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

ABSTRACT: Background: Stroke is a leading cause of morbidity and mortality. Over the past decade, plasma D-dimer levels have emerged as a biomarker for predicting stroke outcome. However, no consensus in the literature currently exists concerning its utility for predicting post-stroke functional outcome and mortality. Objective: To systematically review the effectiveness of plasma D-dimer levels for predicting functional outcome and mortality following stroke. Methods: Five academic databases were screened according to PRISMA guidelines for eligible studies. With these studies, we conducted a random-effect meta-analysis to evaluate the impact of plasma D-dimer levels for predicting functional outcome and mortality post-stroke. We also conducted subgroup analyses to evaluate differences in predictive capacity for different stroke subtypes. Results: Nineteen studies were included, containing data on 5,781 stroke patients (mean age: 65.26 ± 6.4 years). Overall methodological quality for the included studies was high. Meta-analysis showed that increased D-dimer levels were predictive of worsened functional outcomes (Hazard ratio: 2.19, 95% CI: 1.63-2.93) and elevated overall mortality (2.29, 1.35-3.88). Subgroup analysis showed that plasma D-dimer levels were more predictive of poorer functional outcomes for ischemic (2.08, 1.36-3.18) stroke as compared to intracerebral hemorrhage (2.62, 1.65-4.17). We also noted that predictive capacity was similar when it came to mortality in patients with cryptogenic ischemic stroke (2.65, 0.87-8.08) and intracerebral hemorrhage (2.63, 1.50-4.59). Conclusion: The study provides preliminary evidence concerning the capacity of plasma D-dimer levels for predicting functional outcomes and mortality following stroke and reports that higher D-dimer levels of are associated with poorer functional outcomes and higher mortality.

SUBMITTER: Zhang P

PROVIDER: S-EPMC8289899 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Stroke is a leading cause of disability and mortality, resulting in substantial socio-economic burden for healthcare systems. With advances in artificial intelligence, visual image information can be processed into numerous quantitative features in an objective, repeatable and high-throughput fashion, in a process known as radiomics analysis (RA). Recently, investigators have attempted to apply RA to stroke neuroimaging in the hope of promoting personalized precision medicine. This review aimed to evaluate the role of RA as an adjuvant tool in the prognosis of disability after stroke. We conducted a systematic review following the PRISMA guidelines, searching PubMed and Embase using the keywords: 'magnetic resonance imaging (MRI)', 'radiomics', and 'stroke'. The PROBAST tool was used to assess the risk of bias. Radiomics quality score (RQS) was also applied to evaluate the methodological quality of radiomics studies. Of the 150 abstracts returned by electronic literature research, 6 studies fulfilled the inclusion criteria. Five studies evaluated predictive value for different predictive models (PMs). In all studies, the combined PMs consisting of clinical and radiomics features have achieved the best predictive performance compared to PMs based only on clinical or radiomics features, the results varying from an area under the ROC curve (AUC) of 0.80 (95% CI, 0.75-0.86) to an AUC of 0.92 (95% CI, 0.87-0.97). The median RQS of the included studies was 15, reflecting a moderate methodological quality. Assessing the risk of bias using PROBAST, potential high risk of bias in participants selection was identified. Our findings suggest that combined models integrating both clinical and advanced imaging variables seem to better predict the patients' disability outcome group (favorable outcome: modified Rankin scale (mRS) ≤ 2 and unfavorable outcome: mRS > 2) at three and six months after stroke. Although radiomics studies' findings are significant in research field, these results should be validated in multiple clinical settings in order to help clinicians to provide individual patients with optimal tailor-made treatment.

Project description:Background: Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables. Methods: We included patients from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry, an observational cohort of LVO patients treated with EVT. We applied the following machine learning algorithms: Random Forests, Support Vector Machine, Neural Network, and Super Learner and compared their predictive value with classic logistic regression models using various variable selection methodologies. Outcome variables were good reperfusion (post-mTICI ? 2b) and functional independence (modified Rankin Scale ?2) at 3 months using (1) only baseline variables and (2) baseline and treatment variables. Area under the ROC-curves (AUC) and difference of mean AUC between the models were assessed. Results: We included 1,383 EVT patients, with good reperfusion in 531 (38%) and functional independence in 525 (38%) patients. Machine learning and logistic regression models all performed poorly in predicting good reperfusion (range mean AUC: 0.53-0.57), and moderately in predicting 3-months functional independence (range mean AUC: 0.77-0.79) using only baseline variables. All models performed well in predicting 3-months functional independence using both baseline and treatment variables (range mean AUC: 0.88-0.91) with a negligible difference of mean AUC (0.01; 95%CI: 0.00-0.01) between best performing machine learning algorithm (Random Forests) and best performing logistic regression model (based on prior knowledge). Conclusion: In patients with LVO machine learning algorithms did not outperform logistic regression models in predicting reperfusion and 3-months functional independence after endovascular treatment. For all models at time of admission radiological outcome was more difficult to predict than clinical outcome.

Project description:Background: Ischemic stroke causes increased blood-brain barrier permeability and release of markers of axonal damage and inflammation. To investigate diagnostic and prognostic roles of neurofilament light chain (NF-L), we assessed levels of NF-L, S100B, interleukin-6 (IL-6), E-selectin, vascular endothelial growth factor-A (VEGF-A), vascular cell adhesion molecule-1 (VCAM-1), and intercellular adhesion molecule-1 (ICAM-1) in patients with acute ischemic stroke or transient ischemic attack (TIA) and healthy controls. Methods: We studied neurofilament (NF) expression in 2 cases of human postmortem ischemic stroke, representing infarcts aged 3- to >7-days. In a prospective study, we measured plasma NF-L and inflammatory markers <8 h of symptom onset and at 72 h in acute ischemic stroke (n = 31), TIA (n = 9), and healthy controls (n = 29). We assessed whether NF-L, S100B, and IL-6 were associated with clinical severity on admission (Scandinavian Stroke Scale, SSS), diagnosis of ischemic stroke vs. TIA, and functional outcome at 3 months (modified Rankin Scale, mRS). Results: NF expression increased in ischemic neurons and in the infarcted brain parenchyma after stroke. Plasma NF-L levels were higher in stroke patients than in TIA patients and healthy controls, but IL-6 levels were similar. Higher acute NF-L levels were associated with lower SSS scores at admission and higher mRS scores at 3 months. No correlation was observed between NF-L and S100B, NF-L and IL-6, nor between S100B or IL-6 and SSS or mRS. Compared to controls, stroke patients had significantly higher VEGF-A and VCAM-1 at <8 h that remained elevated at 72 h, with significantly higher VEGF-A at <8 h; ICAM-1 was significantly increased at <8 h, while S100B and E-selectin were unchanged. Conclusions: Plasma NF-L levels, but not IL-6 and S100B, were significant predictors of clinical severity on admission and functional outcome at 3 months. Plasma NF-L is a promising biomarker of functional outcome after ischemic stroke.

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

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