A Systematic Review of the Predictive Value of Plasma D-Dimer Levels for Predicting Stroke Outcome

Stroke Mimics at 30 Years: Where We Have Been, Where We Are Now, and Where We Are Going

Stroke mimics and chameleons remain a major challenge to the clinician and clinical investigator. Misdiagnosis of stroke can result in significant harm to our patients, as well as unnecessary financial costs to the health care systems internationally. The approach to stroke mimics and chameleons has evolved over time with the development of clinical scales and technology. The combination of these tools with clinical acumen can minimize diagnostic errors to the benefit of patients.

Blood Biomarkers in Ischemic Stroke Diagnostics and Treatment-Future Perspectives

Stroke is a leading cause of disability and the second most common cause of death worldwide, with its incidence increasing due to an aging population. Early diagnosis is crucial for timely medical intervention. Biomarkers serve as objective indicators to predict outcomes, monitor treatment responses, and assess prognosis. This review examines the evolving landscape of stroke biomarkers, highlighting their potential clinical applications and the challenges hindering their widespread use. Blood biomarkers are readily accessible and provide insight into the pathophysiological processes underlying stroke. This review focuses on neuronal and glial biomarkers, as well as those associated with inflammation, thrombosis, excitotoxicity, and neuroprotection. Also, it focuses on genetic biomarkers. The timing of biomarker measurement is particularly critical in the early stages of stroke, when rapid decision-making is essential, and it requires systematic investigation. Although numerous molecules have been proposed as stroke biomarkers in recent years, none have yet been integrated into routine clinical practice. Stroke biomarkers hold great promise for enhancing diagnosis, risk stratification, and personalized treatment strategies. However, well-designed studies and rigorous validation are necessary to bridge the gap between research findings and clinical implementation. Integrating biomarkers with existing diagnostic tools could revolutionize stroke management and improve patient outcomes. Continued research into blood biomarkers and their clinical utility remains imperative for advancing stroke care.

Etiologies and Risk Factors by Sex and Age in Young Adult Patients with Ischemic Stroke

AIM

The aim of this study was to explore etiologies and risk factors by age and sex in young adult patients with ischemic stroke.

METHODS

We recruited patients with ischemic stroke aged between 18 and 49 years. We assessed pathological etiologies by the Trial of Org 10,172 in Acute Stroke Treatment (TOAST) classification and risk factors by the International Pediatric Stroke Study (IPSS) classification. We explored the distribution of etiologies and risk factors by age and sex and investigated baseline features associated with functional outcomes at 3 months.

RESULTS

Of 8521 stroke patients consecutively admitted, 1017 patients (11.9%) aged between 18-49 years, of whom large artery atherosclerosis was the most common etiology (n=375, 36.9%), followed by other determined cause (n=194, 19.1%) and undetermined cause (n=184, 18.1%). Compared to male patients, female patients had more cardioembolism (16.34% vs 8.42%) and less small artery occlusion (8.56% vs 17.76%). As age increased, the proportions of large artery atherosclerosis (P <0.001) and small artery occlusion (P <0.001) increased, and the proportion of other determined causes decreased (P <0.001). Of 184 patients with undetermined causes, 173 (94.0%) had at least one IPSS risk factor. A higher serum level of D-dimer at baseline was associated with an increased risk of unfavorable outcome (OR 1.118, 95% CI 1.052- 1.189), adjusting for the effect of age and stroke severity.

CONCLUSION

Approximately one-fifth of young patients with ischemic stroke had undetermined etiology, for whom the IPSS classification helps to explore risk factors. A higher level of Ddimer was associated with a higher risk of unfavorable outcomes at 3 months.

D-Dimer and the Short-Term Prognosis of Patients with Subarachnoid Hemorrhage: A Meta-Analysis

INTRODUCTION

Elevated plasma D-dimer has been observed in acute phase of subarachnoid hemorrhage (SAH), while a quantitative evaluation for the association between D-dimer level and prognosis of SAH remains lacking. The aim of the meta-analysis was to investigate the potential predictive role of D-dimer for the short-term functional outcome of patients with SAH.

METHODS

Relevant observational studies were retrieved by searching PubMed, Web of Science, Embase, Wanfang, and CNKI. A poor functional outcome was generally defined by the Glasgow Outcome Scale (1-3) or the modified Rankin Scale (≥3). A random-effects model was used to combine the data by incorporating the influence of between-study heterogeneity.

RESULTS

Eleven studies involving 3,748 patients with SAH were included. Among them, 1,131 patients (30.2%) developed a poor functional outcome within 6 months. The pooled results showed that a higher plasma D-dimer at admission was associated with a higher risk of poor functional outcome during follow-up (odds ratio per 1 mg/L increment of D-dimer: 1.65, 95% confidence interval: 1.49-1.82, p < 0.001; I2 = 0%). Sensitivity analysis by excluding one study at a time showed similar results. Subgroup analyses suggested that the association between D-dimer and poor functional outcome of patients with SAH was not significantly affected by study design, country, mean age, proportion of men, follow-up duration, methods for defining poor outcome, study quality scores, timing of D-dimer measurement, or the publication year.

CONCLUSION

A high plasma D-dimer at admission is associated with a poor short-term functional outcome of patients with SAH.

Machine learning is an effective method to predict the 3-month prognosis of patients with acute ischemic stroke

Background and objectives

Upwards of 50% of acute ischemic stroke (AIS) survivors endure varying degrees of disability, with a recurrence rate of 17.7%. Thus, the prediction of outcomes in AIS may be useful for treatment decisions. This study aimed to determine the applicability of a machine learning approach for forecasting early outcomes in AIS patients.

Methods

A total of 659 patients with new-onset AIS admitted to the Department of Neurology of both the First and Second Affiliated Hospitals of Bengbu Medical University from January 2020 to October 2022 included in the study. The patient' demographic information, medical history, Trial of Org 10,172 in Acute Stroke Treatment (TOAST), National Institute of Health Stroke Scale (NIHSS) and laboratory indicators at 24 h of admission data were collected. The Modified Rankine Scale (mRS) was used to assess the 3-mouth outcome of participants' prognosis. We constructed nine machine learning models based on 18 parameters and compared their accuracies for outcome variables.

Results

Feature selection through the Least Absolute Shrinkage and Selection Operator cross-validation (Lasso CV) method identified the most critical predictors for early prognosis in AIS patients as white blood cell (WBC), homocysteine (HCY), D-Dimer, baseline NIHSS, fibrinogen degradation product (FDP), and glucose (GLU). Among the nine machine learning models evaluated, the Random Forest model exhibited superior performance in the test set, achieving an Area Under the Curve (AUC) of 0.852, an accuracy rate of 0.818, a sensitivity of 0.654, a specificity of 0.945, and a recall rate of 0.900.

Conclusion

These findings indicate that RF models utilizing general clinical and laboratory data from the initial 24 h of admission can effectively predict the early prognosis of AIS patients.

Predictive value of admission D-dimer levels in patient with acute ischaemic stroke and COVID-19: a second-wave prospective cohort study

OBJECTIVES

This study aimed to evaluate the predictive value of admission D-dimer levels for in-hospital mortality in patients with COVID-19 and acute ischaemic stroke.

DESIGN

Cohort (prospective).

SETTING

Tertiary referral hospital in the capital city of Indonesia conducted from June to December 2021.

PARTICIPANTS

60 patients with acute ischaemic stroke and COVID-19 were included. Patients were classified into D-dimer groups (low and high) according to a 2 110 ng/mL cut-off value, determined via receiver operating characteristic analysis.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was in-hospital mortality, with admission D-dimer levels as the major predictor. Secondary outcomes included associations between other demographic and clinical variables and the admission D-dimer value. Kaplan-Meier method was used to carry out survival analysis, with univariable and multivariable Cox regression performed to assess the association of D-dimer levels and other confounding variables (including demographic, clinical and laboratory parameters) with in-hospital mortality.

RESULTS

The findings demonstrated an association between elevated admission D-dimer levels (≥2 110 ng/mL) and an increased likelihood of death during hospitalisation. The adjusted HR was 14.054 (95% CI 1.710 to 115.519; p=0.014), demonstrating an increase in mortality risk after accounting for confounders such as age and diabetes history. Other significant predictors of mortality included a history of diabetes and increased white blood cell count.

CONCLUSIONS

Admission D-dimer levels may be a useful predictive indicator for the likelihood of death during hospitalisation in individuals with COVID-19 and acute ischaemic stroke.

Cuproptosis in stroke: focusing on pathogenesis and treatment

Annually, more than 15 million people worldwide suffer from stroke, a condition linked to high mortality and disability rates. This disease significantly affects daily life, impairing everyday functioning, executive function, and cognition. Moreover, stroke severely restricts patients' ability to perform daily activities, diminishing their overall quality of life. Recent scientific studies have identified cuproptosis, a newly discovered form of cell death, as a key factor in stroke development. However, the role of cuproptosis in stroke remains unclear to researchers. Therefore, it is crucial to investigate the mechanisms of cuproptosis in stroke's pathogenesis. This review examines the physiological role of copper, the characteristics and mechanisms of cuproptosis, the differences and similarities between cuproptosis and other cell death types, and the pathophysiology of cuproptosis in stroke, focusing on mitochondrial dysfunction and immune infiltration. Further research is necessary to understand the relationship between previous strokes and cuproptosis and to clarify the mechanisms behind these associations.

Blood biomarker changes following therapeutic hypothermia in ischemic stroke

INTRODUCTION

Therapeutic hypothermia is a promising candidate for stroke treatment although its efficacy has not yet been demonstrated in patients. Changes in blood molecules could act as surrogate markers to evaluate the efficacy and safety of therapeutic cooling.

METHODS

Blood samples from 54 patients included in the EuroHYP-1 study (27 treated with hypothermia, and 27 controls) were obtained at baseline, 24 ± 2 h, and 72 ± 4 h. The levels of a panel of 27 biomarkers, including matrix metalloproteinases and cardiac and inflammatory markers, were measured.

RESULTS

Metalloproteinase-3 (MMP-3), fatty-acid-binding protein (FABP), and interleukin-8 (IL-8) increased over time in relation to the hypothermia treatment. Statistically significant correlations between the minimum temperature achieved by each patient in the hypothermia group and the MMP-3 level measured at 72 h, FABP level measured at 24 h, and IL-8 levels measured at 24 and 72 h were found. No differential biomarker levels were observed in patients with poor or favorable outcomes according to modified Rankin Scale scores.

CONCLUSION

Although the exact roles of MMP3, FABP, and IL-8 in hypothermia-treated stroke patients are not known, further exploration is needed to confirm their roles in brain ischemia.

The value of clinical routine blood biomarkers in predicting long-term mortality after stroke

BACKGROUND

Several blood biomarkers have been identified as predictors for poor outcome after ischemic stroke. However, recent studies mainly focused on single or experimental biomarkers and considered rather short follow-up intervals limiting their value for daily clinical practice. We, therefore, aimed to compare various clinical routine blood biomarkers for their predictive value on post-stroke mortality over a 5-year follow-up period.

PATIENTS AND METHODS

This data analysis of a prospective single-center study included all consecutive ischemic stroke patients admitted to the stroke unit of our university hospital over a 1-year period. Various blood biomarkers of inflammation, heart failure, metabolic disorders, and coagulation were analyzed from standardized routine blood samples collected within 24 h of hospital admission. All patients underwent a thorough diagnostic workup and were followed for 5 years post-stroke.

RESULTS

Of 405 patients (mean age: 70.3 years), 72 deceased (17.8%) during the follow-up period. While various routine blood biomarkers were associated with post-stroke mortality in univariable analyses, only NT-proBNP remained an independent predictor (adjusted odds ratio 5.1; 95% CI 2.0-13.1; p < 0.001) for death after stroke. NT-proBNP levels ⩾794 pg/mL (n = 169, 42%) had a sensitivity of 90% for post-stroke mortality with a negative predictive value of 97% and was additionally associated with cardioembolic stroke and heart failure (each p ⩽ 0.05).

CONCLUSION

NT-proBNP represents the most relevant routine blood-based biomarker for the prediction of long-term mortality after ischemic stroke. Increased NT-proBNP levels indicate a vulnerable subgroup of stroke patients in which early and thorough cardiovascular assessment and consistent follow-ups could improve outcome after stroke.

Causal Associations between Functional/Structural Connectivity and Stroke: A Bidirectional Mendelian Randomization Study

Disruption of brain resting-state networks (RSNs) is known to be related to stroke exposure, but determining causality can be difficult in epidemiological studies. We used data on genetic variants associated with the levels of functional (FC) and structural connectivity (SC) within 7 RSNs identified from a genome-wide association study (GWAS) meta-analysis among 24,336 European ancestries. The data for stroke and its subtypes were obtained from the MEGASTROKE consortium, including up to 520,000 participants. We conducted a two-sample bidirectional Mendelian randomization (MR) study to investigate the causality relationship between FC and SC within 7 RSNs and stroke and its subtypes. The results showed that lower global mean FC and limbic network FC were associated with a higher risk of any ischemic stroke and small vessel stroke separately. Moreover, ventral attention network FC and default mode network SC have a positive causal relationship with the risk of small vessel stroke and large artery stroke, respectively. In the inverse MR analysis, any stroke and large artery stroke were causally related to dorsal attention network FC and somatomotor FC, respectively. The present study provides genetic support that levels of FC or SC within different RSNs have contrasting causal effects on stroke and its subtypes. Moreover, there is a combination of injury and compensatory physiological processes in brain RSNs following a stroke. Further studies are necessary to validate our results and explain the physiological mechanisms.

Construction of a Nomogram Prediction Model for Prognosis in Patients with Large Artery Occlusion-Acute Ischemic Stroke

BACKGROUND

Patients with large artery occlusion-acute ischemic stroke (LAO-AIS) can experience adverse outcomes, such as brain herniation due to complications. This study aimed to construct a nomogram prediction model for prognosis in patients with LAO-AIS in order to maximize the benefits for clinical patients.

METHODS

Retrospective analysis of 243 patients with LAO-AIS from January 2019 to January 2022 with medical history data and blood examination at admission. Univariate and multivariate analyses were conducted through binary logistic regression equation analysis, and a nomogram prediction model was constructed.

RESULTS

Results of this study showed that hyperlipidemia (odds ratio [OR] = 2.849, 95% confidence interval [CI] = 1.100-7.375, P = 0.031), right cerebral infarction (OR = 2.144, 95% CI = 1.106-4.156, P = 0.024), D-Dimer>500 ng/mL (OR = 2.891, 95% CI = 1.398-5.980, P = 0.004), and neutrophil-lymphocyte ratio >7.8 (OR = 2.149, 95% CI = 1.093-4.225, P = 0.027) were independent risk factors for poor early prognosis in patients with LAO-AIS. In addition, hypertension (OR = 1.947, 95% CI = 1.114-3.405, P = 0.019), hyperlipidemia (OR = 2.594, 95% CI = 1.281-5.252, P = 0.008), smoking (OR = 2.414, 95% CI = 1.368-4.261, P = 0.002), D-dimer>500 ng/mL (OR = 3.170, 95% CI = 1.533-6.553, P = 0.002), and neutrophil-lymphocyte ratio >7.8 (OR = 2.144, 95% CI = 1.231-3.735, P = 0.007) were independent risk factors for poor long-term prognosis. The early prognosis nomogram receiver operating characteristic curve area under the curve value was 0.688 for the training set and 0.805 for the validation set, which was highly differentiated. The mean error was 0.025 for the training set calibration curve and 0.016 for the validation set calibration curve. Both the training and validation set decision curve analyses indicated that the clinical benefit of the nomogram was significant. The long-term prognosis nomogram receiver operating characteristic curve area under the curve values was 0.697 for the training set and 0.735 for the validation set, showing high differentiation. The mean error was 0.041 for the training set calibration curve and 0.021 for the validation set calibration curve. Both of the training and validation set decision curve analyses demonstrated a substantial clinical benefit of the nomogram.

CONCLUSIONS

The nomogram prediction model based on admission history data and blood examination are easy-to-use tools that provide an accurate individualized prediction for patients with LAO-AIS and can assist in early clinical decisions and in obtaining an early prognosis.

A Systematic Review of Body Fluids Biomarkers Associated With Early Neurological Deterioration Following Acute Ischemic Stroke

Biomarkers are objectively measured biological properties of normal and pathological processes. Early neurological deterioration (END) refers to the deterioration of neurological function in a short time after the onset of acute ischemic stroke (AIS) and is associated with adverse outcomes. Although multiple biomarkers have been found to predict END, there are currently no suitable biomarkers to be applied in routine stroke care. According to the Preferred Reporting Items for Systematic Review standards, we present a systematic review, concentrating on body fluids biomarkers that have shown potential to be transferred into clinical practice. We also describe newly reported body fluids biomarkers that can supply different insights into the mechanism of END. In our review, 40 scientific papers were included. Depending on the various mechanisms, sources or physicochemical characteristics of body fluids biomarkers, we classified related biomarkers as inflammation, protease, coagulation, metabolism, oxidative stress, and excitatory neurotoxicity. The body fluids biomarkers whose related articles are limited or mechanisms are unknown are categorized as other biomarkers. The inflammation-related biomarkers, such as neutrophil-to-lymphocyte ratio and hypersensitive C-reactive protein, play a crucial role among the mentioned biomarkers. Considering the vast heterogeneity of stroke progression, using a single body fluids biomarker may not accurately predict the risk of stroke progression, and it is necessary to combine multiple biomarkers (panels, scores, or indices) to improve their capacity to estimate END.

Potential Biomarkers for Post-Stroke Cognitive Impairment: A Systematic Review and Meta-Analysis

Stroke is a primary debilitating disease in adults, occurring in 15 million individuals each year and causing high mortality and disability rates. The latest estimate revealed that stroke is currently the second leading cause of death worldwide. Post-stroke cognitive impairment (PSCI), one of the major complications after stroke, is frequently underdiagnosed. However, stroke has been reported to increase the risk of cognitive impairment by at least five to eight times. In recent decades, peripheral blood molecular biomarkers for stroke have emerged as diagnostic, prognostic, and therapeutic targets. In this study, we aimed to evaluate some blood-derived proteins for stroke, especially related to brain damage and cognitive impairments, by conducting a systematic review and meta-analysis and discussing the possibility of these proteins as biomarkers for PSCI. Articles published before 26 July 2021 were searched in PubMed, Embase, the Web of Science, and the Cochrane Library to identify all relevant studies reporting blood biomarkers in patients with stroke. Among 1820 articles, 40 were finally identified for this study. We meta-analyzed eight peripheral biomarker candidates: homocysteine (Hcy), high-density lipoprotein cholesterol (HDL-C), C-reactive protein (CRP), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglyceride (TG), uric acid, and glycated hemoglobin (HbA1c). The Hcy, CRP, TC, and LDL-C levels were significantly higher in patients with PSCI than in the non-PSCI group; however, the HDL-C, TG, uric acid, and HbA1c levels were not different between the two groups. Based on our findings, we suggest the Hcy, CRP, TC, and LDL-C as possible biomarkers in patients with post-stroke cognitive impairment. Thus, certain blood proteins could be suggested as effective biomarkers for PSCI.