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2.
Comput Math Methods Med ; 2020: 9756518, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33014121

RESUMO

The COVID-19 diagnostic approach is mainly divided into two broad categories, a laboratory-based and chest radiography approach. The last few months have witnessed a rapid increase in the number of studies use artificial intelligence (AI) techniques to diagnose COVID-19 with chest computed tomography (CT). In this study, we review the diagnosis of COVID-19 by using chest CT toward AI. We searched ArXiv, MedRxiv, and Google Scholar using the terms "deep learning", "neural networks", "COVID-19", and "chest CT". At the time of writing (August 24, 2020), there have been nearly 100 studies and 30 studies among them were selected for this review. We categorized the studies based on the classification tasks: COVID-19/normal, COVID-19/non-COVID-19, COVID-19/non-COVID-19 pneumonia, and severity. The sensitivity, specificity, precision, accuracy, area under the curve, and F1 score results were reported as high as 100%, 100%, 99.62, 99.87%, 100%, and 99.5%, respectively. However, the presented results should be carefully compared due to the different degrees of difficulty of different classification tasks.


Assuntos
Betacoronavirus , Técnicas de Laboratório Clínico , Infecções por Coronavirus/diagnóstico por imagem , Pandemias , Pneumonia Viral/diagnóstico por imagem , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/estatística & dados numéricos , Inteligência Artificial , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/epidemiologia , Aprendizado Profundo , Humanos , Redes Neurais de Computação , Pneumonia/classificação , Pneumonia/diagnóstico por imagem , Pneumonia Viral/epidemiologia , Interpretação de Imagem Radiográfica Assistida por Computador/estatística & dados numéricos , Radiografia Torácica/estatística & dados numéricos , Sensibilidade e Especificidade
4.
Nat Commun ; 11(1): 4968, 2020 10 02.
Artigo em Inglês | MEDLINE | ID: mdl-33009413

RESUMO

The outbreak of coronavirus disease 2019 (COVID-19) has rapidly spread to become a worldwide emergency. Early identification of patients at risk of progression may facilitate more individually aligned treatment plans and optimized utilization of medical resource. Here we conducted a multicenter retrospective study involving patients with moderate COVID-19 pneumonia to investigate the utility of chest computed tomography (CT) and clinical characteristics to risk-stratify the patients. Our results show that CT severity score is associated with inflammatory levels and that older age, higher neutrophil-to-lymphocyte ratio (NLR), and CT severity score on admission are independent risk factors for short-term progression. The nomogram based on these risk factors shows good calibration and discrimination in the derivation and validation cohorts. These findings have implications for predicting the progression risk of COVID-19 pneumonia patients at the time of admission. CT examination may help risk-stratification and guide the timing of admission.


Assuntos
Infecções por Coronavirus/diagnóstico , Progressão da Doença , Pneumonia Viral/diagnóstico , Pneumonia , Tomografia Computadorizada por Raios X/métodos , Adulto , Betacoronavirus , China , Técnicas de Laboratório Clínico , Coinfecção , Infecções por Coronavirus/patologia , Infecções por Coronavirus/fisiopatologia , Feminino , Hospitalização , Humanos , Pulmão/diagnóstico por imagem , Pulmão/patologia , Linfócitos , Masculino , Pessoa de Meia-Idade , Neutrófilos , Pandemias , Pneumonia Viral/patologia , Pneumonia Viral/fisiopatologia , Análise de Regressão , Estudos Retrospectivos , Medição de Risco , Fatores de Risco
6.
Eur J Med Res ; 25(1): 49, 2020 Oct 12.
Artigo em Inglês | MEDLINE | ID: mdl-33046116

RESUMO

BACKGROUND: The coronavirus disease 2019 (COVID-19) has brought a global disaster. Quantitative lesions may provide the radiological evidence of the severity of pneumonia and further to assess the effect of comorbidity on patients with COVID-19. METHODS: 294 patients with COVID-19 were enrolled from February, 24, 2020 to June, 1, 2020 from six centers. Multi-task Unet network was used to segment the whole lung and lesions from chest CT images. This deep learning method was pre-trained in 650 CT images (550 in primary dataset and 100 in test dataset) with COVID-19 or community-acquired pneumonia and Dice coefficients in test dataset were calculated. 50 CT scans of 50 patients (15 with comorbidity and 35 without comorbidity) were random selected to mark lesions manually. The results will be compared with the automatic segmentation model. Eight quantitative parameters were calculated based on the segmentation results to evaluate the effect of comorbidity on patients with COVID-19. RESULTS: Quantitative segmentation model was proved to be effective and accurate with all Dice coefficients more than 0.85 and all accuracies more than 0.95. Of the 294 patients, 52 (17.7%) patients were reported having at least one comorbidity; 14 (4.8%) having more than one comorbidity. Patients with any comorbidity were older (P < 0.001), had longer incubation period (P < 0.001), were more likely to have abnormal laboratory findings (P < 0.05), and be in severity status (P < 0.001). More lesions (including larger volume of lesion, consolidation, and ground-glass opacity) were shown in patients with any comorbidity than patients without comorbidity (all P < 0.001). More lesions were found on CT images in patients with more comorbidities. The median volumes of lesion, consolidation, and ground-glass opacity in diabetes mellitus group were largest among the groups with single comorbidity that had the incidence rate of top three. CONCLUSIONS: Multi-task Unet network can make quantitative CT analysis of lesions to assess the effect of comorbidity on patients with COVID-19, further to provide the radiological evidence of the severity of pneumonia. More lesions (including GGO and consolidation) were found in CT images of cases with comorbidity. The more comorbidities patients have, the more lesions CT images show.


Assuntos
Algoritmos , Betacoronavirus , Infecções por Coronavirus/epidemiologia , Processamento de Imagem Assistida por Computador/métodos , Pulmão/diagnóstico por imagem , Pneumonia Viral/epidemiologia , Pneumonia/diagnóstico , Tomografia Computadorizada por Raios X/métodos , Adulto , Idoso , Comorbidade , Infecções por Coronavirus/diagnóstico , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia/epidemiologia , Pneumonia Viral/diagnóstico , Reprodutibilidade dos Testes , Estudos Retrospectivos
7.
J Am Heart Assoc ; 9(19): e017297, 2020 10 20.
Artigo em Inglês | MEDLINE | ID: mdl-32998607

RESUMO

Background Angiotensin-converting enzyme inhibitors (ACE-Is) and angiotensin receptor blockers (ARBs) may worsen the prognosis of coronavirus disease 2019, but any association could be confounded by the cardiometabolic conditions indicating ACE-I/ARB use. We therefore examined the impact of ACE-Is/ARBs on respiratory tract infection outcomes. Methods and Results This cohort study included all adult patients hospitalized with influenza or pneumonia from 2005 to 2018 in Denmark using population-based medical databases. Thirty-day mortality and risk of admission to the intensive care unit in ACE-Is/ARBs users was compared with nonusers and with users of calcium channel blockers. We used propensity scores to handle confounding and computed propensity score-weighted risks, risk differences (RDs), and risk ratios (RRs). Of 568 019 patients hospitalized with influenza or pneumonia, 100 278 were ACE-I/ARB users and 37 961 were users of calcium channel blockers. In propensity score-weighted analyses, ACE-I/ARB users had marginally lower 30-day mortality than users of calcium channel blockers (13.9% versus 14.5%; RD, -0.6%; 95% CI, -1.0 to -0.1; RR, 0.96; 95% CI, 0.93-0.99), and a lower risk of admission to the intensive care unit (8.0% versus 9.6%; RD, -1.6%; 95% CI, -2.0 to -1.2; RR, 0.83; 95% CI, 0.80-0.87). Compared with nonusers, current ACE-I/ARB users had lower mortality (RD, -2.4%; 95% CI, -2.8 to -2.0; RR, 0.85; 95% CI, 0.83-0.87), but similar risk of admission to the intensive care unit (RD, 0.4%; 95% CI, 0.0-0.7; RR, 1.04; 95% CI, 1.00-1.09). Conclusions Among patients with influenza or pneumonia, ACE-I/ARB users had no increased risk of admission to the intensive care unit and slightly reduced mortality after controlling for confounding.


Assuntos
Antagonistas de Receptores de Angiotensina/uso terapêutico , Inibidores da Enzima Conversora de Angiotensina/uso terapêutico , Betacoronavirus , Infecções por Coronavirus/tratamento farmacológico , Influenza Humana/tratamento farmacológico , Pneumonia Viral/tratamento farmacológico , Pneumonia/tratamento farmacológico , Sistema Renina-Angiotensina/efeitos dos fármacos , Idoso , Idoso de 80 Anos ou mais , Dinamarca/epidemiologia , Feminino , Seguimentos , Humanos , Incidência , Influenza Humana/epidemiologia , Masculino , Razão de Chances , Pandemias , Pneumonia/epidemiologia , Pontuação de Propensão , Estudos Retrospectivos , Taxa de Sobrevida/tendências
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2186-2189, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018440

RESUMO

Chest radiography has become the modality of choice for diagnosing pneumonia. However, analyzing chest X-ray images may be tedious, time-consuming and requiring expert knowledge that might not be available in less-developed regions. therefore, computer-aided diagnosis systems are needed. Recently, many classification systems based on deep learning have been proposed. Despite their success, the high development cost for deep networks is still a hurdle for deployment. Deep transfer learning (or simply transfer learning) has the merit of reducing the development cost by borrowing architectures from trained models followed by slight fine-tuning of some layers. Nevertheless, whether deep transfer learning is effective over training from scratch in the medical setting remains a research question for many applications. In this work, we investigate the use of deep transfer learning to classify pneumonia among chest X-ray images. Experimental results demonstrated that, with slight fine-tuning, deep transfer learning brings performance advantage over training from scratch. Three models, ResNet-50, Inception V3 and DensetNet121, were trained separately through transfer learning and from scratch. The former can achieve a 4.1% to 52.5% larger area under the curve (AUC) than those obtained by the latter, suggesting the effectiveness of deep transfer learning for classifying pneumonia in chest X-ray images.


Assuntos
Aprendizado Profundo , Pneumonia , Diagnóstico por Computador , Humanos , Pneumonia/diagnóstico por imagem , Radiografia , Raios X
10.
Shanghai Kou Qiang Yi Xue ; 29(4): 435-439, 2020 Aug.
Artigo em Chinês | MEDLINE | ID: mdl-33089298

RESUMO

During the outbreak of novel coronavirus pneumonia (NCP), dentists are at risk for more severe infection due to their professionalism. This article analyzed the route of infection during diagnosis and treatment of oral diseases. Following the related standards and guidelines of National Health Commission, the types, evaluation index and standards of medical and protective masks were summarized. It is expected to provide certain reference for the selection and use of masks of dental medical staff.


Assuntos
Infecções por Coronavirus , Coronavirus , Pandemias , Pneumonia Viral , Pneumonia , Betacoronavirus , Surtos de Doenças , Humanos , Máscaras , Corpo Clínico
11.
Medicine (Baltimore) ; 99(41): e22386, 2020 Oct 09.
Artigo em Inglês | MEDLINE | ID: mdl-33031274

RESUMO

BACKGROUND: This study will assess the efficacy and safety of ventilator for the management of severe pneumonia (SP). METHODS: This study will search the following electronic databases in MEDLINE, EMBASE, Web of Science, PsycINFO, Cochrane Library, CNKI, and Scopus from the beginning to present without language restrictions. Two authors will screen all records according to the eligibility criteria; assess study quality; and extract all essential data from eligible studies. If sufficient studies are included, we will pool the extracted data and carry out meta-analysis. RESULTS: This study will summarize published studies to assess the efficacy and safety of ventilator for patients with SP. CONCLUSION: The results of this study may supply a genuine understanding of perspective from a scientific basis on ventilator for the management of patients with SP.


Assuntos
Pneumonia/terapia , Respiração Artificial , Ventiladores Mecânicos , Humanos , Gravidade do Paciente , Projetos de Pesquisa , Respiração Artificial/efeitos adversos , Respiração Artificial/instrumentação , Revisões Sistemáticas como Assunto , Ventiladores Mecânicos/efeitos adversos
12.
Medicine (Baltimore) ; 99(41): e22567, 2020 Oct 09.
Artigo em Inglês | MEDLINE | ID: mdl-33031304

RESUMO

BACKGROUND: We put the meta-analysis into practice to reveal the relationship between the incidence risk of immune-related pneumonitis and the use of programmed cell death-1 (PD-1) and ligand 1 (PD-L1) inhibitors related pneumonitis in cancer patients. METHOD: The meta-analysis was put into practice according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Odds ratio (OR) was evaluated by random effect model. RESULTS: After screening and eligibility assessment, 33 clinical trials involving 19,854 patients were selected and used for the final meta-analysis after selection criteria checked. Compared with chemotherapy, the use of PD-1/PD-L1 inhibitors alone increased the incidence risk of all-grade (OR = 4.29, 95% confidence interval: [2.97, 6.19], P < .00001) and grade 3 to 5 immune-related pneumonitis (OR = 3.53, 95% confidence interval: [2.04, 6.11], P < .00001). Similar trend could also be found when PD-1/PD-L1 inhibitors were prescribed alone or in combination with other anti-tumor therapies. CONCLUSION: Whether PD-1/PD-L1 inhibitors were used alone or combined with other antitumor drugs, the incidence risk of immune-related pneumonitis would be increased.


Assuntos
Antígeno B7-H1/antagonistas & inibidores , Neoplasias/tratamento farmacológico , Pneumonia/induzido quimicamente , Receptor de Morte Celular Programada 1/antagonistas & inibidores , Humanos , Pneumonia/imunologia
13.
Wiad Lek ; 73(8): 1707-1711, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33055338

RESUMO

OBJECTIVE: The aim: To assess the metabolic by-products of nitric oxide in peripheral blood before and after the medicamentous management in patients suffering from community-acquired pneumonia associated with coronary heart disease. PATIENTS AND METHODS: Materials and methods: We have examined 102 patients with community-acquired pneumonia aged from 50 to 65 years, of which 58 patients were diagnosed with coronary heart disease (CHD). The complex treatment of patients with coronary heart disease was supplemented by the additional use of tivortin aspartate, which was taken orally with food at the dose of 5 ml (1g) 3 times a day for 15 days. The NO content in blood plasma was assessed by the concentration of the amount of final NO metabolites (NO3 + NO2), identified by means of the photocalorimetric method. RESULTS: Results: The content of (NO3 + NO2) in peripheral blood of patients with CAP was slightly higher (6.83 ± 0.29) µmol/l as compared to the group of apparently healthy individuals (5.19 ± 0.14) µmol/l, while in patients with CAP associated with CHD it has markedly increased to (12.74 ± 1.09) µmol/l. Against the background of administered treatment, the index of (NO3 + NO2) in patients with coronary heart disease has decreased to (5.76 ± 0.33) µmol/l, while in the group of patients who were not given tivortin aspartate additionally, this index has even slightly increased (7.01 ± 0.40) µmol/l. CONCLUSION: Conclusions: Marked increase of (NO3 + NO2) levels in blood pointed to destabilization of the course of coronary heart disease with CAP, which was eliminated by the involvement of tivortin aspartate (15 days) to the main course of treatment.


Assuntos
Infecções Comunitárias Adquiridas , Doença das Coronárias , Pneumonia , Idoso , Infecções Comunitárias Adquiridas/complicações , Infecções Comunitárias Adquiridas/tratamento farmacológico , Doença das Coronárias/complicações , Doença das Coronárias/tratamento farmacológico , Humanos , Pessoa de Meia-Idade , Óxido Nítrico , Nitritos , Pneumonia/complicações , Pneumonia/tratamento farmacológico
14.
Nat Commun ; 11(1): 5088, 2020 10 09.
Artigo em Inglês | MEDLINE | ID: mdl-33037212

RESUMO

Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 .


Assuntos
Inteligência Artificial , Infecções por Coronavirus/diagnóstico por imagem , Pneumonia Viral/diagnóstico por imagem , Adulto , Idoso , Idoso de 80 Anos ou mais , Betacoronavirus , Aprendizado Profundo , Diagnóstico Diferencial , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia/diagnóstico por imagem , Curva ROC , Tomografia Computadorizada por Raios X , Adulto Jovem
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 208-212, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017966

RESUMO

Identifying the presence of sputum in the lung is essential in detection of diseases such as lung infection, pneumonia and cancer. Cough type classification (dry/wet) is an effective way of examining presence of lung sputum. This is traditionally done through physical exam in a clinical visit which is subjective and inaccurate. This work proposes an objective approach relying on the acoustic features of the cough sound. A total number of 5971 coughs (5242 dry and 729 wet) were collected from 131 subjects using Smartphone. The data was reviewed and annotated by a novel multi-layer labeling platform. The annotation kappa inter-rater agreement score is measured to be 0.81 and 0.37 for 1st and 2nd layer respectively. Sensitivity and specificity values of 88% and 86% are measured for classification between wet and dry coughs (highest across the literature).


Assuntos
Tosse , Pneumonia , Tosse/diagnóstico , Humanos , Sensibilidade e Especificidade , Som , Escarro
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 768-771, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018099

RESUMO

Respiratory condition has received a great amount of attention nowadays since respiratory diseases recently become the globally leading causes of death. Traditionally, stethoscope is applied in early diagnosis but it requires clinician with extensive training experience to provide accurate diagnosis. Accordingly, a subjective and fast diagnosing solution of respiratory diseases is highly demanded. Adventitious respiratory sounds (ARSs), such as crackle, are mainly concerned during diagnosis since they are indication of various respiratory diseases. Therefore, the characteristics of crackle are informative and valuable regarding to develop a computerised approach for pathology-based diagnosis. In this work, we propose a framework combining random forest classifier and Empirical Mode Decomposition (EMD) method focusing on a multi-classification task of identifying subjects in 6 respiratory conditions (healthy, bronchiectasis, bronchiolitis, COPD, pneumonia and URTI). Specifically, 14 combinations of respiratory sound segments were compared and we found segmentation plays an important role in classifying different respiratory conditions. The classifier with best performance (accuracy = 0.88, precision = 0.91, recall = 0.87, specificity = 0.91, F1-score = 0.81) was trained with features extracted from the combination of early inspiratory phase and entire inspiratory phase. To our best knowledge, we are the first to address the challenging multi-classification problem.


Assuntos
Pneumonia , Transtornos Respiratórios , Estetoscópios , Humanos , Respiração , Sons Respiratórios/diagnóstico
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1238-1241, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018211

RESUMO

Pneumonia is one of the leading causes of childhood mortality worldwide. Chest x-ray (CXR) can aid the diagnosis of pneumonia, but in the case of low contrast images, it is important to include computational tools to aid specialists. Deep learning is an alternative because it can identify patterns automatically, even in low-resolution images. We propose herein a convolutional neural network (CNN) architecture with different training strategies towards detecting pneumonia on CXRs and distinguishing its subforms of bacteria and virus. We also evaluated different image pre-processing methods to improve the classification. This study used CXRs from pediatric patients from a public pneumonia CXR dataset. The pre-processing methods evaluated were image cropping and histogram equalization. To classify the images, we adopted the VGG16 CNN and replaced its fully-connected layers with a customized multilayer perceptron. With this architecture, we proposed and evaluated four different training strategies: original CXR image (baseline), chest-cavity-cropped image (A), and histogram-equalized segmented image (B). The last strategy method (C) implemented is based on ensemble between strategies A and B. The performance was assessed by the area under the ROC curve (AUC) with 95% confidence interval (CI), accuracy, sensitivity, specificity, and F1-score. The ensemble model C yielded the highest performances: AUC of 0.97 (CI: 0.96-0.99) to classify pneumonia vs. normal, and AUC of 0.91 (CI: 0.88-0.94) to classify bacterial vs. viral cases. All models that used pre-processed images showed higher AUC than baseline, which used the original CXR image. Image cropping and histogram equalization reduced irrelevant information from the exam, enhanced contrast, and was able to identify fine CXR texture details. The proposed ensemble model increased the representation of inflammatory patterns from bacteria and viruses with few epochs to train the deep CNNs.Clinical relevance- Deep learning can identify complex radiographic patterns in low contrast images due to pneumonia and distinguish its subforms of bacteria and virus. The correlation of imaging with lab results could accelerate the adoption of complementary exams to confirm the disease's cause.


Assuntos
Aprendizado Profundo , Pneumonia , Criança , Humanos , Redes Neurais de Computação , Pneumonia/diagnóstico por imagem , Tórax , Raios X
18.
Medicine (Baltimore) ; 99(35): e22010, 2020 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-32871955

RESUMO

BACKGROUND: Tanreqing injection, as a kind of traditional Chinese medicine injection widely used in clinic, has the effects of clearing heat, reducing phlegm and detoxifying, and avoids the problems of slow effect and complicated decocting of traditional Chinese medicine. Severe pneumonia is a critical disease of the respiratory system, with symptoms such as dyspnea, shortness of breath, high fever, and coma. Clinical studies have found that Tanreqing injection combined with Western medicine has a good effect in the treatment of severe pneumonia. In order to explore the efficacy and safety of Tanreqing injection combined with antibiotics in the treatment of severe pneumonia, we plan to conduct a systematic evaluation and meta-analysis. METHODS: Randomized controlled trials (RCTs) on the treatment of severe pneumonia with Tanreqing injection combined with western medicine were collected by searching PubMed, The Cochrane Library, Embase, Web of Science, CNKI, Wanfang Database, Weipu Database, and China Biomedical Literature Service System (CBM) by computer with the retrieval time from establishment of database to July 2020. Two researchers independently screened and extracted the literature, and finally evaluated the bias risk of the included study, and meta-analysis was conducted using RevMan5.3 software. RESULTS: The study evaluated the efficacy and safety of Tanreqing injection combined with Western medicine in the treatment of severe pneumonia in terms of total response rate, CURB-65 score, white blood cell count (WBC), antipyretic time (AT), adverse reaction incidence, etc. CONCLUSIONS:: This study will provide a reliable evidence-based basis for the clinical application of Tanreqing injection in the treatment of severe pneumonia. ETHICS AND DISSEMINATION: The private information from individuals will not be published. This systematic review also will not involve endangering participant rights. Ethical approval is not required. The results may be published in a peer-reviewed journal or disseminated in relevant conferences. OSF REGISTRATION NUMBER: DOI 10.17605/OSF.IO/SQDMG.


Assuntos
Medicamentos de Ervas Chinesas/uso terapêutico , Pneumonia/tratamento farmacológico , Humanos , Injeções , Metanálise como Assunto , Revisões Sistemáticas como Assunto
19.
Stroke ; 51(10): 3156-3168, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32897811

RESUMO

Understanding the relationship between infection and stroke has taken on new urgency in the era of the coronavirus disease 2019 (COVID-19) pandemic. This association is not a new concept, as several infections have long been recognized to contribute to stroke risk. The association of infection and stroke is also bidirectional. Although infection can lead to stroke, stroke also induces immune suppression which increases risk of infection. Apart from their short-term effects, emerging evidence suggests that poststroke immune changes may also adversely affect long-term cognitive outcomes in patients with stroke, increasing the risk of poststroke neurodegeneration and dementia. Infections at the time of stroke may also increase immune dysregulation after the stroke, further exacerbating the risk of cognitive decline. This review will cover the role of acute infections, including respiratory infections such as COVID-19, as a trigger for stroke; the role of infectious burden, or the cumulative number of infections throughout life, as a contributor to long-term risk of atherosclerotic disease and stroke; immune dysregulation after stroke and its effect on the risk of stroke-associated infection; and the impact of infection at the time of a stroke on the immune reaction to brain injury and subsequent long-term cognitive and functional outcomes. Finally, we will present a model to conceptualize the many relationships among chronic and acute infections and their short- and long-term neurological consequences. This model will suggest several directions for future research.


Assuntos
Aterosclerose/epidemiologia , Infecções/epidemiologia , Acidente Vascular Cerebral/epidemiologia , Arritmias Cardíacas/epidemiologia , Arritmias Cardíacas/fisiopatologia , Aterosclerose/imunologia , Aterosclerose/fisiopatologia , Bacteriemia/epidemiologia , Bacteriemia/imunologia , Bacteriemia/fisiopatologia , Betacoronavirus , Doença Crônica , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/imunologia , Infecções por Coronavirus/fisiopatologia , Infecções por Citomegalovirus/epidemiologia , Infecções por Citomegalovirus/imunologia , Infecções por Citomegalovirus/fisiopatologia , Endotélio/fisiopatologia , Infecções por HIV/epidemiologia , Infecções por HIV/imunologia , Infecções por HIV/fisiopatologia , Humanos , Hospedeiro Imunocomprometido/imunologia , Infecções/imunologia , Infecções/fisiopatologia , Inflamação/imunologia , Influenza Humana/epidemiologia , Influenza Humana/imunologia , Influenza Humana/fisiopatologia , Pandemias , Ativação Plaquetária , Agregação Plaquetária , Pneumonia/epidemiologia , Pneumonia/imunologia , Pneumonia/fisiopatologia , Pneumonia Viral/epidemiologia , Pneumonia Viral/imunologia , Pneumonia Viral/fisiopatologia , Prognóstico , Fatores de Risco , Acidente Vascular Cerebral/imunologia , Trombose/epidemiologia , Trombose/imunologia , Infecção pelo Vírus da Varicela-Zoster/epidemiologia , Infecção pelo Vírus da Varicela-Zoster/imunologia , Infecção pelo Vírus da Varicela-Zoster/fisiopatologia
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