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

Soldozy, Sauson; Yağmurlu, Kaan; Norat, Pedro; Elsarrag, Mazin; Costello, John S; Farzad, Faraz; Sokolowski, Jennifer D.; Sharifi, Khadijeh A.; Elarjani, Turki; Burks, Joshua D.; Tvrdík, Petr; Shaffrey, Mark E.; Park, Min; Starke, Robert M.; Gordon, Errol; Kalani, M. Yashar S.

doi:10.1016/j.wneu.2021.10.157

Cited by 12 publications

(8 citation statements)

References 196 publications

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“…In addition, adding copeptin to the NIHSS improved the prognostic accuracy for the functional outcome (AUC 0.75-0.79) and mortality (AUC 0.85-0.89). Other studies and meta-analyses also demonstrated that copeptin had a high prognostic performance in IS to predict adverse outcomes and mortality (41,153,(220)(221)(222). Furthermore, in a prospective cohort study, copeptin was reported to improve the prognostic ability of the ABCD2 score for the prediction of IS in 31.2% of patients (223), requiring further studies to validate whether the addition of copeptin to the ABCD2 score can help refine the management of patients with the transient ischemic attack (TIA) and reduce healthcare costs.…”

Section: Copeptin In Ischemic Strokementioning

confidence: 97%

Copeptin as a Diagnostic and Prognostic Biomarker in Cardiovascular Diseases

Cheng

Qiu

et al. 2022

Front. Cardiovasc. Med.

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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.

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Section: Copeptin In Ischemic Strokementioning

confidence: 97%

Copeptin as a Diagnostic and Prognostic Biomarker in Cardiovascular Diseases

Cheng

Qiu

et al. 2022

Front. Cardiovasc. Med.

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“…In stroke patients, a recent meta-analysis has identified c-reactive protein, albumin, copeptin, and D-dimer to be significantly associated with long-term outcomes after ischemic BI [ 14 ]. In TBI Patients, novel and emerging predictors include the genetic constitution, advanced magnetic resonance imaging, and biomarkers.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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“…CRP concentration was first found to be an independent predictor of survival after ischemic stroke ( 30 ). Subsequently, studies have shown that various inflammatory parameters, such as activated leukocyte cell adhesion molecule ( 31 ), serum chemokines-12 ( 32 ), high-sensitivity C-reactive protein ( 33 ), E-selectin ( 34 ), Interleukin-6 ( 35 ), monocyte chemoattractant protein ( 36 ), and others, were associated with the risk of poor outcome after stroke ( 37 ). Due to the limitation of a single indicator, new biomarkers combining multiple indicators into a new predictor, such as neutrophil-lymphocyte ratio (NLR), lymphocyte-to-monocyte (LMR), and platelet-lymphocyte ratio (PLR), have been designed to investigate outcomes after AIS.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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BackgroundAcute 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.MethodsClinic 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.ResultsA 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.ConclusionA 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.

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

Cited by 12 publications

References 196 publications

Copeptin as a Diagnostic and Prognostic Biomarker in Cardiovascular Diseases

Copeptin as a Diagnostic and Prognostic Biomarker in Cardiovascular Diseases

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

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

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