCUSSION: MR-proANP and NT-proBNP help classify people who are unlikely to have AF after ischemic stroke. Measuring MR-proANP or NT-proBNP could reduce the number of people who need PCM by 30%, without reducing the amount of AF detected. TRIAL REGISTRATION INFORMATION: NCT02274727; clinicaltrials.gov/study/NCT02274727.

Urdu translation and cross-cultural validation of the stroke self-efficacy questionnaire.

BACKGROUND: The Stroke Self-Efficacy Questionnaire (SSEQ) measures the self-confidence of the individual in functional activities after a stroke. The SSEQ is a self-report scale with 13 items that assess self-efficacy after a stroke in several functional domains. OBJECTIVE: The purpose was to translate the Stroke Self-Efficacy Questionnaire into Urdu Language and to find out the validity and reliability of Urdu SSEQ among stroke patients. METHODS: The cross-cultural validation study design was used. Following COSMIN guidelines, forward and backward translation protocols were adopted. After pilot testing on 10 stroke patients, the final Urdu version was drafted. A sample of 110 stroke patients was used to evaluate the validity and reliability of the SSEQ-U. Content and Concurrent validity were determined. The intraclass correlation coefficient and Cronbach's alpha were used to measure internal consistency and test-retest reliability. Data analysis was performed using SPSS 25. RESULTS: The final version was drafted after application on 10 stroke patients. Content validity was analyzed by a content validity index ranging from 0.87 to 1. The internal consistency was calculated by Cronbach's alpha (α > 0.80). Test-retest reliability was determined by the Intra-class correlation coefficient (ICC2,1=0.956). Concurrent validity was determined by correlations with other scales by using the Spearman correlation coefficient; moderate to strong correlations (positive and negative) were found with the Functional Independence Measure (r = 0.76), Beck Depression Inventory (r=-0.54), Short Form of 12-item Scale (r = 0.68) and Fall Efficacy Scale (r = 0.82) with p < 0.05. CONCLUSION: The Urdu version was linguistically acceptable and accurate for stroke survivors for determining self-efficacy. It showed good content and concurrent validity, internal consistency and test-retest reliability.

MSE-VGG: A Novel Deep Learning Approach Based on EEG for Rapid Ischemic Stroke Detection.

Ischemic stroke is a type of brain dysfunction caused by pathological changes in the blood vessels of the brain which leads to brain tissue ischemia and hypoxia and ultimately results in cell necrosis. Without timely and effective treatment in the early time window, ischemic stroke can lead to long-term disability and even death. Therefore, rapid detection is crucial in patients with ischemic stroke. In this study, we developed a deep learning model based on fusion features extracted from electroencephalography (EEG) signals for the fast detection of ischemic stroke. Specifically, we recruited 20 ischemic stroke patients who underwent EEG examination during the acute phase of stroke and collected EEG signals from 19 adults with no history of stroke as a control group. Afterwards, we constructed correlation-weighted Phase Lag Index (cwPLI), a novel feature, to explore the synchronization information and functional connectivity between EEG channels. Moreover, the spatio-temporal information from functional connectivity and the nonlinear information from complexity were fused by combining the cwPLI matrix and Sample Entropy (SaEn) together to further improve the discriminative ability of the model. Finally, the novel MSE-VGG network was employed as a classifier to distinguish ischemic stroke from non-ischemic stroke data. Five-fold cross-validation experiments demonstrated that the proposed model possesses excellent performance, with accuracy, sensitivity, and specificity reaching 90.17%, 89.86%, and 90.44%, respectively. Experiments on time consumption verified that the proposed method is superior to other state-of-the-art examinations. This study contributes to the advancement of the rapid detection of ischemic stroke, shedding light on the untapped potential of EEG and demonstrating the efficacy of deep learning in ischemic stroke identification.

Adaptation of risk prediction equations for cardiovascular outcomes among patients with type 2 diabetes in real-world settings: a cross-institutional study using common data model approach.

OBJECTIVE: To adapt risk prediction equations for myocardial infarction (MI), stroke, and heart failure (HF) among patients with type 2 diabetes in real-world settings using cross-institutional electronic health records (EHRs) in Taiwan. METHODS: The EHRs from two medical centers, National Cheng Kung University Hospital (NCKUH; 11,740 patients) and National Taiwan University Hospital (NTUH; 20,313 patients), were analyzed using the common data model approach. Risk equations for MI, stroke, and HF from UKPDS-OM2, RECODe, and CHIME models were adapted for external validation and recalibration. External validation was assessed by (1) discrimination, evaluated by the area under the receiver operating characteristic curve (AUROC) and (2) calibration, evaluated by calibration slopes and intercepts and the Greenwood-Nam-D'Agostino (GND) test. Recalibration was conducted for unsatisfactory calibration (p-value of GND test < 0.05) by adjusting the baseline hazards of original equations to address variations in patients' cardiovascular risks across institutions. RESULTS: The CHIME risk equations had acceptable discrimination (AUROC: 0.71-0.79) and better calibration than that for UKPDS-OM2 and RECODe, although the calibration remained unsatisfactory. After recalibration, the calibration slopes/intercepts of the CHIME-MI, CHIME-stroke, and CHIME-HF risk equations were 0.9848/- 0.0008, 1.1003/- 0.0046, and 0.9436/0.0063 in the NCKUH population and 1.1060/- 0.0011, 0.8714/0.0030, and 1.0476/- 0.0016 in the NTUH population, respectively. All the recalibrated risk equations showed satisfactory calibration (p-values of GND tests ≥ 0.05). CONCLUSIONS: We provide valid risk prediction equations for MI, stroke, and HF outcomes in Taiwanese type 2 diabetes populations. A framework for adapting risk equations across institutions is also proposed.

Days Alive and Out of Hospital as an Outcome Measure in Patients Receiving Hyperacute Stroke Intervention.

BACKGROUND: Patient outcome after stroke is frequently assessed with clinical scales such as the modified Rankin Scale score (mRS). Days alive and out of hospital at 90 days (DAOH-90), which measures survival, time spent in hospital or rehabilitation settings, readmission and institutionalization, is an objective outcome measure that can be obtained from large administrative data sets without the need for patient contact. We aimed to assess the comparability of DAOH with mRS and its relationship with other prognostic variables after acute stroke reperfusion therapy. METHODS AND RESULTS: Consecutive patients with ischemic stroke treated with intravenous thrombolysis or endovascular thrombectomy were analyzed. DAOH-90 was calculated from a national minimum data set, a mandatory nationwide administrative database. mRS score at day 90 (mRS-90) was assessed with in-person or telephone interviews. The study included 1278 patients with ischemic stroke (714 male, median age 70 [59-79], median National Institutes of Health Stroke Scale score 14 [9-20]). Median DAOH-90 was 71 [29-84] and median mRS-90 score was 3 [2-5]. DAOH-90 was correlated with admission National Institutes of Health Stroke Scale score (Spearman rho -0.44, P<0.001) and Alberta Stroke Program Early CT [Computed Tomography] Score (Spearman rho 0.24, P<0.001). There was a strong association between mRS-90 and DAOH-90 (Spearman rho correlation -0.79, P<0.001). Area under receiver operating curve for predicting mRS score >0 was 0.86 (95% CI, 0.84-0.88), mRS score >1 was 0.88 (95% CI, 0.86-0.90) and mRS score >2 was 0.90 (95% CI, 0.89-0.92). CONCLUSIONS: In patients with stroke treated with reperfusion therapies, DAOH-90 shows reasonable comparability to the more established outcome measure of mRS-90. DAOH-90 can be readily obtained from administrative databases and therefore has the potential to be used in large-scale clinical trials and comparative effectiveness studies.

Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis.

Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care.

The role of triglyceride-glucose index in the differential diagnosis of atherosclerotic stroke and cardiogenic stroke.

OBJECTIVE: This study aims to investigate the role of the triglyceride glucose (TyG) index in differentiating cardiogenic stroke (CE) from large atherosclerotic stroke (LAA). METHOD: In this retrospective study, patients with acute ischemic stroke were recruited from the First Affiliated Hospital of Soochow University, Lianyungang Second People's Hospital and Lianyungang First People's Hospital. Their general data, medical history and laboratory indicators were collected and TyG index was calculated. Groups were classified by the TyG index quartile to compare the differences between groups. Logistic regression was utilized to assess the relationship between the TyG index and LAA. The receiver operating characteristic curve (ROC) curve was used to evaluate the diagnostic efficiency of the TyG index in differentiating LAA from CE. RESULT: The study recruited 1149 patients. After adjusting for several identified risk factors, groups TyG-Q2, TyG-Q3, and TyG-Q4 had a higher risk of developing LAA compared to group TyG-Q1(odds ratio (OR) = 1.63,95% confidence interval (CI) = 1.11-2.39, OR = 1.72,95%CI = 1.16-2.55, OR = 2.06,95%CI = 1.36-3.09). TyG has certain diagnostic value in distinguishing LAA from CE(AUC = 0.595, 95%CI0.566-0.623;P<0.001). CONCLUSION: Summarily, the TyG index has slight significance in the identification of LAA and CE; it is particularly a marker for their preliminary identification.

Journal Club: Prehospital Detection of Large Vessel Occlusion Stroke With Electroencephalography: Results of the ELECTRA-STROKE Study.

The ELECTRA-STROKE study investigated the potential of EEG for prehospital triage of patients with ischemic stroke due to large vessel occlusion (LVO), in which fast triage to stroke centers for endovascular treatment is crucial. The study was conducted in 4 phases, and this Journal Club article focuses on the fourth phase in the prehospital setting with suspected stroke patients. An EEG cap with dry electrodes was used to measure brain activity. The main focus was on the diagnostic accuracy of the theta/alpha ratio, which yielded an area under the receiver operator characteristic curve (AUC) of 0.80. Secondary endpoints, particularly the Brain Symmetry Index (a quantified EEG interhemispheric cortical power asymmetry index) in the delta frequency band, showed an AUC of 0.91. Despite the convenient study design and user-friendly EEG device, limitations include a single-arm design, a relatively small sample size, and exclusions due to data quality issues. The usefulness of EEG in the detection of neuronal changes based on brain ischemia was highlighted, but uncertainties remain regarding its use in certain patient groups. The improvements in the Brain Symmetry Index from phase 3 to 4 of the study indicate the potential for further refinement and investigation of combined methods to improve diagnostic accuracy. The study provides insight into the role of EEG in prehospital stroke detection, recognizing both the strengths and limitations. Overall, the study contributes to understanding the promise of EEG in optimizing LVO stroke triage and urges further refinement and exploration of complementary diagnostic approaches.

Metabolic syndrome parameters' variability and stroke incidence in hypertensive patients: evidence from a functional community cohort.

BACKGROUND: Stroke is a common complication of hypertension, but the predictive value of metabolic syndrome parameters' variability on stroke risk in individuals with hypertension remains unclear. Therefore, our objective was to investigate the relationship between metabolic syndrome parameters' variability and the risk of total stroke and its subtypes in hypertensive patients. METHODS: This prospective cohort study included 17,789 individuals with hypertension from the Kailuan study since 2006. Metabolic syndrome parameters, including waist circumference (WC), fasting blood glucose (FBG), systolic blood pressure (SBP), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG), were collected at three follow-up visits in the 2006, 2008, and 2010 surveys. We assess the variability utilizing the coefficient of variation (CV), standard deviation (SD), average real variation (ARV), and variability independent of the mean (VIM), with CV initially assessed. Participants were categorized based on the number of high-variability metabolic syndrome parameters (0, 1, 2, ≥ 3). Stroke cases were identified by reviewing medical records. The associations between variability in metabolic syndrome parameters and the risk of total stroke and its subtypes were analyzed using Cox proportional hazard regression models. RESULTS: During a median follow-up of 9.32 years, 1223 cases of stroke were recorded. Participants with ≥ 3 high-variability metabolic syndrome parameters had an increased risk of total stroke (HR: 1.29, 95%CI 1.09-1.52), as well as an increased risk of ischemic stroke (HR: 1.31, 95%CI 1.05-1.63) compared to those without high-variability parameters. The study also examined variability in each metabolic syndrome parameter, and significant associations with an increased risk of total stroke were observed for variability in SBP (HR: 1.24, 95%CI 1.05-1.46) and HDL-C (HR: 1.34, 95%CI 1.09-1.64). CONCLUSIONS: Long-term fluctuations in metabolic syndrome parameters significantly increase the risk of total stroke, especially ischemic stroke. Maintaining low variability in metabolic syndrome parameters could benefit health, and hypertensive individuals must be regularly monitored.

[Reversible cerebral vasoconstriction syndrome : A rare cause of stroke]. / Reversibles zerebrales Vasokonstriktionssyndrom : Eine seltene Schlaganfallursache.

Reversible cerebral vasoconstriction syndrome (RCVS) is a complex and etiologically diverse neurovascular disorder that typically presents with severe thunderclap headaches (TCH) as the primary symptom, accompanied by reversible vasoconstriction of the cerebral arteries. The clinical course may include focal neurological deficits or epileptic seizures. There are two types: idiopathic RCVS and secondary RCVS, the latter triggered by various substances, medical interventions, or diseases. In clinical practice, various medical specialists may initially encounter this condition, underscoring the importance of accurate recognition and diagnosis of RCVS. The clinical course often appears monophasic and self-limiting, with recurrences reported in only 1.7% of cases annually. Complications such as cerebral hemorrhages and cerebral ischemia can lead to death in 5-10% of cases. This article utilizes a case study to explore RCVS, its complications, and the diagnostic procedures involved.

Role of Stroke Scales and Scores in Cerebrovascular Disease.

This article provides a comprehensive review of widely utilized stroke scales in both routine clinical settings and research. These scales are crucial for planning treatment, predicting outcomes, and helping stroke patients recover. They also play a pivotal role in planning, executing, and comprehending stroke clinical trials. Each scale presents distinct advantages and limitations, and the authors explore these aspects within the article. The authors' intention is to provide the reader with practical insights for a clear understanding of these scales, and their effective use in their clinical practice.

Cerebral arteriopathies of childhood and stroke - A focus on systemic arteriopathies and pediatric fibromuscular dysplasia (FMD).

Systemic vascular involvement in children with cerebral arteriopathies is increasingly recognized and often highly morbid. Fibromuscular dysplasia (FMD) represents a cerebral arteriopathy with systemic involvement, commonly affecting the renal and carotid arteries. In adults, FMD diagnosis and classification typically relies on angiographic features, like the 'string-of-beads' appearance, following exclusion of other diseases. Pediatric FMD (pFMD) is considered equivalent to adult FMD although robust evidence for similarities is lacking. We conducted a comprehensive literature review on pFMD and revealed inherent differences between pediatric and adult-onset FMD across various domains including epidemiology, natural history, histopathophysiology, clinical, and radiological features. Although focal arterial lesions are often described in children with FMD, the radiological appearance of 'string-of-beads' is highly nonspecific in children. Furthermore, children predominantly exhibit intimal-type fibroplasia, common in other childhood monogenic arteriopathies. Our findings lend support to the notion that pFMD broadly reflects an undefined heterogenous group of monogenic systemic medium-or-large vessel steno-occlusive arteriopathies rather than a single entity. Recognizing the challenges in categorizing complex morphologies of cerebral arteriopathy using current classifications, we propose a novel term for describing children with cerebral and systemic vascular involvement: 'cerebral and systemic arteriopathy of childhood' (CSA-c). This term aims to streamline patient categorization and, when coupled with advanced vascular imaging and high-throughput genomics, will enhance our comprehension of etiology, and accelerate mechanism-targeted therapeutic developments. Lastly, in light of the high morbidity in children with cerebral and systemic arteriopathies, we suggest that investigating for systemic vascular involvement is important in children with cerebral arteriopathies.

Automatic gait events detection with inertial measurement units: healthy subjects and moderate to severe impaired patients.

BACKGROUND: Recently, the use of inertial measurement units (IMUs) in quantitative gait analysis has been widely developed in clinical practice. Numerous methods have been developed for the automatic detection of gait events (GEs). While many of them have achieved high levels of efficiency in healthy subjects, detecting GEs in highly degraded gait from moderate to severely impaired patients remains a challenge. In this paper, we aim to present a method for improving GE detection from IMU recordings in such cases. METHODS: We recorded 10-meter gait IMU signals from 13 healthy subjects, 29 patients with multiple sclerosis, and 21 patients with post-stroke equino varus foot. An instrumented mat was used as the gold standard. Our method detects GEs from filtered acceleration free from gravity and gyration signals. Firstly, we use autocorrelation and pattern detection techniques to identify a reference stride pattern. Next, we apply multiparametric Dynamic Time Warping to annotate this pattern from a model stride, in order to detect all GEs in the signal. RESULTS: We analyzed 16,819 GEs recorded from healthy subjects and achieved an F1-score of 100%, with a median absolute error of 8 ms (IQR [3-13] ms). In multiple sclerosis and equino varus foot cohorts, we analyzed 6067 and 8951 GEs, respectively, with F1-scores of 99.4% and 96.3%, and median absolute errors of 18 ms (IQR [8-39] ms) and 26 ms (IQR [12-50] ms). CONCLUSIONS: Our results are consistent with the state of the art for healthy subjects and demonstrate a good accuracy in GEs detection for pathological patients. Therefore, our proposed method provides an efficient way to detect GEs from IMU signals, even in degraded gaits. However, it should be evaluated in each cohort before being used to ensure its reliability.

Is Stroke risk analysis (SRA) a reliable method for predicting atrial fibrillation? A systematic review.

INTRODUCTION: Atrial fibrillation is responsible for a considerable number of cases of cardioembolism, accounting for 17% to 30% of the etiologies of all strokes. The software known as Stroke Risk Analysis (SRA) detects patients at high risk of paroxysmal atrial fibrillation by analyzing a continuous electrocardiogram recorded over different periods of time. OBJECTIVES: This article aims to carry out a systematic review investigating the effectiveness of the SRA method in predicting the risk of stroke patients having paroxysmal atrial fibrillation as the cause of the event. METHODS: The methods correspond to the format of the International Prospective Register of Systematic Reviews Protocol, according to CRD Identification Code: CRD42021253974. A systematic search was carried out in BMJB, PubMed/MEDLINE, Science Direct and LILACS. Six cohort studies met the inclusion criteria, representing a total of 2,088 participants with stroke, and compared the detection of patients with paroxysmal atrial fibrillation on the continuous recording electrocardiogram with a time variation of 1 to 48h with the use of SRA. RESULTS: Studies have shown that SRA has a high negative predictive value (between 96 and 99.1%) and can contribute to the selection of patients at high risk of paroxysmal atrial fibrillation to be referred for implantable cardiac monitoring to continue the investigation. CONCLUSIONS: A sequential combination of SRA with implantable cardiac monitoring is a promising strategy for detecting undiagnosed paroxysmal atrial fibrillation. Thus, the SRA can act as a cost-effective pre-selection tool to identify patients at higher risk of having paroxysmal atrial fibrillation as a possible cause of stroke and who may benefit from implantable cardiac monitoring. However, the lack of randomized studies is a limitation that must be considered.

Association of Life's essential 8 score with the risk of all-cause mortality and cardio-cerebrovascular mortality in patients with stroke.

BACKGROUND: A higher Life's Essential 8 (LE8)-based cardiovascular health (CVH) has been reported to be associated with a lower risk of both all-cause mortality and cardio-cerebrovascular diseases (CCVDs) related mortality in adults in the United States. At the same time, multiple studies have shown a significant negative association of CVH with the risk of stroke and CCVDs. Since no research has investigated the applicability of the LE8 in stroke patients, this study aimed to explore the association of LE8 with all-cause mortality and cardio-cerebrovascular mortality in stroke patients. METHODS: Data of patients were extracted from the National Health and Nutrition Examination Surveys (NHANES) database in 2007-2018 in this retrospective cohort study. Weighted univariate and multivariate COX regression analyses were utilized to investigate the associations of LE8 with all-cause mortality and cardio-cerebrovascular mortality. We further explored these relationships in subgroups of age, gender, body mass index (BMI), cancer, congestive heart failure (CHF), and coronary heart disease (CHD). The evaluation indexes were hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: Among the eligible patients, 278 died from all-cause and 89 (8.38%) of them died due to CCVDs. After adjusting for covariates, patients with LE8 score ≥ 58.75 seemed to have both lower risk of all-cause mortality (HR = 0.46, 95%CI: 0.31-0.69) and cardio-cerebrovascular mortality (HR = 0.51, 95%CI: 0.26-0.98), comparing to those with LE8 score < 48.123. Higher LE8 scores were associated with lower risk of all-cause mortality in patients aged < 65 years old, without cancer, and whatever the gender, BMI, CHF or CHD conditions (all P < 0.05). The relationships between high LE8 scores and low cardio-cerebrovascular mortality risk were only found in age < 65 years old and non-cancer subgroups (all P < 0.05). CONCLUSION: A higher LE8 score was associated with lower risk of both all-cause mortality and cardio-cerebrovascular mortality in patients with stroke, which may provide some reference for risk management and prognosis improvement in stoke. However, more evidences are needed to verify this beneficial role of high LE8 score in stroke prognosis.

Systematic Review and Meta-Analysis of Prehospital Machine Learning Scores as Screening Tools for Early Detection of Large Vessel Occlusion in Patients With Suspected Stroke.

BACKGROUND: Enhanced detection of large vessel occlusion (LVO) through machine learning (ML) for acute ischemic stroke appears promising. This systematic review explored the capabilities of ML models compared with prehospital stroke scales for LVO prediction. METHODS AND RESULTS: Six bibliographic databases were searched from inception until October 10, 2023. Meta-analyses pooled the model performance using area under the curve (AUC), sensitivity, specificity, and summary receiver operating characteristic curve. Of 1544 studies screened, 8 retrospective studies were eligible, including 32 prehospital stroke scales and 21 ML models. Of the 9 prehospital scales meta-analyzed, the Rapid Arterial Occlusion Evaluation had the highest pooled AUC (0.82 [95% CI, 0.79-0.84]). Support Vector Machine achieved the highest AUC of 9 ML models included (pooled AUC, 0.89 [95% CI, 0.88-0.89]). Six prehospital stroke scales and 10 ML models were eligible for summary receiver operating characteristic analysis. Pooled sensitivity and specificity for any prehospital stroke scale were 0.72 (95% CI, 0.68-0.75) and 0.77 (95% CI, 0.72-0.81), respectively; summary receiver operating characteristic curve AUC was 0.80 (95% CI, 0.76-0.83). Pooled sensitivity for any ML model for LVO was 0.73 (95% CI, 0.64-0.79), specificity was 0.85 (95% CI, 0.80-0.89), and summary receiver operating characteristic curve AUC was 0.87 (95% CI, 0.83-0.89). CONCLUSIONS: Both prehospital stroke scales and ML models demonstrated varying accuracies in predicting LVO. Despite ML potential for improved LVO detection in the prehospital setting, application remains limited by the absence of prospective external validation, limited sample sizes, and lack of real-world performance data in a prehospital setting.

Associations of Plasma Metabolites With Risks of Incident Stroke and Its Subtypes in Chinese Adults.

BACKGROUND: Metabolomics studies have identified various metabolic markers associated with stroke risk, yet much uncertainty persists regarding heterogeneity in these associations between different stroke subtypes. We aimed to examine metabolic profiles associated with incident stroke and its subtypes in Chinese adults. METHODS AND RESULTS: We performed a nested case-control study within the Dongfeng-Tongji cohort, including 1029 and 266 incident cases of ischemic stroke (IS) and hemorrhagic stroke (HS), respectively, with a mean follow-up period of 6.1±2.3 years. Fifty-five metabolites in fasting plasma were measured by ultra-high-performance liquid chromatography-mass spectrometry. We examined the associations of metabolites with the risks of total stroke, IS, and HS, with a focus on the comparison of associations of plasma metabolite with IS and HS, using conditional logistic regression. We found that increased levels of asymmetrical/symmetrical dimethylarginine and glutamate were significantly associated with elevated risk of total stroke (odds ratios and 95%, 1.20 [1.08-1.34] and 1.22 [1.09-1.36], respectively; both Benjamini-Hochberg-adjusted P <0.05). When examining stroke subtypes, asymmetrical/symmetrical dimethylarginine was nominally associated with both IS and HS (odds ratios [95% CIs]: 1.16 [1.03-1.31] and 1.39 [1.07-1.81], respectively), while glutamate was associated with only IS (odds ratios [95% CI]: 1.26 [1.11-1.43]). The associations of glutamate with IS risk were significantly stronger among participants with hypertension and diabetes than among those without these diseases (both P for interaction <0.05). CONCLUSIONS: This study validated the positive associations of asymmetrical/symmetrical dimethylarginine and glutamate with stroke risk, mainly that of IS, in a Chinese population, and revealed a novel unanimous association of with both IS and HS. Our findings provided potential intervention targets for stroke prevention.

Association between atherogenic index of plasma and new-onset stroke in individuals with different glucose metabolism status: insights from CHARLS.

BACKGROUND: Circulating atherogenic index of plasma (AIP) levels has been proposed as a novel biomarker for dyslipidemia and as a predictor of insulin resistance (IR) risk. However, the association between AIP and the incidence of new-onset stroke, particularly in individuals with varying glucose metabolism status, remains ambiguous. METHODS: A total of 8727 participants aged 45 years or older without a history of stroke from the China Health and Retirement Longitudinal Study (CHARLS) were included in this study. The AIP was calculated using the formula log [Triglyceride (mg/dL) / High-density lipoprotein cholesterol (mg/dL)]. Participants were divided into four groups based on their baseline AIP levels: Q1 (AIP ≤ 0.122), Q2 (0.122 < AIP ≤ 0.329), Q3 (0.329 < AIP ≤ 0.562), and Q4 (AIP > 0.562). The primary endpoint was the occurrence of new-onset stroke events. The Kaplan-Meier curves, multivariate Cox proportional hazard models, and Restricted cubic spline analysis were applied to explore the association between baseline AIP levels and the risk of developing a stroke among individuals with varying glycemic metabolic states. RESULTS: During an average follow-up of 8.72 years, 734 participants (8.4%) had a first stroke event. The risk for stroke increased with each increasing quartile of baseline AIP levels. Kaplan-Meier curve analysis revealed a significant difference in stroke occurrence among the AIP groups in all participants, as well as in those with prediabetes mellitus (Pre-DM) and diabetes mellitus (DM) (all P values < 0.05). After adjusting for potential confounders, the risk of stroke was significantly higher in the Q2, Q3, and Q4 groups than in the Q1 group in all participants. The respective hazard ratios (95% confidence interval) for stroke in the Q2, Q3, and Q4 groups were 1.34 (1.05-1.71), 1.52 (1.19-1.93), and 1.84 (1.45-2.34). Furthermore, high levels of AIP were found to be linked to an increased risk of stroke in both pre-diabetic and diabetic participants across all three Cox models. However, this association was not observed in participants with normal glucose regulation (NGR) (p > 0.05). Restricted cubic spline analysis also demonstrated that higher baseline AIP levels were associated with higher hazard ratios for stroke in all participants and those with glucose metabolism disorders. CONCLUSIONS: An increase in baseline AIP levels was significantly associated with the risk of stroke in middle-aged and elderly individuals, and exhibited distinct characteristics depending on the individual's glucose metabolism status.

Pre-Hospital Stroke Care beyond the MSU.

PURPOSE OF REVIEW: Mobile stroke units (MSU) have established a new, evidence-based treatment in prehospital stroke care, endorsed by current international guidelines and can facilitate pre-hospital research efforts. In addition, other novel pre-hospital modalities beyond the MSU are emerging. In this review, we will summarize existing evidence and outline future trajectories of prehospital stroke care & research on and off MSUs. RECENT FINDINGS: The proof of MSUs' positive effect on patient outcomes is leading to their increased adoption in emergency medical services of many countries. Nevertheless, prehospital stroke care worldwide largely consists of regular ambulances. Advancements in portable technology for detecting neurocardiovascular diseases, telemedicine, AI and large-scale ultra-early biobanking have the potential to transform prehospital stroke care also beyond the MSU concept. The increasing implementation of telemedicine in emergency medical services is demonstrating beneficial effects in the pre-hospital setting. In synergy with telemedicine the exponential growth of AI-technology is already changing and will likely further transform pre-hospital stroke care in the future. Other promising areas include the development and validation of miniaturized portable devices for the pre-hospital detection of acute stroke. MSUs are enabling large-scale screening for ultra-early blood-based biomarkers, facilitating the differentiation between ischemia, hemorrhage, and stroke mimics. The development of suitable point-of-care tests for such biomarkers holds the potential to advance pre-hospital stroke care outside the MSU-concept. A multimodal approach of AI-supported telemedicine, portable devices and blood-based biomarkers appears to be an increasingly realistic scenario for improving prehospital stroke care in regular ambulances in the future.