Outcome In Ischemic Stroke Patients Research Articles

Effect of frequent blood pressure measurements after discharge on clinical outcomes in ischemic stroke patients.

Effect of frequent blood pressure measurements after discharge on clinical outcomes in ischemic stroke patients.

Predictors of good functional outcome in ischaemic stroke patients without delayed neurological improvement after mechanical thrombectomy.

This study aimed to identify predictors of 90-day good functional outcome (GFO) in patients with acute ischaemic stroke (AIS) who were treated with mechanical thrombectomy but did not achieve a delayed neurological improvement (DNI). In-hospital neurological improvement in patients with AIS is consistently associated with long- -term GFO. Patients who experience neither early nor delayed neurological improvement can still achieve long-term GFO, but predictors of such an outcome have not been studied. This single-centre retrospective study involved 307 patients with anterior circulation AIS treated with mechanical thrombectomy. Multiple clinical, biochemical, radiological, and treatment-related variables were collected and analysed. DNI on day 7 was defined as at least a 10-point reduction in the National Institutes of Health Stroke Scale (NIHSS) score or NIHSS score < 2. GFO on day 90 was defined as a modified Rankin Scale (mRS) score ≤ 2. We compared the characteristics of patients with and without DNI, with special attention paid to patients who achieved 90-GFO despite a lack of DNI. Multivariate analyses were then performed to establish independent predictors of 90-day GFO among patients without DNI. DNI occurred in 150 out of 307 patients (48.7%) and significantly increased the odds for 90-day GFO (odds ratio [OR]: 13.99; p < 0.001). Among patients without DNI, 41.4% achieved 90-day GFO. Younger age (OR: 0.96; 95% confidence interval [CI]: 0.93-0.99; p = 0.008), lower baseline NIHSS score (OR: 0.80; 95% CI: 0.73-0.89; p < 0.001), treatment with intravenous thrombolysis (OR: 3.06; 95% CI: 1.25-7.49; p = 0.014), lack of an undetermined aetiology (OR: 0.40; 95% CI: 0.16-0.998; p = 0.050), lack of pneumonia (OR: 0.08; 95% CI: 0.02-0.31; p < 0.001), and higher haemoglobin concentration on admission (OR: 1.31; 95% CI: 1.04-1.69; p = 0.024) were identified as predictors of 90-day GFO in this subgroup. Almost half of patients with AIS in anterior circulation treated with mechanical thrombectomy experience DNI, which is a good predictor of 90-day GFO. Furthermore, 40% of patients without DNI achieve 90-day GFO which can be independently predicted by younger age, lower baseline NIHSS score, treatment with intravenous thrombolysis, higher haemoglobin concentration on admission, lack of undetermined ischaemic stroke aetiology, and lack of pneumonia.

The utility of serum glucose potassium ratio as a predictive factor for haemorrhagic transformation, stroke recurrence, and mortality among ischemic stroke patients

Background and ObjectiveGlucose-Potassium Ratio (GPR) has emerged as a biomarker in several pathophysiological conditions. However, the association between GPR and long-term outcomes in stroke patients has not been investigated. Our study evaluated the applicability of baseline GPR as a predictive prognostic tool for clinical outcomes in ischemic stroke patients. MethodsThe multicenter retrospective cohort study included acute-subacute adult ischemic stroke patients who had their baseline serum GPR levels measured. Eligible patients were categorized into two sub-cohorts based on the baseline GPR levels (<1.67 vs. ≥ 1.67). The primary outcome was the incidence of 30-day hemorrhagic transformation, while stroke recurrence, and all-cause mortality within twelve months, were considered secondary. ResultsAmong 4083 patients screened, 1047 were included in the current study. In comparison with GPR < 1.67 group, patients with ≥ 1.67 GPR had a significantly higher ratio of all-cause mortality within twelve months (aHR 2.07 [95 % CI 1.21–3.75] p = 0.01), and higher ratio of 30-day hemorrhagic transformation but failed to reach the statistical significance (aHR 1.60 [95 % CI 0.95–2.79], p = 0.08). ConclusionOverall, baseline GPR serum is an independent predictor of all-cause mortality within twelve months in patients with acute and subacute ischemic stroke. Further clinical studies are necessary to validate these findings.

Prediction of prognosis in patients with ischemic stroke combined with type 2 diabetes mellitus based on serum total bilirubin levels.

Serum bilirubin levels may have therapeutic benefits in oxidative stress-related diseases, but their role in stroke remains unclear. This study aimed to investigate the relationship between serum bilirubin levels on admission and clinical outcomes in ischemic stroke patients. We prospectively collected data from consecutive ischemic stroke admissions. Serum total bilirubin (TBIL) and direct bilirubin levels on admission were measured. Stroke severity at admission was assessed using the National Institutes of Health Stroke Scale, and functional status at discharge was evaluated using the modified Rankin scale. Among 180 patients, lower TBIL levels were observed in all 3 groups, with the mild group (7.89 ± 2.12 μmol/L) having lower levels than the moderate group (8.01 ± 2.12 μmol/L) and the severe group (9.12 ± 2.12 μmol/L). Although TBIL levels were initially associated with stroke severity, this relationship did not hold after adjusting for confounding factors. Serum bilirubin levels appear to be related to stroke severity but not independently associated with outcomes in ischemic stroke patients. Further research is needed to understand the underlying mechanisms of this relationship. There is a strong correlation between serum bilirubin levels and the severity and prognosis of ischemic stroke in patients with type 2 diabetes. Therefore, early control of serum TBIL and direct bilirubin is crucial for the treatment and prognosis of ischemic stroke in patients with type 2 diabetes.

Dyslipidemia versus obesity as predictors of ischemic stroke prognosis: a multi-center study in China

BackgroundThis multicenter observational study aimed to determine whether dyslipidemia or obesity contributes more significantly to unfavorable clinical outcomes in patients experiencing a first-ever ischemic stroke (IS).MethodsThe study employed a machine learning predictive model to investigate associations among body mass index (BMI), body fat percentage (BFP), high-density lipoprotein (HDL), triglycerides (TG), and total cholesterol (TC) with adverse outcomes in IS patients. Extensive real-world clinical data was utilized, and risk factors significantly linked to adverse outcomes were identified through multivariate analysis, propensity score matching (PSM), and regression discontinuity design (RDD) techniques. Furthermore, these findings were validated via a nationwide multicenter prospective cohort study.ResultsIn the derived cohort, a total of 45,162 patients diagnosed with IS were assessed, with 522 experiencing adverse outcomes. A multifactorial analysis incorporating PSM and RDD methods identified TG (adjusted odds ratio (OR) = 1.110; 95% confidence interval (CI): 1.041–1.183; P < 0.01) and TC (adjusted OR = 1.139; 95%CI: 1.039–1.248; P < 0.01) as risk factors. However, BMI, BFP, and HDL showed no significant effect. In the validation cohort, 1410 controls and 941 patients were enrolled, confirming that lipid levels are more strongly correlated with the prognosis of IS patients compared to obesity (TC, OR = 1.369; 95%CI: 1.069–1.754; P < 0.05; TG, OR = 1.332; 95%CI: 1.097–1.618; P < 0.01).ConclusionThis study suggests that dyslipidemia has a more substantial impact on the prognosis of IS patients compared to obesity. This highlights the importance of prioritizing dyslipidemia management in the treatment and prevention of adverse outcomes in IS patients.Graphical abstract

Patterns of atrial fibrillation, relevant cardiac structural and functional changes predict functional and cognitive outcomes in patients with ischemic stroke and atrial fibrillation

BackgroundAtrial fibrillation (AF) pattern, relevant cardiac changes are important predictors of outcomes in AF, but their impact on patients with ischemic stroke and AF remained unclear. We aimed to explore the impact of AF patterns, cardiac structural and functional markers on long-term functional and cognitive outcomes in ischemic stroke patients with AF. MethodsIschemic stroke patients diagnosed with AF were enrolled in this retrospective cohort study. AF pattern was defined by both traditional and novel classification, in which patients were divided into AF diagnosed after stroke (AFDAS) and known before stroke (KAF). Left atrial (LA) diameter, left ventricular ejection fraction (LVEF), natriuretic peptide (BNP) and cardiac troponin (cTnI) were dichotomized according to the median value. Outcomes include poor functional outcome and cognitive impairment at the 1-year follow-up. Multivariable logistic regression was performed to validate the association between AF pattern, parameters of cardiac change and functional and cognitive outcome. ResultsA total of 377 patients were included. Non-paroxysmal AF patients had a higher risk of poor functional outcome (OR = 3.59, P < 0.0001) and cognitive impairment (OR = 2.38, P = 0.019) than paroxysmal AF patients, while there were no differences between AFDAS and KAF. Lower LVEF (OR = 1.83, P = 0.045) and higher BNP (OR = 2.66, P = 0.001) were associated with poor functional outcome. Lower LVEF (OR = 2.86, P = 0.004), higher LA diameter (OR = 2.72, P = 0.008) and BNP (OR = 2.31, P = 0.023) were associated with cognitive impairment. ConclusionsAF type and related cardiac markers can serve as predictors for poor functional and cognitive outcomes. Comprehensive cardiac assessment and monitoring should be strengthened after stroke.

Prognostic significance of serum cystatin C in acute brainstem infarctions patients

ObjectivesSome studies show that high circulating cystatin C (CysC) may predict cardiovascular events and death after ischemic stroke onset. However, the association between serum CysC and outcome in ischemic stroke patients remains contradictory. We sought to assess the association between a specific stroke subgroup, brainstem infarction (BSI) and serum CysC. Materials and methodsA total of 324 acute BSI patients were included in the study. Serum CysC was used to calculate estimated glomerular filtration rate (eGFRCysC) at baseline. Modified Rankin scale score ((mRS) ≥3) six months after acute BSI indicates poor functional outcome. Patients were categorized into two groups according to mRS and eGFRCysC. Logistic regression analyses were performed to determine independent risk factors. ResultsLower eGFRCysC was associated with hemoglobin A1c (HbA1c). This risk remained statistically significant after controlling for age, hypertension, initial National Institutes of Health Stroke Scale (NIHSS) score, HbA1c, fibrinogen and homocysteine. The serum eGFRCysC levels were significantly lower in the poor functional outcome group than the good functional outcome group (P<0.001). Multivariate logistic regression analyses showed that eGFRCysC level was significantly lower in the poor outcome group after adjusting for age, previous infarctions, initial NIHSS score, and HbA1c. ConclusionsLower eGFRCysC levels were strongly associated with poor functional outcome of acute BSI patients with a higher HbA1c level. Lower eGFRCysC may be a more helpful serologic biomarker for the prediction of prognosis in BSI.

Predictive value of fibrinogen for early neurological deterioration in large-artery atherosclerotic cerebral infarction

Background &amp; Objective: To evaluate the predictive value of fibrinogen at admission for early neurological deterioration (END) in patients with large artery atherosclerotic (LAA) stroke without reperfusion therapy. Fibrinogen, a glycoprotein crucial for clotting, is associated with worse outcomes in ischemic stroke patients. While its connection with thrombolytic therapy’s prognosis is recognized, its predictive role for non-thrombolytic-treated patients is less clear. The objective of this study is to evaluate the predictive value of fibrinogen at admission for early neurological deterioration (END) in patients with large artery atherosclerotic (LAA) stroke without reperfusion therapy. Methods: This retrospective case-control study included consecutive inpatients with LAA stroke admitted within 72 h of onset at the Fifth Affiliated Hospital of Sun Yat-Sen University between January 2021 and December 2021. An increase of &gt;2 in the National Institutes of Health Stroke Scale (NIHSS) within 7 days after admission was defined as END. Results: This study included 179 patients (121 males); the mean age was 62.67±13.51 years. There were 42 and 137 patients in the END and non-END groups. The NIHSS score at admission was higher in the END group than in the non-END group, the fibrinogen levels were higher, and the uric acid levels were lower (all P&lt;0.05). NIHSS at admission (OR=1.086, 95%CI: 1.001-1.179), fibrinogen levels (OR=2.182, 95%CI: 1.286- 3.702), and uric acid levels (OR=0.995, 95%CI=0.990-0.999) were independently associated with END. Receiver-operating characteristic curve analysis suggested that the sensitivity, specificity, and area under the curve for fibrinogen for END were 62.9%, 78.1%, and 0.730 (95% CI: 0.532-0.828, P&lt;0.009), respectively. Conclusions: Elevated fibrinogen is independently associated with END in patients with LAA stroke without reperfusion therapy.

Value of Automatically Derived Full Thrombus Characteristics: An Explorative Study of Their Associations with Outcomes in Ischemic Stroke Patients.

(1) Background: For acute ischemic strokes caused by large vessel occlusion, manually assessed thrombus volume and perviousness have been associated with treatment outcomes. However, the manual assessment of these characteristics is time-consuming and subject to inter-observer bias. Alternatively, a recently introduced fully automated deep learning-based algorithm can be used to consistently estimate full thrombus characteristics. Here, we exploratively assess the value of these novel biomarkers in terms of their association with stroke outcomes. (2) Methods: We studied two applications of automated full thrombus characterization as follows: one in a randomized trial, MR CLEAN-NO IV (n = 314), and another in a Dutch nationwide registry, MR CLEAN Registry (n = 1839). We used an automatic pipeline to determine the thrombus volume, perviousness, density, and heterogeneity. We assessed their relationship with the functional outcome defined as the modified Rankin Scale (mRS) at 90 days and two technical success measures as follows: successful final reperfusion, which is defined as an eTICI score of 2b-3, and successful first-pass reperfusion (FPS). (3) Results: Higher perviousness was significantly related to a better mRS in both MR CLEAN-NO IV and the MR CLEAN Registry. A lower thrombus volume and lower heterogeneity were only significantly related to better mRS scores in the MR CLEAN Registry. Only lower thrombus heterogeneity was significantly related to technical success; it was significantly related to a higher chance of FPS in the MR CLEAN-NO IV trial (OR = 0.55, 95% CI: 0.31-0.98) and successful reperfusion in the MR CLEAN Registry (OR = 0.88, 95% CI: 0.78-0.99). (4) Conclusions: Thrombus characteristics derived from automatic entire thrombus segmentations are significantly related to stroke outcomes.

Abstract TP144: Deep Learning on Pre-Procedural CT and Clinical Data Predicts Outcome Following Stroke Thrombectomy

Background: Deep learning using clinical and imaging data may improve pre-treatment prognostication in ischemic stroke patients undergoing endovascular thrombectomy (EVT). Methods: Deep learning models were trained and tested on pre-treatment clinical and imaging (CT head and CT angiography) data to predict 3-month functional outcomes in ischemic stroke patients who underwent EVT. Logistic regression models were constructed to compare their performance with the deep learning models. An external validation dataset was used to validate the models. The MR PREDICTS prognostic tool was tested on the external validation set, and its performance was compared with the deep learning and logistic regression models. Results: There were a total of 975 patients (550 men; mean±SD age 67.5±15.1 years), including 778 patients from the model development cohort, and 197 patients in the external validation cohort. The deep learning model trained on CT head and clinical data, and the logistic regression model (clinical data alone) demonstrated the strongest discriminative abilities for 3-month functional outcome and were comparable (AUC 0.811 versus 0.817, Q=0.82). Both models exhibited superior prognostic performance than the other deep learning (CT head alone, CT angiography alone) and MR PREDICTS models (all Q&lt;0.05). Conclusions: The discriminative performance of deep learning for predicting functional outcome following EVT was comparable to logistic regression. Future studies should focus on whether incorporating procedural and post-procedural data significantly improves model performance.

Abstract WP10: Impact of Dexmedetomidine in Neurocritical Care on Outcomes of Ischemic Stroke Patients With Large Vessel Occlusion

Introduction: While observational data suggest that dexmedetomidine may have neuroprotective effects on ischemic brain, its clinical use in acute ischemic stroke is limited due to concerns about cerebral vasoconstriction. We aimed to examine the impact of dexmedetomidine on outcomes in patients with anterior circulation large vessel occlusion (acLVO) requiring neurocritical care after endovascular therapy (EVT). Methods: We extracted data from our stroke registry for consecutive patients with acLVO who underwent EVT and subsequent neurocritical care (12/2019-12/2022). Patients were categorized based on dexmedetomidine treatment. Intravenous dexmedetomidine was given alone or adjunctively for endotracheal tube intolerance, withdrawal symptoms, sedation transition and delirium. Primary outcomes were favorable functional status (mRS≤2) and mortality at 90 days. Results: Of 402 patients, 76 (18.9%) received dexmedetomidine after a median of 4 [3] days post-stroke. Dexmedetomidine-treated patients were more often male (63.2% vs. 45.4%; p=0.01), had higher baseline NIHSS scores (17 [7] vs. 15 [9.5]; p=0.001), lower baseline ASPECTS (7 [3] vs. 8 [2]; p&lt;0.001) and higher diabetes prevalence (45.9% vs. 32.6%; p=0.045) compared to patients not exposed to dexmedetomidine. While favorable functional outcome was less frequently observed in the dexmedetomidine than in the comparator group (7.9% vs. 32.5%; p&lt;0.001), there was no difference in 90-days mortality (40.8% vs. 28.8%; p=0.24). Adjusting for age, baseline NIHSS, IV thrombolysis, post-EVT reperfusion status and intubation for EVT, dexmedetomidine was associated with lower odds of favorable functional outcome (aOR 0.20; 95% CI 0.08-0.49). Among patients receiving dexmedetomidine within 72 hours post-stroke, no significant association with favorable functional outcome was found (aOR 0.55; 95%CI 0.19-1.62). Conclusions: Dexmedetomidine could potentially have negative impacts on patients with acute ischemic stroke in neurocritical care. However, these results appear to be mainly related to delirium complications rather than critical vulnerability of ischemic stroke. Further research is warranted to elucidate the predictive role of dexmedetomidine in this patient population.

Neuron-specific enolase at admission as a predictor for stroke volume, severity and outcome in ischemic stroke patients: a prognostic biomarker review

An ideal blood biomarker for stroke should provide reliable results, enable fast diagnosis, and be readily accessible for practical use. Neuron-specific enolase (NSE), an enzyme released after neuronal damage, has been studied as a marker for brain injury, including cerebral infarction. However, different methodologies and limited sample sizes have restricted the applicability of any potential findings. This work aims to determine whether NSE levels at Emergency Department (ED) admission correlate with stroke severity, infarcted brain volume, functional outcome, and/or death rates. A systematic literature review was performed using PubMed, Embase, and Scopus databases. Each reviewer independently assessed all published studies identified as potentially relevant. All relevant original observational studies (cohort, case–control, and cross-sectional studies) were included. Eleven studies (1398 patients) met the inclusion criteria. Among these, six studies reported a significant correlation between NSE levels and stroke severity, while only one found no association. Four studies indicated a positive relationship between infarcted brain volume assessed by imaging and NSE levels, in contrast to the findings of only one study. Four studies identified an association related to functional outcome and death rates, while three others did not reach statistical significance in their findings. These data highlight that NSE levels at ED admissions proved to be a promising tool for predicting the outcome of ischemic stroke patients in most studies. However, they presented high discrepancies and low robustness. Therefore, further research is necessary to establish and define the role of NSE in clinical practice.

Enhancing Ischemic Stroke Management with Cluster Nursing: A Comparative Study on the Effects of Ginkgo biloba Diterpene Lipid Amine Treatment

This study aimed to investigate the effect of cluster nursing on the management of ischemic stroke patients receiving Ginkgo biloba diterpene lipid amine treatment. This study was conducted on 197 patients from the Neurology Department of the author’s hospital from December 2021 to December 2022. These patients were divided based on their admission time into two groups: a control group comprising 98 patients who received routine nursing care and an observation group of 99 patients who were administered cluster nursing. This study evaluated and compared the incidence of nutritional intolerance events, nursing-related adverse events, and the overall quality of life among these groups. Results indicated a statistically significant reduction in the incidence of nutritional intolerance (P &lt; 0.05) and nursing adverse events associated with enteral nutrition support (P &lt; 0.05) in the observation group compared to the control group. Furthermore, the observation group exhibited higher scores in family role, energy, and verbal ability (P &lt; 0.05), suggesting an improvement in the quality of life. These findings highlight the effectiveness of the cluster nursing approach in improving the management and outcomes for ischemic stroke patients in terms of reducing nutritional intolerance and nursing adverse events, thereby significantly enhancing the quality of life.

Impact of early post-stroke affective and motivational disorders on functional outcome of ischemic stroke patients

Impact of early post-stroke affective and motivational disorders on functional outcome of ischemic stroke patients

Biomarkers — predictors of mortality in patients with ischemic stroke

BACKGROUND: Stroke is a global health problem and the second leading cause of death worldwide, with ischemic stroke accounting for the majority of acute cerebrovascular accidents. Predicting outcomes in patients with ischemic stroke is challenging due to the limited prognostic performance of existing models. The lack of reliable and predictive biomarkers of ischemic stroke used in clinical practice provides a rationale for studies to address this issue.&#x0D; AIM: The aim of this study was to identify clinical, demographic, neuroimaging and molecular biomarkers of ischemic stroke associated with the fatal outcome of the disease.&#x0D; MATERIALS AND METHODS: We retrospectively analyzed cases of ischemic stroke confirmed by computed tomography of the brain with a duration of 3 months or less. As factors potentially related to the outcome of ischemic stroke, we considered (1) clinical and demographic characteristics, including sex, age, history of acute cerebrovascular accident, time from stroke to hospitalization, phase and type of stroke, neurological status on admission (level of consciousness, FOUR coma scale), comorbidities (diabetes mellitus, infectious complications, oncological diseases); (2) molecular biomarkers including blood glucose and neuron-specific enolase (NSE) levels at different phases of stroke; (3) neuroimaging biomarkers such as number and location of stroke lesion, total infarct volume, signs of edema, hemorrhagic component according to computed tomography.&#x0D; RESULTS: 38 patients with ischemic stroke were included in the study. Lesion volume was larger in non-survivors: 123 [44.6–206.2] versus 42.7 [8.2–88.7] ml3 (p=0.032), and cerebral edema was detected significantly more often on admission CT scan than in survivors: 7 (77.8%) versus 10 (34.5%), p=0.022. Stroke lesion volume was a predictor of mortality (AUC 0.739; 95% CI 0.542–0.937; p=0.032). The optimal cut-off for stroke lesion volume was found to be 90 ml3.&#x0D; CONCLUSION: Signs of edema/brain stem dislocation and cerebral lesion volume 90 ml3 documented at hospital admission are predictors of fatal outcome. Therefore, neuroimaging biomarkers can be used for risk stratification of fatal outcome in ischemic stroke patients.

Deep Learning-Based Extraction of Biomarkers for the Prediction of the Functional Outcome of Ischemic Stroke Patients.

Accurately predicting functional outcomes in stroke patients remains challenging yet clinically relevant. While brain CTs provide prognostic information, their practical value for outcome prediction is unclear. We analyzed a multi-center cohort of 743 ischemic stroke patients (<72 h onset), including their admission brain NCCT and CTA scans as well as their clinical data. Our goal was to predict the patients' future functional outcome, measured by the 3-month post-stroke modified Rankin Scale (mRS), dichotomized into good (mRS ≤ 2) and poor (mRS > 2). To this end, we developed deep learning models to predict the outcome from CT data only, and models that incorporate other patient variables. Three deep learning architectures were tested in the image-only prediction, achieving 0.779 ± 0.005 AUC. In addition, we created a model fusing imaging and tabular data by feeding the output of a deep learning model trained to detect occlusions on CT angiograms into our prediction framework, which achieved an AUC of 0.806 ± 0.082. These findings highlight how further refinement of prognostic models incorporating both image biomarkers and clinical data could enable more accurate outcome prediction for ischemic stroke patients.

Predicting stroke outcome: A case for multimodal deep learning methods with tabular and CT Perfusion data

Motivation:Acute ischemic stroke is one of the leading causes of morbidity and disability worldwide, often followed by a long rehabilitation period. To improve and personalize stroke rehabilitation, it is essential to provide a reliable prognosis to caregivers and patients. Deep learning techniques might improve the predictions by incorporating different data modalities. We present a multimodal approach to predict the functional status of acute ischemic stroke patients after their discharge based on tabular data and CT perfusion imaging. Methods:We conducted experiments on tabular, imaging, and multimodal deep learning architectures to predict dichotomized mRS scores 3 months after the event. The dataset was collected from a Dutch hospital and includes 98 CVA patients with a visible occlusion on their CT perfusion scan. Tabular data is based on the Dutch Acute Stroke Audit data, and imaging data consists of summed-up CT perfusion maps. Results:On the tabular data, TabNet outperformed our baselines with an AUC of 0.71, while ResNet-10 on the imaging data performed comparably with an AUC of 0.70. Our implementation of the multimodal DAFT architecture outperforms baselines as well as comparable studies by achieving an 0.75 AUC, and 0.80 F1 score. This was achieved with a final model of less than a hundred thousand optimizable parameters, and a dataset less than half the size of reference papers. Conclusion:Overall, we demonstrate the feasibility of predicting the functional outcome for ischemic stroke patients and the usability of multimodal deep learning architectures for this purpose.

Serum uric acid and prognosis of ischemic stroke: Cohort study, meta-analysis and Mendelian randomization study.

The role of serum uric acid (UA) levels in the functional recovery of ischemic stroke remains uncertain. To evaluate whether UA could predict clinical outcomes in patients with ischemic stroke. A three-stage study design was employed, combining a large-scale prospective cohort study, a meta-analysis and a Mendelian randomization (MR) analysis. Firstly, we conducted a cohort study using data from the Nanjing Stroke Registry Program (NSRP) to assess the association between UA levels and 3-month functional outcomes in ischemic stroke patients. Secondly, the meta-analysis was conducted to integrate currently available cohort evidence. Lastly, MR analysis was utilized to explore whether genetically determined UA had a causal link to the functional outcomes of ischemic stroke using summary data from the CKDGen and GISCOME datasets. In the first stage, the cohort study included 5631 patients and found no significant association between UA levels and functional outcomes at 3 months after ischemic stroke. In the second stage, the meta-analysis, including 10 studies with 14,657 patients, also showed no significant association between UA levels and stroke prognosis. Finally, in the third stage, MR analysis using data from 6165 patients in the GISCOME study revealed no evidence of a causal relationship between genetically determined UA and stroke functional outcomes. Our comprehensive triangulation approach found no significant association between UA levels and functional outcomes at 3 months after ischemic stroke.

Association of nutritional screening tools with 6-month outcomes in ischemic stroke patients: A retrospective study

ObjectiveNutritional screening tools based on laboratory examinations are relatively objective and available indicators. However, few studies have investigated whether malnutrition severity might be associated with adverse outcomes at the platform recovery period of 6 mo and differentiated in acute ischemic stroke patients with or without intravenous thrombolysis. Therefore, we assessed the association between malnutrition and 6-mo outcomes in both intravenous thrombolysis and non-intravenous thrombolysis patients. MethodsWe retrospectively recruited 138 acute ischemic stroke patients who received intravenous thrombolysis and 311 who did not. The Geriatric Nutritional Risk Index, prognostic nutritional index, and Controlling Nutritional Status were used to assess nutritional status. The concordance between the 3 malnutrition screening tools was investigated with the κ statistic. Subgroups analyses were conducted to assess the correlation between malnutrition and functional outcomes in intravenous thrombolysis and non-intravenous thrombolysis patients. ResultsA total of 17 (6.44%) patients were suffering from malnutrition, as indicated by the Geriatric Nutritional Risk Index, prognostic nutritional index, and Controlling Nutritional Status jointly. Moderate–severe malnutrition evaluated by the Geriatric Nutritional Risk Index was significantly associated with poor functional outcome (odds ratio = 4.074; P = 0.003). Patients in the good functional outcome group (modified Rankin scale scores = 0 to 2) had a higher proportion of intravenous thrombolysis treatment (32.79% versus 21.25%; P = 0.043). Furthermore, subgroup analyses found no significant interactions between malnourished levels and intravenous thrombolysis treatment (P interaction > 0.05). ConclusionThe Geriatric Nutritional Risk Index, over ≤24 h, compared with the prognostic nutritional index and Controlling Nutritional Status, provided timely signals to improve acute ischemic stroke patients’ nutritional status. Also, nutritional status might not lead todifferent 6-mo outcomes, whether or not patients received intravenous thrombolysis treatment.

Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease.

Cerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms. We review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice. The incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers. The implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.