Development and validation of predictive models for mortality of cases with COVID-19 (Omicron BA.5.2.48 and B.7.14): A retrospective study (preprint)

Background With the emergence of new COVID-19 variants (Omicron BA.5.2.48 and B.7.14), predicting the mortality of infected patients has become increasingly challenging due to the continuous mutation of the virus. Existing models have shown poor performance and limited clinical utility. This study aims to identify the independent risk factors and develop a practical predictive model for mortality among patients infected with new COVID-19 variants.Methods Demographic, clinical, and laboratory data of COVID-19 patients were retrospectively collected at our hospital between December 22, 2022, and February 15, 2023. Logistic regression (LR), decision tree (DT), and Extreme Gradient Boosting (XGBoost) models were developed to predict mortality. Those models were separately visualized via nomogram, decision trees, and Shapley Additive Explanations (SHAP). To evaluate those models, accuracy, sensitivity, specificity, precision, Youden’s index, and area under curve (AUC, 95% CI) were calculated.Results A total of 987 cases with new COVID-19 variants (Omicron BA.5.2.48 and B.7.14) were eventually included, among them, 153 (15.5%) died. Noninvasive ventilation, intubation, myoglobin, INR, age, number of diagnoses, respiratory, pulse, neutrophil, and albumin were the most important predictors of mortality among new COVID-19 variants. The AUC of LR, DT, and XGBoost models were 0.959, 0.878, and 0.961, respectively. The diagnostic accuracy was 0.926 for LR, 0.913 for DT, and 0.977 for XGBoost. XGBoost model had the highest sensitivity (0.983) and specificity (0.940).Conclusion Our study developed and validated three practical models for predicting mortality in patients with new COVID-19 variants. All models performed well, and XGBoost was the best-performing model.

Investigation of a cluster of COVID-19 cases imported through inbound air flight

Objective: To understand the epidemiological characteristics and explore source of infection of coronavirus disease 2019 (COVID-19) cases imported through an inbound air flight from Kenya to Guangzhou, China.

Comparison of the Respiratory Tractmicrobiome in Hospitalized COVID-19 Patients with Different Disease Severity (preprint)

Background: Little is known about the characteristics of respiratory tract microbiome in Coronavirus disease 2019 (COVID-19) inpatients with different severity. Methods: A cross-sectional study was conducted to characterize respiratory tract microbial communities of 69 COVID-19 inpatients from 64 nasopharyngeal swabs and 5 sputum specimens using 16S ribosomal RNA (rRNA) gene V3-V4 region sequencing. The bacterial profiles were used to find potential biomarkers by the two-step method, the combination of random forest model and the linear discriminant analysis effect size (LEfSe), and explore the connections with clinical characteristics by Spearman’s rank test. Results: : Compared with mild COVID-19 patients, severe patients had significantly decreased bacterial diversity ( P values were less than 0.05 in the alpha and beta diversity) and relative lower abundance of opportunistic pathogens, including Actinomyces , Prevotella , Rothia , Streptococcus , Veillonella . Eight potential biomarkers including Treponema, Lachnoanaerobaculum , Parvimonas, Selenomonas, Alloprevotella , Porphyromonas , Gemella and Streptococcus were found to distinguish the mild COVID-19 patients from the severe COVID-19 patients. The genera of Actinomyces and Prevotella were negatively correlated with age and inpatient days. Intensive Care Unit (ICU) admission, neutrophil count (GRA) and lymphocyte count (LYMPH) were significantly correlated with different genera in the two groups. In addition, there were a positive correlation between Klebsiella and white blood cell count (WBC) in two groups. Conclusion: The respiratory tract microbiome had significant difference in COVID-19 patients with different severity. The value of the respiratory tract microbiome as predictive biomarkers for COVID-19 severity merits further exploration.

Pediatric asthma control during the COVID-19 pandemic: a systematic review and meta-analysis (preprint)

Background: During the current ongoing COVID-19 pandemic, studies had reported that patients with asthma would experience increased asthma-associated morbidity because of the respiratory virus SARS-CoV-2 infection, based on experience with other respiratory viral infections. However, some studies suggested that there was no apparent increase in asthma related morbidity in children with asthma, it is even possible that due to reduced exposures due to confinement, such children may have improved outcomes. In order to understand the impact of Covid-19 on asthma control in children, we performed this systematic review and meta-analysis. Methods: We searched PubMed, Embase, and Cochrane Library to find literature from December 2019 to June 2021 related to Covid-19 and children’s asthma control, among which results such as abstracts, comments, letters, reviews and case reports were excluded. The level of asthma control during the COVID-19 pandemic was synthesized and discussed. Results: A total of 20456 subjects were included in 7 studies. Random effect model is used to account for the data. Compared to the same period before the COVID-19 pandemic, asthma exacerbation, asthma admission, emergency room visit reduced a lot. The outcome of use of inhaled corticosteroids and Beta-2 agonists shows no significant difference. Conclusion: Compared to the same period before the COVID-19 pandemic and the measures in response to it, the level of asthma control has been significantly improved. We need to understand the exact factors leading to these improvements and find methods to sustain it.