ABSTRACT
Objectives: The pandemic of the coronavirus disease 2019 (COVID-19) continuously poses a serious threat to public health, highlighting an urgent need for simple and efficient early detection and prediction. Methods: We comprehensively investigated and reanalyzed the published indexes and models for predicting severe illness among COVID‑19 patients in our dataset, and validated them on an independent dataset. Results: 696 COVID-19 cases in the discovery stage and 337 patients in the validation stage were involved. The AuROC of neutrophil to lymphocyte ratio (NLR) (0.782) was significantly higher than that of the other 11 independent risk indexes in severe outcome prediction. The combination of NLR and oxygen saturation (SaO2) (NLR+SaO2) showed the biggest AuROC calculations with a value of 0.901; with a cut-off value of 0.532, it exhibited 84.2% sensitivity, 88.4% specificity and 86.8% correct classification ratio. Moreover, we first identified that principal component analysis (PCA) is an effective tool to predict the severity of COVID-19. We obtained 86.5% prediction accuracy with 86% sensitivity when PCA was applied to predict severe illness. In addition, to evaluate the performance of NLR+SaO2 and PCA, we compared them with currently published predictive models in the same dataset. Conclusions: It showed that NLR+SaO2 is an appropriate and promising method for predicting severe illness, followed by PCA. We then validated the results on an independent dataset and revealed that they remained robust accuracy in outcome prediction. This study is significant for early treatment, intervention, triage and saving limited resources.
Subject(s)
COVID-19ABSTRACT
Background: The pandemic of the coronavirus disease 2019 (COVID-19) has brought a global public health crisis. However, the pathogenesis underlying COVID-19 are barely understood.Methods: In this study, we performed proteomic analyses of airway mucus obtained by bronchoscopy from severe COVID-19 patients. In total, 2351 and 2073 proteins were identified and quantified in COVID-19 patients and healthy controls, respectively.Results: Among them, 92 differentiated expressed proteins (DEPs) (46 up-regulated and 46 down-regulated) were found with a fold change > 1.5 or < 0.67 and a p-value < 0.05, and 375 proteins were uniquely present in airway mucus from COVID-19 patients. Pathway and network enrichment analyses revealed that the 92 DEPs were mostly associated with metabolic, complement and coagulation cascades, lysosome, and cholesterol metabolism pathways, and the 375 COVID-19 only proteins were mainly enriched in amino acid degradation (Valine, Leucine and Isoleucine degradation), amino acid metabolism (beta-Alanine, Tryptophan, Cysteine and Methionine metabolism), oxidative phosphorylation, phagosome, and cholesterol metabolism pathways.Conclusions: This study aims to provide fundamental data for elucidating proteomic changes of COVID-19, which may implicate further investigation of molecular targets directing at specific therapy.Funding Statement: This work was supported by grants from the National Key R&D Program of China (2016YFC0903700), the National Natural Science Foundation of China (81520108001 and 81770043), and grant specific for COVID-19 study from Guangzhou Institute of Respiratory Health.Declaration of Interests: The authors have no conflict of interest to declare.Ethics Approval Statement: All the procedures were approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (No. 2020-65). Verbal informed consent were obtained from all participants because the family members were in quarantine.
Subject(s)
COVID-19ABSTRACT
Objectives: The pandemic of the coronavirus disease 2019 (COVID-19) continuously poses a serious threat to public health, highlighting an urgent need for simple and efficient early detection and prediction. Methods: We comprehensively investigated and reanalyzed the published indexes and models for predicting severe illness among COVID‑19 patients in our dataset, and validated them on an independent dataset. Results: 696 COVID-19 cases in the discovery stage and 337 patients in the validation stage were involved. The AuROC of neutrophil to lymphocyte ratio (NLR) (0.782) was significantly higher than that of the other 11 independent risk indexes in severe outcome prediction. The combination of NLR and oxygen saturation (SaO2) (NLR+SaO2) showed the biggest AuROC calculations with a value of 0.901; with a cut-off value of 0.532, it exhibited 84.2% sensitivity, 88.4% specificity and 86.8% correct classification ratio. Moreover, we first identified that principal component analysis (PCA) is an effective tool to predict the severity of COVID-19. We obtained 86.5% prediction accuracy with 86% sensitivity when PCA was applied to predict severe illness. In addition, to evaluate the performance of NLR+SaO2 and PCA, we compared them with currently published predictive models in the same dataset. Conclusions: It showed that NLR+SaO2 is an appropriate and promising method for predicting severe illness, followed by PCA. We then validated the results on an independent dataset and revealed that they remained robust accuracy in outcome prediction. This study is significant for early treatment, intervention, triage and saving limited resources.
Subject(s)
COVID-19ABSTRACT
Background: The pandemic of the coronavirus disease 2019 (COVID-19) has brought a global public health crisis. However, the pathogenesis underlying COVID-19 are barely understood. Methods: : In this study, we performed proteomic analyses of airway mucus obtained by bronchoscopy from severe COVID-19 patients. In total, 2351 and 2073 proteins were identified and quantified in COVID-19 patients and healthy controls, respectively. Results: : Among them, 92 differentiated expressed proteins (DEPs) (46 up-regulated and 46 down-regulated) were found with a fold change > 1.5 or < 0.67 and a p-value < 0.05, and 375 proteins were uniquely present in airway mucus from COVID-19 patients. Pathway and network enrichment analyses revealed that the 92 DEPs were mostly associated with metabolic, complement and coagulation cascades, lysosome, and cholesterol metabolism pathways, and the 375 COVID-19 only proteins were mainly enriched in amino acid degradation (Valine, Leucine and Isoleucine degradation), amino acid metabolism (beta-Alanine, Tryptophan, Cysteine and Methionine metabolism), oxidative phosphorylation, phagosome, and cholesterol metabolism pathways. Conclusions: : This study aims to provide fundamental data for elucidating proteomic changes of COVID-19, which may implicate further investigation of molecular targets directing at specific therapy.
Subject(s)
COVID-19ABSTRACT
Background: In December 2019, a novel coronavirus (SARS-CoV-2) caused infectious disease, termed COVID-19, outbroke in Wuhan, China. COVID-19 patients manifested as lung injury with complications in other organs, such as liver, heart, gastrointestinal tract, especially for severe cases. However, whether COVID-19 causes significant acute kidney injury (AKI) remained controversial. Methods: We retrospectively analyzed the clinical characteristics, urine and blood routine tests and other laboratory parameters of hospitalized COVID-19 patients in Wuhan Union Hospital. Findings: 178 patients, admitted to Wuhan Union hospital from February 02 to February 29, 2020, were included in this study. No patient (0 [0%]) presented increased serum creatinine (Scr), and 5 (2.8%) patients showed increased blood urea nitrogen (BUN), indicating few cases with kidney dysfunction. However, for patients (83) with no history of kidney disease who received routine urine test upon hospitalization, 45 (54.2%) patients displayed abnormality in urinalysis, such as proteinuria, hematuria and leukocyturia, while none of the patients was recorded to have acute kidney injury (AKI) throughout the study. Meanwhile, the patients with abnormal urinalysis usually had worse disease progression reflecting by laboratory parameters presentations, including markers of liver injury, inflammation, and coagulation. Conclusion: Many patients manifested by abnormal urinalysis on admission, including proteinuria or hematuria. Our results revealed that urinalysis is better in unveiling potential kidney impairment of COVID-19 patients than blood chemistry test and urinalysis could be used to reflect and predict the disease severity. We therefore recommend pay more attention in urinalysis and kidney impairment in COVID-19 patients.