Background/Aims: Coronavirus (CoV) infections induce respiratory tract illnesses and central nervous system (CNS) diseases. We aimed to explore the cytokine expression profiles in hospitalized children with CoV-CNS and CoV-respiratory tract infections. Methods: A total of 183 and 236 hospitalized children with acute encephalitis-like syndrome and respiratory tract infection, respectively, were screened for anti-CoV IgM antibodies. The expression profiles of multiple cytokines were determined in CoV-positive patients. Results: Anti-CoV IgM antibodies were detected in 22/183 (12.02%) and 26/236 (11.02%) patients with acute encephalitis-like syndrome and respiratory tract infection, respectively. Cytokine analysis revealed that the level of serum granulocyte colony-stimulating factor (G-CSF) was significantly higher in both CoV-CNS and CoV-respiratory tract infection compared with healthy controls. Additionally, the serum level of granulocyte macrophage colony-stimulating factor (GM-CSF) was significantly higher in CoV-CNS infection than in CoV-respiratory tract infection. In patients with CoV-CNS infection, the levels of IL-6, IL-8, MCP-1, and GM-CSF were significantly higher in their cerebrospinal fluid samples than in matched serum samples. Conclusion: To the best of our knowledge, this is the first report showing a high incidence of CoV infection in hospitalized children, especially with CNS illness. The characteristic cytokine expression profiles in CoV infection indicate the importance of host immune response in disease progression.
S pontaneous intracerebral hemorrhage (ICH) accounts for 10% to 15% of all strokes and is one of the leading causes of stroke-related mortality and morbidity worldwide. Patients with ICH are generally at risk of developing stroke-associated pneumonia (SAP) during acute hospitalization. Evidence has shown that SAP not only increases the length of hospital stay (LOS) and medical cost 1,2 but also is an important risk factor of mortality and morbidity after acute stroke. 3,4 Several risk factors for SAP have been identified, such as older age, 4-12 male sex, 5,6,10,11,13 current smoking, 12 diabetes mellitus, 6 hypertension, 14 atrial fibrillation, 7,10,12 congestive heart failure, 7,12,13,15 chronic obstructive pulmonary disease, 8,[12][13][14] preexisting dependency, 8,12,13,16 stroke severity, 5,6,8,12,17,18 dysphagia, [8][9][10][11][12]14,[18][19][20] and blood glucose. 12 Meanwhile, based on these risk factors, a few risk models have been developed for SAP after acute ischemic stroke. [8][9][10][11][12] Currently, no valid scoring system is available for predicting SAP after ICH in routine clinical practice or clinical trial. We hypothesized that there might be some common grounds for the development of pneumonia after acute ischemic stroke and ICH, and those predictors for SAP after acute ischemic stroke might also be useful for predicting SAP after ICH. For clinical practice, an effective risk-stratification and prognostic model for SAP after ICH would be helpful to identify vulnerable patients, allocate relevant medical resources, and implement tailored preventive strategies. In addition, for clinical trial, it could be used in nonrandomized studies to control for case-mix variation and in controlled studies as a selection criterion.Background and Purpose-We aimed to develop a risk score (intracerebral hemorrhage-associated pneumonia score, ICH-APS) for predicting hospital-acquired stroke-associated pneumonia (SAP) after ICH. Methods-The ICH-APS was developed based on the China National Stroke Registry (CNSR), in which eligible patients were randomly divided into derivation (60%) and validation (40%) cohorts. Variables routinely collected at presentation were used for predicting SAP after ICH. For testing the added value of hematoma volume measure, we separately developed 2 models with (ICH-APS-B) and without (ICH-APS-A) hematoma volume included. Multivariable logistic regression was performed to identify independent predictors. The area under the receiver operating characteristic curve (AUROC), Hosmer-Lemeshow goodness-of-fit test, and integrated discrimination index were used to assess model discrimination, calibration, and reclassification, respectively. Results-The SAP was 16.4% and 17.7% in the overall derivation (n=2998) and validation (n=2000) cohorts, respectively.A 23-point ICH-APS-A was developed based on a set of predictors and showed good discrimination in the overall derivation (AUROC, 0.75; 95% confidence interval, 0. Ji et al Risk Score to Predict SAP After ICH 2621In the study, we aimed to ...
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