Chinese Stroke Association guidelines for clinical management of cerebrovascular disorders: executive summary and 2019 update on organizational stroke management

Lou, Min; Ding, Jing; Hu, Bo; Zhang, Yusheng; Li, Hao; Tan, Zefeng; Wang, Yan; Xu, Anding

doi:10.1136/svn-2020-000355

Cited by 39 publications

(40 citation statements)

References 9 publications

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“…Among those with infections, stroke-associated pneumonia (SAP), the most acute type of SAI, has the worst impact on functional outcomes [ 3 , 4 ]. The incidence rate of SAP is approximately 10% among stroke patients but could be as high as 40% among high-risk populations [ 5 ]. Except for the relatively high incidence rate, SAP has serious consequences such as increased mortality, extended hospital stays, and deteriorated functional outcomes at discharge [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

ICH-LR2S2: a new risk score for predicting stroke-associated pneumonia from spontaneous intracerebral hemorrhage

Yan

Zhai

et al. 2022

J Transl Med

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Purpose We develop a new risk score to predict patients with stroke-associated pneumonia (SAP) who have an acute intracranial hemorrhage (ICH). Method We applied logistic regression to develop a new risk score called ICH-LR2S2. It was derived from examining a dataset of 70,540 ICH patients between 2015 and 2018 from the Chinese Stroke Center Alliance (CSCA). During the training of ICH-LR2S2, patients were randomly divided into two groups – 80% for the training set and 20% for model validation. A prospective test set was developed using 12,523 patients recruited in 2019. To further verify its effectiveness, we tested ICH-LR2S2 on an external dataset of 24,860 patients from the China National Stroke Registration Management System II (CNSR II). The performance of ICH-LR2S2 was measured by the area under the receiver operating characteristic curve (AUROC). Results The incidence of SAP in the dataset was 25.52%. A 24-point ICH-LR2S2 was developed from independent predictors, including age, modified Rankin Scale, fasting blood glucose, National Institutes of Health Stroke Scale admission score, Glasgow Coma Scale score, C-reactive protein, dysphagia, Chronic Obstructive Pulmonary Disease, and current smoking. The results showed that ICH-LR2S2 achieved an AUC = 0.749 [95% CI 0.739–0.759], which outperforms the best baseline ICH-APS (AUC = 0.704) [95% CI 0.694–0.714]. Compared with the previous ICH risk scores, ICH-LR2S2 incorporates fasting blood glucose and C-reactive protein, improving its discriminative ability. Machine learning methods such as XGboost (AUC = 0.772) [95% CI 0.762–0.782] can further improve our prediction performance. It also performed well when further validated by the external independent cohort of patients (n = 24,860), ICH-LR2S2 AUC = 0.784 [95% CI 0.774–0.794]. Conclusion ICH-LR2S2 accurately distinguishes SAP patients based on easily available clinical features. It can help identify high-risk patients in the early stages of diseases.

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Section: Introductionmentioning

confidence: 99%

ICH-LR2S2: a new risk score for predicting stroke-associated pneumonia from spontaneous intracerebral hemorrhage

Yan

Zhai

et al. 2022

J Transl Med

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“…Received:14 Novamber, 2020, Accepted: 16 Novamber, 2020 Automatic and accurate localization of the carotid artery is a major and important step in the early detection of carotid artery disease and the application of the necessary medical intervention without entering into health complications and treatment before the occurrence of a stroke [4].…”

Section: Introductionmentioning

confidence: 99%

Automatic localization of Common Carotid Artery in ultrasound images using Deep Learning

Hassanin¹,

Abd-Ellah

Khalaf

et al. 2021

Journal of Advanced Engineering Trends

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Accurate and automatic localization of the common carotid artery (CCA) is extremely important because the narrowing of the CCA is a silent disease. CCA disease doesn't cause any symptoms in its early stages, and people don't realize that they usually have a problem until they have a stroke. A stroke occurs when the brain doesn't receive enough blood for a long time. Brain damage from a stroke can lead to loss of speech or vision, and major strokes can cause death. In this paper, we proposed various techniques to localize the CCA in transverse section ultrasound (US) images using deep learning. First, we applied preprocessing to the images in the dataset before detecting the bounding box containing the CCA. We used a faster regional proposal convolutional neural network (Faster R-CNN) to detect the rectangular region (bounding box) around the CCA. Then we applied various localization techniques to localize the CCA in the US images. The proposed method has been performed on ultrasonic transverse images of the signal processing (SP) Lab. We compared our results with the clinicians' circles obtaining a great match between them. The accuracy of the bounding box detection was 97.5 and a Jaccard similarity of 90.86% between our proposed system and the clinicians' manual circles. Our proposed system has shown results that outperform other systems in Literature.

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“…Stroke is a serious global public health problem, with one in four people affected over their lifetime, and is the second leading cause of death and third leading cause of disability in adults worldwide. [ 1 , 2 ] Stroke is defined as a neurological deficit attributed to acute focal injury of the CNS by vascular causes, including hemorrhagic stroke and ischemic stroke. [ 3 , 4 ]…”

Section: Introductionmentioning

confidence: 99%

“…Dysphagia is a common disorder after stroke, with an incidence of 37% to 78%. [ 2 , 5 , 6 ] Most poststroke patients can recover swallowing function with routine therapies; however, for approximately 11% to 50% of patients, inability to swallow is a long-term disability. [ 7 , 8 ] Chronic dysphagia may lead to several complications, such as dehydration, malnutrition and aspiration pneumonia, which consequently result in poor quality of life.…”

Section: Introductionmentioning

confidence: 99%

“…As a branch of traditional meta-analysis, network meta-analysis integrates the existing research and forms an evidence network that can indirectly compare the therapeutic benefits. [ 2 , 11 , 12 ] This study will use network meta-analysis to evaluate the effectiveness and safety of different physical therapy methods for stroke patients with dysphagia, and its conclusion will further guide clinical practice and strive for the best interests of patients.…”

Section: Introductionmentioning

confidence: 99%

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Medicine effectiveness comparisons of different physicotherapeutics for stroke patients with dysphagia

Liu

Xiao

et al. 2020

Medicine

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Background: Oropharyngeal dysphagia is a common disorder after stroke. Physical therapy has been widely used in the rehabilitation of patients with dysphagia after stroke. Due to the lack of randomized trials directly comparing the efficacy of various physical therapies directly, the relative efficacy of these methods is difficult to determined. Therefore, we intend to conduct a network meta-analysis to evaluate the benefits of these physical therapies. Methods: According to the retrieval strategies, randomized controlled trials (RCTs) on physical therapies for stroke patients with dysphagia will be obtained from CNKI, Wan Fang Data, PubMed, Web of science, Embase databases and Cochrane Library, regardless of publication date or language. Studies were screened based on inclusion and exclusion criteria, and the Cochrane risk bias assessment tool will be used to evaluate the quality of the literature. The network meta-analysis will be performed in Markov Chain Monte Carlo (MCMC) method and carried out with Stata14 and OpenBUGS14 software. Ultimately, the evidentiary grade for the results will be evaluated. Results: This study will compare the efficacy of physical therapies in the treatment of stroke patients with dysphagia and suggests a reasonable clinical choice. Conclusion: Our findings will provide references for future guidance developing and clinical decision.

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Chinese Stroke Association guidelines for clinical management of cerebrovascular disorders: executive summary and 2019 update on organizational stroke management

Cited by 39 publications

References 9 publications

ICH-LR2S2: a new risk score for predicting stroke-associated pneumonia from spontaneous intracerebral hemorrhage

ICH-LR2S2: a new risk score for predicting stroke-associated pneumonia from spontaneous intracerebral hemorrhage

Automatic localization of Common Carotid Artery in ultrasound images using Deep Learning

Medicine effectiveness comparisons of different physicotherapeutics for stroke patients with dysphagia

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