Metabolomics acts as a powerful tool for comprehensively evaluating vaccines approved under emergency: a CoronaVac retrospective study

The coronavirus disease 2019 (COVID-19) has a significant impact on the global population, particularly on individuals with chronic kidney disease (CKD). COVID-19 patients with CKD will face a considerably higher risk of mortality than the general population. This study developed a predictive model for assessing mortality in COVID-19-affected CKD patients, providing personalized risk prediction to optimize clinical management and reduce mortality rates. We developed machine learning algorithms to analyze 219 patients’ clinical laboratory test data retrospectively. The performance of each model was assessed using a calibration curve, decision curve analysis, and receiver operating characteristic (ROC) curve. It was found that the LightGBM model showed the most satisfied performance, with an area under the ROC curve of 0.833, sensitivity of 0.952, and specificity of 0.714. Prealbumin, neutrophil percent, respiratory index in arterial blood, half-saturated pressure of oxygen, carbon dioxide in serum, glucose, neutrophil count, and uric acid were the top 8 significant variables in the prediction model. Validation by 46 patients demonstrated acceptable accuracy. This model can serve as a powerful tool for screening CKD patients at high risk of COVID-19-related mortality and providing decision support for clinical staff, enabling efficient allocation of resources, and facilitating timely and targeted management for those who need the relevant interference urgently. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-78498-w.

show abstract

Respiratory Tract Pathogen Profiles of COVID-19 Pneumonia Patients and the Mortality Prediction

Liu,

Fan

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Background COVID-19 pneumonia is easily complicated with other respiratory pathogenic attacks, increasing the risk of death. Exploring the pathogen profiles of COVID-19 patients-related facilitated the clinical management and decisions to pursue better prognosis. Methods This study systematically compared the sputum culture results and death events of 170 non-COVID-19 and 197 COVID-19 patients. Statistical analysis was carried out to find the pathogen profile difference between the two populations. The death risk model was constructed for the infected COVID-19. Results It was found that co-infection with bacteria and fungi increased the mortality of COVID-19 pneumonia patients. The isolation rate of Acinetobacter baumannii in COVID-19 patients was significantly higher than that in non-COVID-19 patients and often showed multi-drug resistant phenotypes. The COVID-19 pneumonia patients showed a higher incidence of intensive care unit admission, ventilator-assisted ventilation and death with fungal infection. The serum levels of interleukin-1, interleukin-6, interleukin-8, TNF, lymphocytes, neutrophils and white blood cells in patients with COVID-19 pneumonia decreased. A death prediction model was constructed based on machine learning methods, achieving a prediction accuracy of 90.0%. The main factors affecting the survival rate of COVID-19 pneumonia patients co-infected with other pathogens were admission to the intensive care unit, days of hospital stay, ventilator-aided treatment, carbapenems administration, lymphocyte, serum aspartate aminotransferase level, Acinetobacter baumannii infection, and Candida infection. Conclusions This study provided necessary clinical indicators for timely and precise intervention of COVID-19 pneumonia patients when they were infected by other pathogens. The COVID-19-related secondary infection microorganisms were different compared with the pathogens isolated from non-COVID-19 patients.

show abstract

Metabolomics acts as a powerful tool for comprehensively evaluating vaccines approved under emergency: a CoronaVac retrospective study

Cited by 4 publications

References 54 publications

Generating prophylactic immunity against arboviruses in vertebrates and invertebrates

Generating prophylactic immunity against arboviruses in vertebrates and invertebrates

Predictive modeling of COVID-19 mortality risk in chronic kidney disease patients using multiple machine learning algorithms

Respiratory Tract Pathogen Profiles of COVID-19 Pneumonia Patients and the Mortality Prediction

Contact Info

Product

Resources

About