Biomarkers Predictive of Long-Term Outcome After Ischemic Stroke: A Meta-Analysis

Revolutionizing Stroke Care: Nanotechnology-Based Brain Delivery as a Novel Paradigm for Treatment and Diagnosis

Stroke, a severe medical condition arising from abnormalities in the coagulation-fibrinolysis cycle and metabolic processes, results in brain cell impairment and injury due to blood flow obstruction within the brain. Prompt and efficient therapeutic approaches are imperative to control and preserve brain functions. Conventional stroke medications, including fibrinolytic agents, play a crucial role in facilitating reperfusion to the ischemic brain. However, their clinical efficacy is hampered by short plasma half-lives, limited brain tissue distribution attributed to the blood-brain barrier (BBB), and lack of targeted drug delivery to the ischemic region. To address these challenges, diverse nanomedicine strategies, such as vesicular systems, polymeric nanoparticles, dendrimers, exosomes, inorganic nanoparticles, and biomimetic nanoparticles, have emerged. These platforms enhance drug pharmacokinetics by facilitating targeted drug accumulation at the ischemic site. By leveraging nanocarriers, engineered drug delivery systems hold the potential to overcome challenges associated with conventional stroke medications. This comprehensive review explores the pathophysiological mechanism underlying stroke and BBB disruption in stroke. Additionally, this review investigates the utilization of nanocarriers for current therapeutic and diagnostic interventions in stroke management. By addressing these aspects, the review aims to provide insight into potential strategies for improving stroke treatment and diagnosis through a nanomedicine approach.

Development and Validation of a Predictive Score for Three-Year Mortality in Acute Ischemic Stroke Patients

Background and Objectives: Acute ischemic stroke (AIS) is a leading cause of death and disability with poor long-term outcomes. Creating a predictive score for long-term mortality in AIS might be important for optimizing treatment strategies. The aim of this study is to develop and validate a predictive score for three-year mortality in patients with AIS using several demographic, clinical, laboratory and imaging parameters. Materials and Methods: This study included 244 AIS patients admitted to a tertiary center and followed up for three years. The patients' data included demographics, clinical features, laboratory tests (including resistin and leptin levels) and imaging parameters. The patients were randomly divided into a predictive group (n = 164) and a validation group (n = 80). Results: Advanced age, a high NIHSS score, low levels of hemoglobin, elevated resistin levels and the presence of carotid plaques were independently associated with three-year mortality. The predictive model incorporated these variables, and it was validated in a separate cohort. Leptin levels did not significantly predict mortality. Conclusions: This study developed and validated a promising predictive score for three-year mortality in patients with AIS. Advanced age, high NIHSS scores, low hemoglobin levels, elevated resistin levels and the presence of carotid plaques were the independent predictors of long-term mortality.

S100B and brain ultrasound: Novel predictors for functional outcome in acute ischemic stroke patients

OBJECTIVE

Stroke is a leading cause of mortality and disability worldwide. This study aimed to assess the prognostic value of serum S100B protein, transcranial color-coded duplex sonography (TCCD), and optic nerve sheath diameter (ONSD) in predicting functional outcomes in critically ill patients with acute ischemic stroke (AIS).

METHODS

In this prospective observational study, 80 adult AIS patients were evaluated. Serum S100B protein levels, ONSD, and middle cerebral artery pulsatility index (MCA PI) were measured on days 1 and 3. Functional outcomes at 90 days were assessed using the modified Rankin Scale (mRS) and categorized into favourable (mRS 0-2) or unfavourable (mRS 3-6) groups. The association of demographic, clinical, laboratory, and imaging parameters with mRS outcomes was analyzed.

RESULTS

Poor mRS outcomes occurred in 82.5 % of patients. Factors significantly associated with poor outcomes were female sex, higher National Institutes of Health Stroke Scale (NIHSS) scores on days 1, 3, and 7, and larger stroke size. Receiver Operating Characteristic (ROC) curve analysis revealed that ONSD at days 1 and 3, serum S100B levels at day 1, and right MCA PI at day 1 had significant predictive value for poor mRS outcome. Multivariate analysis identified female sex, S100B on day 1, and NIHSS on days 1, 3, and 7 as independent predictors of poor mRS outcomes.

CONCLUSIONS

The combination of S100B, ONSD, and MCA PI improved the prediction of functional outcomes in critically ill AIS patients. Early S100B measurement and brain ultrasound evaluation may serve as valuable prognostic tools for guiding therapeutic decision-making. This study provides novel insights into the role of S100B and brain ultrasound in stroke outcome prediction, particularly in critically ill AIS patients.

Investigating the Predictive Value of Thyroid Hormone Levels for Stroke Prognosis

Given the expansion of life expectancy, the aging of the population, and the anticipated rise in the number of stroke survivors in Europe with severe neurological consequences in the coming decades, stroke is becoming the most prevalent cause of functional disability. Therefore, the prognosis for a stroke must be timely and precise. Two databases (MEDLINE and Scopus) were searched to identify all relevant studies published between 1 January 2005 and 31 December 2022 that investigated the relationship between thyroid hormone levels and acute stroke severity, mortality, and post-hospital prognosis. Only full-text English-language articles were included. This review includes Thirty articles that were traced and incorporated into the present review. Emerging data regarding the potential predictive value of thyroid hormone levels suggests there may be a correlation between low T3 syndrome, subclinical hypothyroidism, and poor stroke outcome, especially in certain age groups. These findings may prove useful for rehabilitation and therapy planning in clinical practice. Serum thyroid hormone concentration measurement is a non-invasive, relatively harmless, and secure screening test that may be useful for this purpose.

Eosinophil: A New Circulating Biomarker for Risk of Poor Outcome in Stroke Patients Undergoing Mechanical Thrombectomy

Objective

Acute ischemic stroke (AIS), caused by occlusion of large vessel, is a serious life-threatening disease. This study aimed to comprehensively investigate the association of 14 common and readily available circulating biomarkers with the 90-day modified Rankin Scale (mRS) score in patients undergoing mechanical thrombectomy (MT).

Methods

This study included patients with anterior circulation large vessel occlusive stroke treated with MT from 05/2017 to 12/2021. Baseline comparisons of poor outcome were performed among enrolled patients. Factors that may be associated with the mRS score were assessed using correlation analysis. Univariate and multivariate logistic regression analyses were used to evaluate the predictive value of circulating biomarkers and poor outcome.

Results

The mRS score has a strong correlation with neutrophil to lymphocyte ratio (NLR) and eosinophil levels (all r_s>0.4 in absolute value and all P<0.001) in addition to a high correlation with National Institute of Health Stroke Scale (NIHSS) score (r_s=0.40, P<0.001). There was also a high correlation between NLR and eosinophil (r_s=-0.58, P<0.001). In the multivariate regression analysis, only neutrophil (adjusted OR=1.301, 95% CI: 1.155-1.465, P<0.001), eosinophil (adjusted OR<0.001, 95% CI: <0.001-0.016, P<0.001), and NLR (adjusted OR=1.158, 95% CI: 1.082-1.241, P<0.001) were independently associated with poor outcome.

Conclusion

This study evaluated a series of circulating biomarkers and found that neutrophil, eosinophil, and NLR independently predicted poor outcome after MT in AIS patients. There was a significant negative correlation between eosinophil and NLR levels.

Copeptin Implementation on Stroke Prognosis

Predicting functional outcome following stroke is considered to be of key importance in an attempt to optimize overall stroke care. Although clinical prognostic tools have been widely implemented, optimal blood biomarkers might be able to yield additional information regarding each stroke survivor's propensity for recovery. Copeptin seems to have interesting prognostic potential poststroke. The present review aims to explore the prognostic significance of copeptin in stroke patients. Literature research of two databases (MEDLINE and Scopus) was conducted to trace all relevant studies published between 16 February 2012 and 16 February 2022 that focused on the utility of copeptin as a prognostic marker in acute stroke setting. 25 studies have been identified and included in the present review. The predictive ability of copeptin regarding both functional outcome and mortality appears to be in the range of established clinical variables, thus highlighting the added value of copeptin evaluation in stroke management. Apart from acute ischemic stroke, the discriminatory accuracy of the biomarker was also demonstrated among patients with transient ischemic attack, intracerebral hemorrhage, and subarachnoid hemorrhage. Overall, copeptin represents a powerful prognostic tool, the clinical implementation of which is expected to significantly facilitate the individualized management of stroke patients.

Systemic inflammation response index as a prognostic predictor in patients with acute ischemic stroke: A propensity score matching analysis

Background

Acute ischemic stroke (AIS), the most common type of stroke, is a major cause of morbidity and mortality worldwide. A growing number of studies have demonstrated that inflammation is a critical mechanism in AIS. Being an easily available and effective inflammatory marker, the systemic inflammation response index (SIRI) shows a high association with mortality in patients with cancer and intracerebral hemorrhage. In this study, we evaluated the potential prognostic role of SIRI in critically ill patients with AIS.

Methods

Clinic data were extracted from the Medical Information Mart data for the Intensive Care IV (MIMIC-IV) database. The optimal cutoff value of SIRI was determined by X-tile software. The primary outcome was the 90-day all-cause mortality, and the secondary outcomes were 30-day and 1-year all-cause mortality of patients with AIS. Cox proportional hazards regression analyses were used to assess the association between SIRI levels and all-cause mortality, and survival curves were estimated using the Kaplan-Meier method. Furthermore, a 1:1 propensity score matching (PSM) method was performed to balance the influence of potential confounding factors.

Results

A total of 2,043 patients were included in our study. X-tile software indicated that the optimal cutoff value of the SIRI for 90-day mortality was 4.57. After PSM, 444 pairs of score-matched patients were generated. Cox proportional hazard model showed that after adjusting for possible confounders, high SIRI level (≥4.57) was independently associated with the 90-day all-cause mortality in the cohort before PSM (HR = 1.56, 95% CI: 1.30-1.89, p < 0.001) and the PSM subset (HR = 1.47, 95% CI: 1.16-1.86, p = 0.001). The survival curves showed that patients with SIRI ≥4.57 had a significantly lower 90-day survival rate in the cohort before PSM (56.7 vs. 77.3%, p < 0.001) and the PSM subset (61.0 vs. 71.8%, p = 0.001). Consistently, AIS patients with high SIRI levels (≥4.57) presented a significantly high risk of 30-day and 1-year all-cause mortality before and after PSM.

Conclusion

A higher SIRI (≥4.57) was associated with a higher risk of 90-day, 30-day, and 1-year mortality and was an independent risk factor of mortality in patients with acute ischemic stroke.

Predicting Outcome in Patients with Brain Injury: Differences between Machine Learning versus Conventional Statistics

Defining reliable tools for early prediction of outcome is the main target for physicians to guide care decisions in patients with brain injury. The application of machine learning (ML) is rapidly increasing in this field of study, but with a poor translation to clinical practice. This is basically dependent on the uncertainty about the advantages of this novel technique with respect to traditional approaches. In this review we address the main differences between ML techniques and traditional statistics (such as logistic regression, LR) applied for predicting outcome in patients with stroke and traumatic brain injury (TBI). Thirteen papers directly addressing the different performance among ML and LR methods were included in this review. Basically, ML algorithms do not outperform traditional regression approaches for outcome prediction in brain injury. Better performance of specific ML algorithms (such as Artificial neural networks) was mainly described in the stroke domain, but the high heterogeneity in features extracted from low-dimensional clinical data reduces the enthusiasm for applying this powerful method in clinical practice. To better capture and predict the dynamic changes in patients with brain injury during intensive care courses ML algorithms should be extended to high-dimensional data extracted from neuroimaging (structural and fMRI), EEG and genetics.

Copeptin as a Diagnostic and Prognostic Biomarker in Cardiovascular Diseases

Copeptin is the carboxyl-terminus of the arginine vasopressin (AVP) precursor peptide. The main physiological functions of AVP are fluid and osmotic balance, cardiovascular homeostasis, and regulation of endocrine stress response. Copeptin, which is released in an equimolar mode with AVP from the neurohypophysis, has emerged as a stable and simple-to-measure surrogate marker of AVP and has displayed enormous potential in clinical practice. Cardiovascular disease (CVD) is currently recognized as a primary threat to the health of the population worldwide, and thus, rapid and effective approaches to identify individuals that are at high risk of, or have already developed CVD are required. Copeptin is a diagnostic and prognostic biomarker in CVD, including the rapid rule-out of acute myocardial infarction (AMI), mortality prediction in heart failure (HF), and stroke. This review summarizes and discusses the value of copeptin in the diagnosis, discrimination, and prognosis of CVD (AMI, HF, and stroke), as well as the caveats and prospects for the application of this potential biomarker.

Copeptin Release in Arterial Hypotension and Its Association with Severity of Disease in Critically Ill Children

Low copeptin levels may indicate inadequate arginine-vasopressin release promoting arterial hypotension, whereas high copeptin concentrations may reflect disease severity. This single-center prospective non-randomized clinical trial analyzed the course of blood copeptin in critically ill normo- and hypotensive children and its association with disease severity. In 164 patients (median age 0.5 years (interquartile range 0.1, 2.9)), the mean copeptin concentration at baseline was 43.5 pmol/L. Though not significantly different after 61 h (primary outcome, mean individual change: −12%, p = 0.36, paired t-test), we detected 1.47-fold higher copeptin concentrations during arterial hypotension when compared to normotension (mixed-effect ANOVA, p = 0.01). In total, 8 out of 34 patients (23.5%) with low copeptin concentrations <10 pmol/L were hypotensive. Copeptin was highest in the adjusted mixed-effect regression analysis within the first day (+20% at 14 h) and decreased significantly at 108 h (−27%) compared to baseline (p = 0.002). Moreover, we found a significant association with vasopressor-inotrope treatment intensity, infancy (1−12 months) and cardiopulmonary bypass (all p ≤ 0.001). In conclusion, high copeptin values were associated with arterial hypotension and severity of disease in critically ill children. This study does not support the hypothesis that low copeptin values might be indicative of arginine-vasopressin deficiency.

Copeptin in fluid disorders and stress

Copeptin, a glycosylated peptide of 39 amino acids, is the C-terminal segment of arginine vasopressin (AVP) precursor peptide, which is consisted of two other fragments, vasopressin and neurophysin Ⅱ. The main physiological functions of AVP are fluid and osmotic balance, cardiovascular homeostasis and regulation of the endocrine stress response. Numerous studies have demonstrated that the endogenous AVP in plasma is a meaningful biomarker to guide diagnosis and therapy of diseases associated with fluids disorders and stress. However, due to its instability, short half-time life in circulation and lack of readily available AVP assays, clinical measurement of AVP is restricted. In contrast to AVP, copeptin which is released in an equimolar mode with AVP from the pituitary, has emerged as a stable and simple-to-measure surrogate marker of AVP and displays excellent potential in diagnosis, differentiation and prognosis of various diseases. This review will discuss the studies on the clinical value of copeptin in different diseases, especially in AVP-dependent fluids disorders, as well as issues and prospects of the application of this potential biomarker.