Predictors of stroke outcome: the role of hemorheology, natural anticoagulants, and serum albumin

Hashem, Saher; Helmy, Sadek; El-Fayomy, Nervana M.; Oraby, Mohammed I.; Menshawy, Mohammed; Dawood, Nermin A.; Hashem, Heba S.

doi:10.1186/s41983-018-0019-x

Cited by 24 publications

(19 citation statements)

References 28 publications

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“…Global sensitivity analysis indicated that albumin plays an important role in the prognosis of acute stroke. Hashem et al [ 36 ], Aquilani et al [ 37 ], and Xu et al [ 38 ] reported that the high dose of albumin positively affects the prognosis of ischemic stroke patients, which were consistent with the results of this study. Albumin is often regarded as a marker of nutritional status and a negative phase protein that decreases in concentration during injury [ 39 ].…”

Section: Discussionsupporting

confidence: 91%

Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions

Zhou

Jiao

et al. 2022

PLoS ONE

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Background Generalized regression neural network (GRNN) and logistic regression (LR) are extensively used in the medical field; however, the better model for predicting stroke outcome has not been established. The primary goal of this study was to compare the accuracies of GRNN and LR models to identify the most optimal model for the prediction of acute stroke outcome, as well as explore useful biomarkers for predicting the prognosis of acute stroke patients. Method In a single-center study, 216 (80% for the training set and 20% for the test set) acute stroke patients admitted to the Shenzhen Second People’s Hospital between December 2019 to June 2021 were retrospectively recruited. The functional outcomes of the patients were measured using Barthel Index (BI) on discharge. A training set was used to optimize the GRNN and LR models. The test set was utilized to validate and compare the performances of GRNN and LR in predicting acute stroke outcome based on the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and the Kappa value. Result The LR analysis showed that age, the National Institute Health Stroke Scale score, BI index, hemoglobin, and albumin were independently associated with stroke outcome. After validating in test set using these variables, we found that the GRNN model showed a better performance based on AUROC (0.931 vs 0.702), sensitivity (0.933 vs 0.700), specificity (0.889 vs 0.722), accuracy (0.896 vs 0.729), and the Kappa value (0.775 vs 0.416) than the LR model. Conclusion Overall, the GRNN model demonstrated superior performance to the LR model in predicting the prognosis of acute stroke patients. In addition to its advantage in not affected by implicit interactions and complex relationship in the data. Thus, we suggested that GRNN could be served as the optimal statistical model for acute stroke outcome prediction. Simultaneously, prospective validation based on more variables of the GRNN model for the prediction is required in future studies.

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Section: Discussionsupporting

confidence: 91%

Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions

Zhou

Jiao

et al. 2022

PLoS ONE

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“…An increase in the levels of hemorheological indicators is one critical risk factor for stroke [ 56 , 57 ]. Abnormalities in hemorheology have an important impact on the formation of blood clots, and whole blood viscosity, PV, and FIB are all important indicators reflecting hemorheology.…”

Section: Discussionmentioning

confidence: 99%

Clinical Efficacy of Tonic Traditional Chinese Medicine Injection on Acute Cerebral Infarction: A Bayesian Network Meta‐Analysis

Zhou

Xie

Wang³

et al. 2020

Evidence-Based Complementary and Alternative Medicine

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Western medicine (WM) has certain limitations in terms of treating acute cerebral infarction (ACI), while tonic traditional Chinese medicine injections (TCMIs) have been shown to have obvious clinical effects as an adjunct to WM for ACI. However, most randomized controlled trials (RCTs) to date have not performed direct comparisons of efficacy among tonic TCMIs. This study designed a Bayesian network meta-analysis (NMA) to explore the therapeutic effect of tonic TCMIs on ACI. A comprehensive search of RCTs of TCMIs combined with WM for ACI was conducted using electronic databases for studies dated from the start date of each database until February 2020. Stata 13.0 and ADDIS 1.16.7 software were used to plot and analyze the data. Sixty-six RCTs with a total of 5,989 patients involving 7 kinds of tonic TCMIs were included. Among TCMIs, Shenfu injection (SFI) + WM ranked first in terms of improving clinical efficacy and the activities of daily living (ADLs) rating and reducing interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) levels. While Ciwujia injection (CI) + WM was the best choice for reducing neurological impairment and the high-cut viscosity of whole blood (HCV). Shenmai injection (SI) + WM had the greatest effects in terms of decreasing the levels of low-cut viscosity of whole blood (LCV), fibrinogen (FIB), and plasma viscosity (PV). Based on the cluster analysis of the clinical efficacy and the neurological impairment, CI + WM and Shenqifuzheng (SQI) + WM were the best options for treating ACI. With respect to adverse drug reactions (ADRs), 35 RCTs did not monitor ADRs during treatment. In conclusion, tonic TCMIs could assist WM in benefiting patients with ACI. However, due to the limitations of the current study, strict monitoring of ADRs and data from high-quality RCTs will be required in future to verify the advantage of TCMIs.

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“…The early hours after an acute stroke are crucial; it is the most useful time for effective management. With the rising burden of stroke and marked heterogeneity in stroke manifestation and outcome, it is necessary to find accurate, reliable, and simple predictors of functional recovery [12]. Accumulating evidences suggest that oxidative stress and inflammation play a crucial role in the pathophysiology of acute ischemic stroke [13].…”

Section: Discussionmentioning

confidence: 99%

Blood biomarkers for stroke: the role of thioredoxin in diagnosis and prognosis of acute ischemic stroke

Oraby

Rabie

2019

Egypt J Neurol Psychiatry Neurosurg

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Background: Oxidative stress plays a crucial role in the pathophysiology of acute ischemic stroke. Thioredoxin exists and released from cells during inflammation and oxidative stress and was recognized as an oxidative-stress marker. Objective: The objective of this study was to assess the role of thioredoxin as an oxidative stress biomarker in diagnosis and prognosis of acute ischemic stroke in a sample of patients recruited from Beni-Suef Governorate, north Upper Egypt. Methods: A case control study included 100 subjects; 50 patients with first-ever acute ischemic stroke presented within 24 h from the onset and 50 healthy volunteers as a control. Clinical, functional, and radiological evaluation was done for the patients, and all patients and control were subjected to routine laboratory tests and assessment of serum level of thioredoxin by solid-phase sandwich enzyme-linked immunosorbent assay. Results: Thioredoxin was significantly higher in acute stroke patients compared to control group (p value = 0.001). Thioredoxin level was significantly higher in hypertensive patients (p value = 0.007), patients who had carotid stenosis ≥50% (p value = 0.001), patients with poor outcome (p value = 0.009), and in patients with cardio-embolic stroke (p value = 0.001). Significant positive correlation was found between thioredoxin level and volume of infarction (r = 0.501 and p = 0.001), stroke severity at presentation (r = 0.503 and p = 0.021) and clinical outcome after 3 months (r = 0.551 and p value = 0.001). Conclusion: Thioredoxin as a marker of oxidative stress can be used as a new diagnostic and prognostic blood biomarker for stroke.

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Predictors of stroke outcome: the role of hemorheology, natural anticoagulants, and serum albumin

Cited by 24 publications

References 28 publications

Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions

Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions

Clinical Efficacy of Tonic Traditional Chinese Medicine Injection on Acute Cerebral Infarction: A Bayesian Network Meta‐Analysis

Blood biomarkers for stroke: the role of thioredoxin in diagnosis and prognosis of acute ischemic stroke

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