Predict Stroke Severity Research Articles

Systemic Immune-Inflammation Index (SII) and Neutrophil-to-Lymphocyte Ratio (NLR): A Strong Predictor of Disease Severity in Large-Artery Atherosclerosis (LAA) Stroke Patients.

Systemic immune-inflammation index (SII) and neutrophil-to-lymphocyte ratio (NLR) are novel inflammatory markers based on neutrophil, platelet and lymphocyte counts. Atherosclerosis is a chronic inflammatory vascular disease. This study aimed to verify the predictive value of the clinical parameters such as systemic immune-inflammation index (SII) and neutrophil-to-lymphocyte ratio (NLR) for the severity in Large Artery Atherosclerosis (LAA) stroke patients. The SII is defined as platelet × (neutrophil count/lymphocyte count), the NLR is defined as neutrophil count/lymphocyte count. Univariate logistic regression was used to analyze the association between SII and NLR and NIHSS score in patients with LAA stroke. Multiple logistic regression was used to analyze the risk factors for the severity of LAA stroke. We plotted receiver operating characteristic curves to determine the diagnostic role of SII and NLR in differentiating stroke disease severity. We included 283 LAA stroke patients, the SII and NLR in the moderate-to-severe stroke group were significantly higher than the mild stroke group. Multiple logistic regression analysis showed that SII (OR 1.051 95% CI (1.035-1.066), P < 0.001), NLR (OR 1.077,95% CI (1.032-1.123), P < 0.001) were significantly associated with stroke severity. The SII values under the receiver operating characteristic curve (0.701, 95% CI (0.649-0.791, P < 0.001, cut-off value 912.97) and NLR values under the receiver operating characteristic curve (0.604,5% CI (0.519-0.689), P < 0.01, cut-off value 1.461), and SII values had high discrimination ability. Both SII and NLR had high diagnostic and predictive value for stroke severity, and SII was better than NLR. The higher SII and NLR, the more severity in LAA stroke patients. SII and NLR are independent risk factors for LAA stroke, and they can also effectively predict stroke severity; moreover, SII has a higher diagnostic efficacy than NLR. However, multicenter studies with large sample size are still needed to confirm this conclusion.

Predicting stroke severity of patients using interpretable machine learning algorithms

BackgroundStroke is a significant global health concern, ranking as the second leading cause of death and placing a substantial financial burden on healthcare systems, particularly in low- and middle-income countries. Timely evaluation of stroke severity is crucial for predicting clinical outcomes, with standard assessment tools being the Rapid Arterial Occlusion Evaluation (RACE) and the National Institutes of Health Stroke Scale (NIHSS). This study aims to utilize Machine Learning (ML) algorithms to predict stroke severity using these two distinct scales.MethodsWe conducted this study using two datasets collected from hospitals in Urmia, Iran, corresponding to stroke severity assessments based on RACE and NIHSS. Seven ML algorithms were applied, including K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Artificial Neural Network (ANN). Hyperparameter tuning was performed using grid search to optimize model performance, and SHapley Additive Explanations (SHAP) were used to interpret the contribution of individual features.ResultsAmong the models, the RF achieved the highest performance, with accuracies of 92.68% for the RACE dataset and 91.19% for the NIHSS dataset. The Area Under the Curve (AUC) was 92.02% and 97.86% for the RACE and NIHSS datasets, respectively. The SHAP analysis identified triglyceride levels, length of hospital stay, and age as critical predictors of stroke severity.ConclusionsThis study is the first to apply ML models to the RACE and NIHSS scales for predicting stroke severity. The use of SHAP enhances the interpretability of the models, increasing clinicians’ trust in these ML algorithms. The best-performing ML model can be a valuable tool for assisting medical professionals in predicting stroke severity in clinical settings.

The Role of Troponin Measured on Admission to the Emergency Department in Patients with Acute Ischemic Stroke to Predict Stroke Severity and Neurological Outcome

Objective: This study aims to investigate the prognostic value of cardiac troponin levels measured at admission to the emergency department (ED) for stroke severity assessed by the National Institutes of Health Stroke Scale (NIHSS) and neurological outcomes determined by Modified Rankin Scale (mRS) scores in patients diagnosed with acute ischemic stroke (AIS). Materials and Methods: Patients presenting to ED with a diagnosis of AIS confirmed by neuroimaging findings were included. Patients were divided into 2 groups based on troponin-I levels: elevated and normal. NIHSS during admission, 30-day all-cause mortality data, and 30-day mRS were examined. In comparisons between groups, categorical variables were evaluated with Chi-square and continuous variables were evaluated with the Mann-Whitney-U test. To determine the prognostic value of troponin with poor outcomes in stroke patients, diagnostic 2x2 tables were made. Results: The study was conducted with 200 patients. Troponin elevation was detected in 37, and mortality was significantly higher in this group. The sensitivity of troponin to predict mortality was 88.89%, and PLR (positive likelihood ratio) was 5.85 (3.89–8.79). The 30-day mRS scores were significantly higher in the elevated troponin group. NIHSS scores didn’t show a significant difference between groups. Conclusions: Troponin levels assessed at admission in AIS patients may be a prognostic marker for mortality and adverse neurological outcomes.

The stroke outcome optimization project: Acute ischemic strokes from a comprehensive stroke center

Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. Publicly sharing these datasets can aid in the development of machine learning algorithms, particularly for lesion identification, brain health quantification, and prognosis. These algorithms thrive on large amounts of information, but require diverse datasets to avoid overfitting to specific populations or acquisitions. While there are many large public MRI datasets, few of these include acute stroke. We describe clinical MRI using diffusion-weighted, fluid-attenuated and T1-weighted modalities for 1715 individuals admitted in the upstate of South Carolina, of whom 1461 have acute ischemic stroke. Demographic and impairment data are provided for 1106 of the stroke survivors from this cohort. Our validation demonstrates that machine learning can leverage the imaging data to predict stroke severity as measured by the NIH Stroke Scale/Score (NIHSS). We share not only the raw data, but also the scripts for replicating our findings. These tools can aid in education, and provide a benchmark for validating improved methods.

Evaluating Serum Copeptin as a Promising Biomarker for Predicting Acute Ischaemic Stroke Severity: A Hospital-Based Study on Strokes.

Stroke is the second cause of mortality and the foremost leading cause of disability globally. Many potential biomarkers have been described to contribute to prognosticating the severity in the acute phase of stroke as well as help with risk stratification. Copeptin, an inactive peptide that is produced in an equimolar ratio to arginine vasopressin and adequately mirrors an individual's stress response to acute illnesses like acute ischaemic stroke as evidenced by elevated or increasing levels is being explored in this study to determine its relationship with acute stroke severity and infarct size on admission. This is a cross-sectional study of 80 neuroimaging-confirmed acute ischaemic patients who presented within seven days of symptom onset and 80 control subjects. The ischaemic stroke cases had stroke severity and infarct volume determined on admission by the National Institute of Health Stroke Scale (NIHSS) and neuroimaging (brain CT/MRI). A baseline serum copeptin level was measured in the study subjects. Spearman correlation and Kruskal Wallis test were used to determine the relationship between serum copeptin level with admission NIHSS and infarct size respectively. The receiver operating characteristic (ROC) curve was calculated to determine the sensitivity and specificity of copeptin to predict severity and outcome. The mean age of the study group was 61.3 ± 12.7 years with 55.0% males and 45.0% females. The serum level of copeptin was significantly higher in the stroke cases with a median of 28.6 pmol/L (interquartile range (IQR)- 15.4-31.6 pmol/L) versus 8.8 pmol/L (IQR- 3.2- 10.7 pmol/L) among the stroke-free controls (p= 0.001) at a statistically significant level. There was a weak correlation between copeptin and NIHSS calculated at admission to measure stroke severity (r- 0.02, p= 0.873). Patients with infarct sizes in the fourth quartile (infarct sizes greater than 18.78 cm3) had higher copeptin levels, though this was not statistically significant (H= 2.88; p= 0.410). Admission serum copeptin did not show a statistically significant prognostic value in predicting stroke severity and mortality in stroke patients who presented within seven days of symptomonset with an area under curve (AUC) of 0.51 (95% CI: 0.36-0.65; p= 0.982). In this study, copeptin was higher among the stroke cases compared with the stroke-free controls which suggests a significant prognostic value in risk stratification in the acute phase of stroke; however, this did not significantly correlate with stroke severity and thus warrants further study in this field to elucidate it's fascinating potential as a prognostic biomarker (especially in the acute period) as this may enable allocation of a better-focused therapy for stroke patients.

Modified rankin scale is a reliable tool for the rapid assessment of stroke severity and predicting disability outcomes.

'Time is brain' goes the adage. Rapid and precise management of stroke is of the utmost essence. The modified National Institutes of Health Stroke Scale (mNIHSS) and the modified Rankin Scale (mRS) predict stroke severity and functional disability outcomes. However, the mRS can be administered more rapidly than the mNIHSS and therefore might be better to assess patient outcomes. Hence, the aim of this study was to assess the correlation of stroke severity on admission and functional disability outcomes on the day of discharge or on the 8th day of hospitalization. This was an observational, cross-sectional study with a sample size of 61 participants. The mNIHSS score was calculated on admission for patients with clinical features suggestive of stroke and mRS was calculated on the 8th day of hospitalization or on discharge. Evaluation of the association between continuous variables was done using Spearman's correlation analysis. Correlation between mNIHSS and mRS was positive and statistically significant (rho = 0.866, 95% CI [0.751, 0.925]. For each point increase in the mNIHSS, the odds of having higher mRS scores are 153% more than the odds of having lower mRS scores (aOR = 2.534, 95% CI [1.904, 3.560]). Our study concluded that mRS can be reliably used to predict the functional outcomes for patients with stroke in circumstances where the mNIHSS may prove to be lengthy. Thus, where 'time is brain', the mRS can be used with a similar power to predict the outcome.

Neutrophil Lymphocyte Ratio as a Predictor of Stroke Severity in Type 2 Diabetes Mellitus: A Single-Center Study.

Type 2 diabetes mellitus (T2DM) is associated with various microvascular and macrovascular complications. Stroke, being a vascular complication, is associated with severe morbidity and mortality. Neutrophil lymphocyte ratio (NLR), a crude, inexpensive, and rather easily available modality to detect inflammation, has been utilized to find the extent of inflammation in type 2 diabetes mellitus patients. In this study, we find the effect of hemoglobin A1c (HbA1c) on NLR and the effect of NLR on stroke severity index. This study aims to determine the use of the NLR in predicting stroke severity in a type 2 diabetes mellitus patient. This study is an observational cross-sectional study. A total of 400 patients were enrolled, all of whom had type 2 diabetes mellitus, with 200 of them diagnosed with an ischemic stroke. The National Institute of Health stroke scale (NIHSS) was used to standardize stroke severity and NLR was calculated from differential counts. The mean NLR for patients with type 2 diabetes mellitus was 3.87 ± 0.76 (mean ± SD), while for those with type 2 diabetes mellitus and stroke, it was 7.89 ± 1.29 (mean ± SD), with a statistically significant p-value < 0.001. Additionally, for every 1 unit increase in HbA1c, the NLR increased by 0.38 in type 2 diabetes mellitus patients and 0.86 in type 2 diabetes mellitus patients with stroke. Furthermore, each 1-unit increase in NLR corresponded to a rise of 0.80 in the stroke severity index. The study shows a significant correlation between NLR in type 2 diabetes mellitus patients and stroke in type 2 diabetes mellitus patients. Also, it shows the significance of NLR in predicting stroke severity.

Gender Predicts Differences in Acute Ischemic Cardioembolic Stroke Profile: Emphasis on Woman-Specific Clinical Data and Early Outcome-The Experience of Sagrat Cor Hospital of Barcelona Stroke Registry.

Background and Objectives: Acute ischemic cardioembolic stroke (CS) is a clinical condition with a high risk of death, and can lead to dependence, recurrence, and dementia. Materials and Methods: In this study, we evaluated gender differences and female-specific clinical data and early outcomes in 602 women diagnosed with CS from a total of 4600 consecutive acute stroke patients in a single-center hospital stroke registry over 24 years. A comparative analysis was performed in women and men in terms of demographics, cerebrovascular risk factors, clinical data, and early outcomes. Results: In a multivariate analysis, age, hypertension, valvular heart disease, obesity, and internal capsule location were independent variables associated with CS in women. The overall in-hospital mortality rate was similar, but the group of women had a greater presence of neurological deficits and a higher percentage of severe limitation at hospital discharge. After the multivariate analysis, age, altered consciousness, limb weakness, and neurological, respiratory, gastrointestinal, renal, cardiac and peripheral vascular complications were independent predictors related to early mortality in women. Conclusions: Women with CS showed a differential demographic and clinical profile and worse early outcomes than men. Advanced age, impaired consciousness, and medical complications were predictors of stroke severity in women with CS.

Hybrid deep learning and metaheuristic model based stroke diagnosis system using electroencephalogram (EEG)

Over the last few decades, there has been a significant increase in the average lifespan. Consequently, the number of elderly people suffering from strokes has also risen. As a result, strokes and their treatments have become crucial subjects of research, particularly for the application of machine learning. One of the primary factors in stroke treatment is the speed of response. Currently, both computed tomography (CT) and magnetic resonance imaging (MRI) are used to diagnose strokes. However, CT takes eight hours before an accurate diagnosis can be made, and MRI is expensive and not available in all hospitals. Therefore, there is a growing need for novel approaches to identifying strokes based on electroencephalogram (EEG) signals. In this paper, a hybrid model of deep learning and metaheuristic was developed in the offline stage to classify strokes. Since EEG data is a time series with frequencies, a hybrid model was deemed appropriate. This hybrid model combined a Convolutional Neural Network (CNN) with bidirectional Gated Recurrent Unit (BiGRU). The performance of this model surpassed that of other comparable models. Given the paramount importance of speed and accuracy in this work, the harmony search (HS) algorithm, which is specialized in handling frequencies, was used for feature selection. HS outperformed all similar algorithms when applied to the CNN-BiGRU hybrid model. Additionally, for the optimization of continuous hyperparameters, the multiverse optimization (MVO) algorithm was employed, which proved to be the most effective when compared to another similar algorithm for validation purposes. The new model, CNN-BiGRU-HS-MVO, was applied to analyze the data collected from Al Bashir Hospital using the MUSE-2 portable device, resulting in an impressive prediction accuracy of 99.991%. Moreover, it demonstrated an 11.08% improvement over the results from the paper titled “Predicting stroke severity with a 3-min recording from the Muse portable EEG study”. Furthermore, a decision support system was built on the cloud computing environment based on the hybrid model. This system allows for the diagnosis of patients anytime and from anywhere within minutes, with the authorized person receiving the diagnosis results through SMS notification.

Assessing the Relationship between LAMS and CT Perfusion Parameters in Acute Ischemic Stroke Secondary to Large Vessel Occlusion.

The Los Angeles Motor Scale (LAMS) is a rapid pre-hospital scale used to predict stroke severity which has also been shown to accurately predict large vessel occlusions (LVOs). However, to date there is no study exploring whether LAMS correlates with the computed tomography perfusion (CTP) parameters in LVOs. Patients with LVO between September 2019 and October 2021 were retrospectively reviewed and included if the CTP data and admission neurologic exams were available. The LAMS was documented based on emergency personnel exams or scored retrospectively using an admission neurologic exam. The CTP data was processed by RAPID (IschemaView, Menlo Park, CA, USA) with an ischemic core volume (relative cerebral blood flow [rCBF] < 30%), time-to-maximum (Tmax) volume (Tmax > 6 s delay), hypoperfusion index (HI), and cerebral blood volume (CBV) index. Spearman's correlations were performed between the LAMS and CTP parameters. A total of 85 patients were included, of which there were 9 intracranial internal carotid artery (ICA), 53 proximal M1 branch middle cerebral artery M1, and 23 proximal M2 branch occlusions. Overall, 26 patients had LAMS 0-3, and 59 had LAMS 4-5. In total, LAMS positively correlated with CBF < 30% (Correlation Coefficient (CC): 0.32, p < 0.01), Tmax > 6 s (CC:0.23, p < 0.04), HI (CC:0.27, p < 0.01), and negatively correlated with the CBV index (CC:-0.24, p < 0.05). The relationships between LAMS and CBF were < 30% and the HI was more pronounced in M1 occlusions (CC:0.42, p < 0.01; 0.34, p < 0.01 respectively) and proximal M2 occlusions (CC:0.53, p < 0.01; 0.48, p < 0.03 respectively). The LAMS also correlated with a Tmax > 6 s in M1 occlusions (CC:0.42, p < 0.01), and negatively correlated with the CBV index in M2 occlusions (CC:-0.69, p < 0.01). There were no significant correlations between the LAMS and intracranial ICA occlusions. The results of our preliminary study indicate that the LAMS is positively correlated with the estimated ischemic core, perfusion deficit, and HI, and negatively correlated with the CBV index in patients with anterior circulation LVO, with stronger relationships in the M1 and M2 occlusions. This is the first study showing that the LAMS may be correlated with the collateral status and estimated ischemic core in patients with LVO.

Role of osteoprotegerin rs3102735 gene polymorphism in acute ischemic stroke patients

BackgroundIschemic stroke ranks third among leading causes of death and disability. Both endothelial and vascular smooth muscle cells generate osteoprotegerin (OPG). Ischemic stroke and its severity may be enhanced by the OPG rs3102735 gene polymorphism. Our research aims to investigate OPG rs3102735 gene polymorphism role in ischemic stroke risk and to assess its association with stroke severity at presentation and degree of vascular stenosis and evaluate its potential as a predictor of stroke severity. Fifty people with acute ischemic stroke as well as fifty controls were studied. The NIHSS and ASPECTS were utilized to evaluate stroke severity and the infarction size, respectively. All subjects underwent extracranial carotid duplex study and molecular assessment for genotyping of OPG rs3102735) gene polymorphism.ResultsStroke patients had markedly higher concentrations of OPG in the plasma than controls (311.60 ± 109.48 versus 240.20 ± 75.96 mmol/ml, p = 0.001). The optimal plasma OPG cutoff value for the predicting the occurrence of stroke was determined to be > 250 mmol/ml, the 95% confidence interval (CI) was (0.625–0.843), sensitivity was 68% and specificity was 72%. Ischemic stroke had a significantly different genotype distribution for the OPG rs3102735 gene polymorphism than did controls (36 CC, 13 CT, and 1 TT) versus (28 CC, 15 CT, and 7 TT) respectively. Stroke patients had a significantly greater CC + CT genotype than controls did (P = 0.041), also they had a higher propensity for carrying the C allele than the T allele (P = 0.017). Carotid intima medium thickness and the NIHSS both had positive correlations with OPG serum level (r = 0.39, p = 0.02 and r = 0.4, p = 0.02, respectively), whereas ASPECTS had an inversed correlation (r = − 0.65, p = 0.001).ConclusionsThe current study shows that as an independent risk factor, increased plasma OPG level, may participate in the atherothrombotic ischemic stroke pathophysiology, in addition, genetic variants in the OPG gene (rs3102735) are a separate risk factor for large artery atherosclerosis and plasma OPG level can serve as a biomarker to determine the severity of a stroke.

Hepatic responses following acute ischemic stroke: A clinical research update.

Acute ischemic stroke (AIS) not only affects the brain but also has significant implications for peripheral organs through neuroendocrine regulation. This reciprocal relationship influences overall brain function and stroke prognosis. Recent research has highlighted the importance of poststroke liver changes in determining patient outcomes. In our previous study, we investigated the relationship between stroke and liver function. Our findings revealed that the prognostic impact of stress-induced hyperglycemia in patients undergoing acute endovascular treatment for acute large vessel occlusion is closely related to their preexisting diabetes status. We found that the liver contributes to stress hyperglycemia after AIS by increasing hepatic gluconeogenesis and decreasing hepatic insulin sensitivity. These changes are detrimental to the brain, particularly in patients without diabetes. Furthermore, we examined the role of bilirubin, a byproduct of hepatic hemoglobin metabolism, in stroke pathophysiology. Our results demonstrated that blood bilirubin levels can serve as predictors of stroke severity and may hold therapeutic potential for reducing oxidative stress-induced stroke injury in patients with mild stroke. These results underscore the potential role of the liver in the oxidative stress response following AIS, paving the way for further investigation into liver-targeted therapeutic strategies to improve stroke prognosis and patient outcomes.

Abstract WMP67: Cerebral Collateral Flow State Correlates With Stroke Severity And Functional Outcomes

Introduction: Hypoperfusion index ratio (HIR) measured by computerized tomography perfusion (CTP) has been shown to predict collateral flow state in acute ischemic stroke (AIS). Low HIR (&lt;0.4) is indicative of good collateral flow state. Hypothesis: Good collateral flow state correlates with lower stroke severity and better clinical outcome at discharge. Methods: Data from AIS patients who underwent CTP during initial stroke evaluation were linked with Get With the Guideline database in a comprehensive stroke center between 2018 and 2020. Patients with good collateral flow (HIR&lt;0.4) were compared to those with poor collateral flow (≥0.4) group. They were stratified based on modified Rankin Score (mRS) at discharge into good outcome (mRS 0-2) or poor outcome (mRS 3-6). In addition to demographics, and risk factors data was collected on the initial stroke severity using the NIH stroke scale (NIHSS). Continuous variables were compared using t-test (parametric) and Mann-Whitney U test (non-parametric). Univariate and multivariable analyses (multiple logistic regression) were used to test the association between good collateral flow state and good discharge outcome. Results: CT perfusion data was obtained in 1442 patients, after excluding patients who underwent thrombectomy and all non-vascular etiologies of stroke like presentation, there was 890 patients (age 68±14, 52% male, 50% white, 50% black/others), of who 756 (85%) demonstrated good collateral flow and 134 (15%) showed poor collateral flow. Those with good collateral flow were younger (68±14 vs. 70±15, p=0.07) and lower median NIHSS (6 IQR 3-10 vs. 13 IQR 6-18, p&lt;0.001). Good collateral flow was associated with good outcome on discharge (OR 2.2, 95% CI 1.5-3.3). The association remained significant after adjustment for age, gender, race, hypertension, diabetes, coronary artery disease, congestive heart failure and atrial fibrillation (adjusted OR 2.2 (1.4-3.3). Conclusions: In patients presenting with AIS, good collateral flow state was associated with lower stroke severity. It was independently associated with good outcome after adjustment for several comorbidities. Based on our study results, CT perfusion imaging may have utility in predicting stroke severity and discharge outcome.

Abstract TP41: Assessing The Relationship Between The Los Angeles Motor Scale And Computed Tomography Perfusion Parameters In Acute Ischemic Stroke Secondary To Large Vessel Occlusion

The Los Angeles Motor Scale (LAMS) is a rapid pre-hospital scale used to predict stroke severity. LAMS has also been shown to accurately predict large vessel occlusions (LVOs), however, to date there is no study exploring whether LAMS correlates with CT perfusion (CTP) parameters used to decide mechanical thrombectomy (MT) candidacy. The aim of this study is to elucidate the potential relationship between LAMS and these CTP parameters. We retrospectively reviewed patients with LVO between September 2019 and October 2021. Patients were included if they had available CTP data and neurologic exam on admission. LAMS was documented based on EMS exam or scored retrospectively based on admission neurologic exam. CTP data was processed by RAPID (IschemaView, Menlo Park, CA) with ischemic core volume (rCBF &lt; 30%), Tmax volume (Tmax &gt; 6 second delay), hypoperfusion index (HI), and CBV index. Spearman’s correlations were performed between LAMS and the aforementioned CTP parameters. Eighty-five patients were included, of which 11 had intracranial internal carotid artery (ICA) occlusions, 52 had M1 occlusions, and 22 had proximal M2 occlusions. Of all patients, 26 had LAMS of 0-3, and 59 had LAMS of 4-5. In all cases, LAMS was positively correlated with CBF &lt;30% (Correlation Coefficient (CC): 0.32, p &lt; 0.01), Tmax &gt; 6 s (CC:0.23, p&lt; 0.04), HI (CC:0.27, p &lt; 0.01), and negatively correlated with CBV index (CC:-0.24, p &lt; 0.05). These relationships between LAMS and CBF &lt; 30% and HI were more pronounced in M1 occlusions (CC:0.42, p&lt; 0.01; 0.34, p&lt; 0.01 respectively) and proximal M2 occlusions (CC:0.53, p&lt;0.01; 0.48, p&lt; 0.03 respectively). LAMS also correlated with Tmax &gt; 6 s in M1 occlusions (CC:0.42, p&lt; 0.01), and negatively correlated with CBV index in M2 occlusions (CC:-0.69, p&lt; 0.01). There was no significant correlation between LAMS and CTP parameters in intracranial ICA occlusions. Our results indicate that LAMS is positively correlated with estimated ischemic core, perfusion deficit, and HI, and negatively correlated with CBV index in patients with LVO. Vessel based analyses reveal stronger correlations with M1 and M2 occlusions. This is the first study showing that the LAMS may be correlated with estimated ischemic core and collateral status parameters in patients with LVO.

Abstract TMP72: Multimodal Prediction Of Stroke Severity

Introduction: Predicting individual outcomes post-stroke with the highest possible accuracy is a crucial steppingstone in the realization of precision medicine. We here evaluated various types of lesion information in their capacity to predict stroke severity in a large cohort of patients with acute ischemic stroke. Methods: A total of 1,075 patients of the MRI-GENIE study [age: 64.2(14.7), 38% women] contributed to analyses (N=792 as train and N=283 as test sample). We employed ridge regression with hyperparameter optimization and nested five-fold cross-validation to predict acute NIHSS-based stroke severity. Our baseline model considered DWI lesion volume. Further models tested structural lesion location, indirect structural and functional lesion connectivity, individually and combined with lesion volume. Data was preprocessed by principal component analysis (PCA), retaining 95% of the variance of the original data. Model performance was compared in terms of explained variance (R-squared) and 95% confidence intervals in the outer loop of the training set and the test set. Results: Structural lesion connectivity enabled the highest prediction performance in the test set ( Figure 1 ). Prediction performance did not change notably with inclusion of lesion volume information. This contrasted with the combination of functional lesion connectivity and lesion volume that achieved a substantially higher prediction performance compared to functional lesion connectivity or lesion volume in isolation. Lesion location resulted in a prediction performance that was in the range of lesion volume alone and significantly lower than the ones of structural or functional connectivity with lesion volume. Conclusions: Structural connectivity facilitated the most convincing prediction of stroke severity. The capacity of functional lesion connectivity was substantially improved by the inclusion of lesion volume, indicating both represent complementary information.

Determination of endogenous sphingolipid content in stroke rats and HT22 cells subjected to oxygen-glucose deprivation by LC‒MS/MS

BackgroundStroke is the leading cause of death in humans worldwide, and its incidence increases every year. It is well documented that lipids are closely related to stroke. Analyzing the changes in lipid content in the stroke model after absolute quantification and investigating whether changes in lipid content can predict stroke severity provides a basis for the combination of clinical stroke and quantitative lipid indicators.MethodsThis paper establishes a rapid, sensitive, and reliable LC‒MS/MS analytical method for the detection of endogenous sphingolipids in rat serum and brain tissue and HT22 cells and quantifies the changes in sphingolipid content in the serum and brain tissue of rats from the normal and pMCAO groups and in cells from the normal and OGD/R groups. Using sphingosine (d17:1) as the internal standard, a chloroform: methanol (9:1) mixed system was used for protein precipitation and lipid extraction, followed by analysis by reversed-phase liquid chromatography coupled to triple quadrupole mass spectrometry.ResultsBased on absolute quantitative analysis of lipids in multiple biological samples, our results show that compared with those in the normal group, the contents of sphinganine (d16:0), sphinganine (d18:0), and phytosphingosine were significantly increased in the model group, except sphingosine-1-phosphate, which was decreased in various biological samples. The levels of each sphingolipid component in serum fluctuate with time.ConclusionThis isotope-free and derivatization-free LC‒MS/MS method can achieve absolute quantification of sphingolipids in biological samples, which may also help identify lipid biomarkers of cerebral ischemia.Graphical

Evaluation of Neutrophil-to-Lymphocyte Ratio Among Ischemic Stroke Subtypes and Stroke Severity

Introduction: Recently, neutrophil-to-lymphocyte ratio (NLR) has been proven to be useful in predicting severity, mortality, and morbidity of stroke. NLR is low cost and yet not used widely as a biomarker to predict prognosis. Aims: We evaluated the association of NLR in severity of stroke and ischemic stroke subtypes. Methodology: A total of 69 subjects with ischemic stroke onset within 24 h were included in the study and categorized according to TOAST classification. National Institutes of Health Stroke Scale was used to determine stroke severity at admission. Samples were obtained within 24 h of stroke onset and NLR measured which was later correlated with severity and subtypes of stroke. Results: NLR and severity of stroke showed statistically significant association ( P &lt; .05). Median (interquartile range [IQR]) of NLR was significantly high in severe cases (7.1 [4.03-7.698]), as compared to others. The area under the receiver operating characteristic curve for NLR to predict the stroke severity was 6.07 [area under the curve 0.764; 95% confidence interval: 0.647 to 0.858). When NLR was more than 6.07, there was 90.90% chances of moderate-to-severe stroke and with milder stroke, 93.10% had NLR of less than equal to 6.07. NLR and stroke subtypes also showed statistically significant association ( P &lt; .05). Median (IQR) of NLR in embolic stroke was significantly high (4.75 [2.95-8.2]), as compared to other stroke subtypes. Conclusion: NLR, at a cut-off 6, has moderate sensitivity and higher specificity in predicting stroke severity and NLR was high in embolic stroke among other stroke subtypes.

Abstract TP57: Effects Of Vitamin D Level On Stroke Clinical Severity - A Retrospective Study In Saudi Arabia.

Authors: Faisal Alamri, Seraj Makkawi, Hussain Alqahtani, Amer Alali, Abdullah Alharbi, Maan almaghrabi, Nouf Alghamdi, Esra Aldabbagh, Yahya Samman, Omar Albaradie, Anhar Bawashkhah, Saeed Alghamdi, Muhammad Alsalmi. Background (objectives): Stroke is the most common neurological condition globally, with significant rates of mortality and disability. Many studies focused on vitamin D as a risk factor, but only a limited number of studies focused on vitamin D as a predictor of stroke severity. The purpose of this study was to assess the relationship between serum vitamin D levels and stroke clinical severity. Methodology: A retrospective cohort study was conducted in 2021 in King Abdulaziz Medical City-Jeddah, Saudi Arabia. 774 ischemic stroke patients were screened over the last five years. However, the study enrolled only 372 patients who have their vitamin D baseline level measured. The National Institute of Health Stroke Scale (NIHSS) was assessed at admission to predict stroke clinical severity. The association between vitamin D level and stroke severity was assessed using multivariate logistic regression model. Results: Out of 372 participants, 11% , 22.1%, 66.9% had normal, insufficient, and deficient 25(OH)D serum levels, respectively. The mean age was 68.8 ±12.4 years, and 52.2% were male. The majority of patients (66,9%) had moderate NIHSS. The correlation coefficient test showed a significant inverse relationship between 25(OH)D serum level and NIHSS score (r=-0.117 p-value 0.024). After adjustment of the potential covariates, the study found that the relationship between deficient 25(OH)D serum level and NIHSS score has further strengthened with an OR (CI) for severe NIHSS 3.8 (1.2-4.7) P-value =0.001. Conclusion: Our finding adds to the evidence that vitamin D is a predictor of stroke clinical severity at admission. Limitation: The limitations of this study should be mentioned, NIHSS was calculated using admission notes only.

Association of BIRC5 Gene Polymorphism with the Collateral Circulation and Severity of Large Artery Atherosclerotic Stroke.

Objectives The collateral circulation near the cerebral artery occlusion can contribute to the relief of the symptoms and signs of stroke. Genetic factors play a decisive role in the difference in collateral circulation. Survivin, encoded by the baculoviral inhibitor of apoptosis (IAP) repeat-containing 5 gene (BIRC5), plays an important role in maintaining long-term endothelial integrity and homeostasis and as an angiogenic factor in the treatment of vascular diseases. We hypothesized that genetic variations in the BIRC5 gene may contribute to severity by influencing the collateral circulation. This study aimed at examining how the polymorphism of the BIRC5 gene correlated with the collateral circulation and severity of large artery atherosclerotic stroke. Methods This study enrolled 428 patients with large artery atherosclerotic stroke. There are no statistical differences in age, sex, social behavior, such as smoking and drinking, between the groups classified by the collateral circulation and by the severity of stroke (P > 0.01). Direct sequencing was performed for the genotyping of single nucleotide polymorphism (SNP) of BIRC5 (rs2071214). The enrolled patients were divided into several subgroups based on the collateral flow grading system from the American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN/SIR), the results of the National Institutes of Health Stroke Survey (NIHSS) (6 as a threshold), and the score of the modified Rankin scale (mRS) (for the prediction of prognosis, 2 as a threshold). Differences among subgroups were identified through logistic regression. Results The analysis of collateral circulation revealed the significant correlation of SNP of rs2071214 with the development of poor collateral circulation of large artery atherosclerotic stroke in the additive model (GG vs. AA, odds ratio (OR) = 3.592, 95% confidence interval (CI) = 1.410–9.150, and P=0.007) and the recessive model (GG vs. AA/GA, OR = 3.313, 95% CI = 1.420–7.727, and P=0.006). The analysis of stroke severity exposed the significant role of the SNP of rs2071214 in increasing stroke severity in the dominant model (GA/GG vs. AA, OR = 1.658, 95% CI = 1.017–2.703, and P=0.043) and the additive model (GA vs. AA, OR = 1.717, 95% CI = 1.021–2.888, and P=0.042). However, the analysis of the short-term outcome indicated that three genetic models were not associated with short-term outcomes in the additive model (GA vs. AA, P=0.815, GG vs. AA, and P=0.336), the dominant model (GA/GG vs. AA and P=0.589), and the recessive model (GG vs. AA/GA and P=0.342). Conclusion Our findings identified the SNP of rs2071214 of the BIRC5 gene as a risk factor for the poor compensatory ability of collateral circulation and a predictor of stroke severity in large artery atherosclerotic stroke, which suggested that the SNP of rs2071214 can serve as an innovative therapeutic target for patients with acute ischemic stroke.

Clinicoradiological evaluation of oxidative stress activity in acute cerebral infarction in the first 24 h and the qualitative importance of dysfunctional HDL in stroke.

Acute cerebral infarction (ACI) occurs as a result of instant disruption of vascular flow that causes disbalance between oxidative/antioxidative activity. We examined the relationship of serum neuro-oxidative stress parameters with stroke severity and infarct volume in ACI and emphasized the qualitative importance of high-density lipoprotein (HDL) on its relationship with myeloperoxidase (MPO) and paraoxonase-1 (PON1) in the acute period of stroke. One hundred ACI patients applied within the first 24 h and 50 healthy volunteers were included. The patient group was evaluated with demographic data (including arrival serum biochemical assessment), clinical disability scores, infarct volume, serum oxidative/antioxidative parameters (lipid hydroperoxide (LOOH), MPO, PON1, MPO/PON ratio). The relevant serum parameters were compared with the control group. Dysfunctional HDL measurement was based on detecting dysfunctionality as a result of a high positive correlation between the dysfunctional feature of HDL and the MPO/PON ratio. The correlation of serum parameters, clinical disability score, and infarct volume were evaluated, and independent analyses of variability with comorbidities were performed. A negative correlation between PON1 and arrival NIH score/scale (NIHSS), LOOH and discharge modified rankin scale (mRS), triglyceride level, and infarct volume; a positive correlation between MPO\PON ratio and infarct volume was determined. Logistic regression analyses showed that hypertension, diabetes, and high HbA1C may be predictors of stroke severity, and diabetes mellitus, high HbA1C, infarct volume, and high NIHSS score may be predictors of early disability (p < 0.005). The ROC curve analysis revealed that determining the cut-off value for LOOH is of importance in determining early disability scores (7.2 and 6.2, respectively). The balance between oxidative and antioxidative stress parameters and their quantitative/qualitative changes is of importance, especially in the acute period of ACI. Dysfunctional HDL's evolution and its relationship with other oxidants are significant not only in the cardiovascular aspect but also in the clinicoradiological aspect.