Dataset Information

Inflammatory markers as independent predictors for stroke outcomes.

ABSTRACT:

Background

Inflammation plays an important role in the pathophysiology of stroke. The aim of the present study was to investigate the association between various inflammatory risk markers and ischemic stroke outcome and subtype.

Methods

A total of 3,013 ischemic stroke patients who were admitted to our hospital from 01/01/2016 to 12/30/2018 were retrospectively studied. Stroke subtypes were defined by the Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification. Levels of five common inflammatory markers including white blood cell (WBC) count, neutrophil, lymphocyte, serum C-reactive protein (CRP), and interleukin-6 (IL-6) were measured, and eleven conventional risk factors were further evaluated in the prediction of overall mortality as well as three functional outcomes defined by the National Institute of Health Stroke Scale (NIHSS), the modified Rankin Scale (mRS), and the Barthel Index (BI). Independent predictors of outcome were identified by multivariate logistic regression, and an importance score measured by the area under the receiver operating characteristics curve for each predictor using a Naive Bayes model was reported.

Results

Neutrophil and WBC were significantly higher in large-artery atherosclerosis (LAA) and cardioembolism (CE) subtype. In contrast, lymphocyte was significantly higher in small-artery occlusion (SAO). Neutrophil-lymphocyte ratio and CRP level were the best independent predictors, after adjustment for traditional risk factors and TOAST subtype for all four types of outcomes.

Conclusion

Inflammatory risk markers including neutrophil, lymphocyte, and CRP may have strong independent prediction values for stroke outcome.

SUBMITTER: Zhang XG

PROVIDER: S-EPMC7821567 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Inflammatory markers as independent predictors for stroke outcomes.

Zhang Xiao-Guang XG Xue Jie J Yang Wen-Hao WH Xu Xu-Shen XS Sun Hong-Xian HX Hu Liang L Liu Ling-Yun LY Yue Yun-Hua YH

Brain and behavior 20201212 1

<h4>Background</h4>Inflammation plays an important role in the pathophysiology of stroke. The aim of the present study was to investigate the association between various inflammatory risk markers and ischemic stroke outcome and subtype.<h4>Methods</h4>A total of 3,013 ischemic stroke patients who were admitted to our hospital from 01/01/2016 to 12/30/2018 were retrospectively studied. Stroke subtypes were defined by the Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification. Levels ...[more]

PMID: 33314753

Similar Datasets

Project description:BackgroundInflammation is increasingly recognised as playing a central role in atherosclerosis, and peripheral blood markers of inflammation have been associated with incident and recurrent cardiac events. The relationship of these potentially modifiable risk markers to prognosis after ischaemic stroke is less clear. The Levels of Inflammatory Markers in the Treatment of Stroke (LIMITS) study will address hypotheses related to the role of inflammatory markers in secondary stroke prevention in an efficient manner using the well-established framework of the Secondary Prevention of Small Subcortical Strokes (SPS3) trial (NCT00059306).MethodsSPS3 is an ongoing Phase III multicentre secondary prevention trial focused on preventing recurrent stroke in patients with small vessel ischaemic stroke, or lacunes. In SPS3, patients are assigned in a factorial design to aspirin vs. aspirin plus clopidogrel, and to usual vs. aggressive blood pressure targets. The purpose of LIMITS is to determine whether serum levels of inflammatory markers - including high-sensitivity C-reactive protein, serum amyloid A, CD40 ligand, and monocyte chemoattractant protein-1 - predict recurrent stroke and other vascular events among lacunar stroke patients. The project will also determine whether these markers predict which people will respond best to dual antiplatelet therapy with clopidogrel and aspirin, as well the relationship to cognitive function.Analysisplan Multivariable Cox proportional hazard regression modeling will be used to estimate hazard ratios for the effect of marker levels on risk of recurrent stroke and other outcomes after adjusting for additional potential risk factors, including age, gender, ethnicity, treatment arm, and traditional stroke risk factors. Interactions between marker levels and treatment assignment for both arms of the SPS3 study will be assessed. Observations will be censored at the time of last follow-up visit.ConclusionsLIMITS represents an efficient approach to the identification of novel inflammatory biomarkers for use in risk prediction and treatment selection in patients with small vessel disease.

Project description:Introduction: Ischemic stroke remains one of the most debilitating diseases and is the fifth leading cause of death in the US. The ability to predict stroke outcomes within the acute period of stroke would be essential for care planning and rehabilitation. The Blood and Clot Thrombectomy Registry and Collaboration (BACTRAC; clinicaltrials.gov NCT03153683) study collects arterial blood immediately distal and proximal to the intracranial thrombus at the time of mechanical thrombectomy. These blood samples are an innovative resource in evaluating acute gene expression changes at the time of ischemic stroke. The purpose of this study was to identify inflammatory genes and important immune factors during mechanical thrombectomy for emergent large vessel occlusion (ELVO) and which patient demographics were predictors for stroke outcomes (infarct and/or edema volume) in acute ischemic stroke patients. Methods: The BACTRAC study is a non-probability sampling of male and female subjects (≥18 year old) treated with mechanical thrombectomy for ELVO. We evaluated 28 subjects (66 ± 15.48 years) relative concentrations of mRNA for gene expression in 84 inflammatory molecules in arterial blood distal and proximal to the intracranial thrombus who underwent thrombectomy. We used the machine learning method, Random Forest to predict which inflammatory genes and patient demographics were important features for infarct and edema volumes. To validate the overlapping genes with outcomes, we perform ordinary least squares regression analysis. Results: Machine learning analyses demonstrated that the genes and subject factors CCR4, IFNA2, IL-9, CXCL3, Age, T2DM, IL-7, CCL4, BMI, IL-5, CCR3, TNFα, and IL-27 predicted infarct volume. The genes and subject factor IFNA2, IL-5, CCL11, IL-17C, CCR4, IL-9, IL-7, CCR3, IL-27, T2DM, and CSF2 predicted edema volume. The overlap of genes CCR4, IFNA2, IL-9, IL-7, IL-5, CCR3, and IL-27 with T2DM predicted both infarct and edema volumes. These genes relate to a microenvironment for chemoattraction and proliferation of autoimmune cells, particularly Th2 cells and neutrophils. Conclusions: Machine learning algorithms can be employed to develop prognostic predictive biomarkers for stroke outcomes in ischemic stroke patients, particularly in regard to identifying acute gene expression changes that occur during stroke.

Project description:BackgroundFalls are common following stroke and are frequently related to deficits in balance and mobility. This study aimed to investigate the predictive strength of gait and balance variables for evaluating post-stroke falls risk over 12?months following rehabilitation discharge.MethodsA prospective cohort study was undertaken in inpatient rehabilitation centres based in Australia and Singapore. A consecutive sample of 81 individuals (mean age 63?years; median 24?days post stroke) were assessed within one week prior to discharge. In addition to comfortable gait speed over six metres (6mWT), a depth-sensing camera (Kinect) was used to obtain fast-paced gait speed, stride length, cadence, step width, step length asymmetry, gait speed variability, and mediolateral and vertical pelvic displacement. Balance variables were the step test, timed up and go (TUG), dual-task TUG, and Wii Balance Board-derived centre of pressure velocity during static standing. Falls data were collected using monthly calendars.ResultsOver 12?months, 28% of individuals fell at least once. The faller group had increased TUG time and reduced stride length, gait speed variability, mediolateral and vertical pelvic displacement, and step test scores (P?<?0.001-0.048). Significant predictors, when adjusted for country, prior falls and assistance (i.e., physical assistance and/or gait aid use) were stride length, step length asymmetry, mediolateral pelvic displacement, step test and TUG scores (P?<?0.040; IQR-odds ratio(OR)?=?1.37-7.85). With comfortable gait speed as an additional covariate, to determine the additive benefit over standard clinical assessment, only mediolateral pelvic displacement, TUG and step test scores remained significant (P?=?0.001-0.018; IQR-OR?=?5.28-10.29).ConclusionsReduced displacement of the pelvis in the mediolateral direction during walking was the strongest predictor of post-stroke falls compared with other gait variables. Dynamic balance measures, such as the TUG and step test, may better predict falls than gait speed or static balance measures.

Project description:Background: Numerous studies have focused on the PI3K/AKT/mTOR pathway in estrogen receptor positive (ER) breast cancer (BC), as a linear signal transduction pathway and reported its association with worse clinical outcomes. To gain better insight into the relative contribution of each of the signalling pathways which lie downstream to PI3K (namely AKT and mTOR) to BC outcomes, we have developed a novel in silico approach which assessed the activation of each of these signalling pathways separately. Methods: By analysing gene expression and matched proteomic data in ER-positive patients from the TCGA repository, we developed gene signatures that reflect the level of expression of phosphorylated-Serine473-AKT (pAKT) and phosphorylated-Serine2448-mTOR (p-mTOR) separately, capturing their corresponding level of pathway activation. Using pooled analysis of gene-expression data from over 7,000 patients with ER-positive early BC, we then investigated the association between pathway activation and outcomes. Results: Our analysis revealed that the pAKT pathway activation (as measured by the developed signature) was associated with luminal A BC while the p-mTOR pathway activation was more associated with luminal B BC (Kruskal-Wallis test p<10-10). pAKT pathway activation was significantly associated with better outcomes (multivariable HR, 0.79; 95% CI, 0.74 to 0.85; p=2.5x10-10) whereas p-mTOR pathway activation showed worse outcomes (multivariable HR, 1.1; 95% CI, 1.1 to 1.2; p=9.9x10-4). Similar associations between activation and outcomes were obtained when the analysis was performed in patients who were treated with hormonal therapy. Additionally, pAKT pathway activation predicted better outcomes irrespective of the BC molecular subtypes (luminal A multivariable HR, 0.85; 95% CI, 0.75 to 0.96; p=0.01; luminal B HR, 0.91; 95% CI, 0.83 to 0.99; p=0.033). pAKT pathway activation was associated with PIK3CA mutations (p=0.0001) while p-mTOR pathway activation was associated with p53 mutations (p=0.04). Finally, we show that pAKT pathway activation was associated with less response to Everolimus. Conclusions: Our data show that pAKT and p-mTOR pathway activation have differing impact on prognosis and suggest that they are not linearly connected in luminal breast cancers. These findings add to our understanding of signalling through the PI3K pathway and could have important implications for the treatment of luminal BC.

Project description:Background and purposeAlthough most strokes present with mild symptoms, these have been poorly represented in clinical trials. The objective of this study is to describe multidimensional outcomes, identify predictors of worse outcomes, and explore the effect of thrombolysis in this population.MethodsThis prospective observational study included patients with ischemic stroke or transient ischemic attack, a baseline National Institutes of Health Stroke Scale (NIHSS) score 0 to 5, presenting within 4.5 hours from symptom onset. The primary outcome was a 90-day modified Rankin Scale score of 0 to 1; secondary outcomes included good outcomes in the Barthel Index, Stroke Impact Scale-16, and European Quality of Life. Multivariable models were created to determine predictors of outcomes and the effect of alteplase.ResultsA total of 1765 participants were included from 100 Get With The Guidelines-Stroke participating hospitals (age, 65±14; 42% women; final diagnosis of ischemic stroke, 90%; transient ischemic attack, 10%; 57% received alteplase). At 90 days, 37% were disabled and 25% not independent. Worse outcomes were noted for older individuals, women, non-Hispanic Blacks and Hispanics, Medicaid recipients, smokers, those with diabetes, atrial fibrillation, prior stroke, higher baseline NIHSS, visual field defects, and extremity weakness. Similar outcomes were noted for the alteplase-treated and untreated groups. Alteplase-treated patients were younger (64±13 versus 67±1.4) with higher NIHSS (2.9±1.4 versus 1.7±1.4). After adjusting for age, sex, race/ethnicity, and baseline NIHSS, we did not identify an effect of alteplase on the primary outcome but did find an association with Stroke Impact Scale-16 in the restricted sample of baseline NIHSS score 3–5. Few symptomatic intracerebral hemorrhages were recorded (<1%).ConclusionsA large proportion of stroke patients presenting with low NIHSS have a disabled outcome. Baseline predictors of worse outcomes are described. An effect of alteplase on outcomes was not identified in the overall cohort, but a suggestion of efficacy was noted in the NIHSS 3–5 subgroup.RegistrationURL: https://www.clinicaltrials.gov; Unique identifier: NCT02072681.

Dataset Information

Inflammatory markers as independent predictors for stroke outcomes.

Background

Methods

Results

Conclusion

Publications

Inflammatory markers as independent predictors for stroke outcomes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets