ABSTRACT
BACKGROUND: To investigate and select the useful prognostic parameters to develop and validate a model to predict the mortality risk for severely and critically ill patients with the coronavirus disease 2019 (COVID-19). METHODS: We established a retrospective cohort of patients with laboratory-confirmed COVID-19 (≥18 years old) from two tertiary hospitals: the People's Hospital of Wuhan University and Leishenshan Hospital between February 16, 2020, and April 14, 2020. The diagnosis of the cases was confirmed according to the WHO interim guidance. The data of consecutive severely and critically ill patients with COVID-19 admitted to these hospitals were analyzed. A total of 566 patients from the People's Hospital of Wuhan University were included in the training cohort and 436 patients from Leishenshan Hospital were included in the validation cohort. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used to select the variables and build the mortality risk prediction model. RESULTS: The prediction model was presented as a nomograph and developed based on identified predictors, including age, chronic lung disease, C-reactive protein (CRP), D-dimer levels, neutrophil-to-lymphocyte ratio (NLR), creatinine, and total bilirubin. In the training cohort, the model displayed good discrimination with an AUC of 0.912 [95% confidence interval (CI): 0.884-0.940] and good calibration (intercept = 0; slope = 1). In the validation cohort, the model had an AUC of 0.922 [95% confidence interval (CI): 0.891-0.953] and a good calibration (intercept = 0.056; slope = 1.161). The decision curve analysis (DCA) demonstrated that the nomogram was clinically useful. CONCLUSION: A risk score for severely and critically ill COVID-19 patients' mortality was developed and externally validated. This model can help clinicians to identify individual patients at a high mortality risk.
ABSTRACT
The lung is the primary organ targeted by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), making respiratory failure a leading coronavirus disease 2019 (COVID-19)-related mortality. However, our cellular and molecular understanding of how SARS-CoV-2 infection drives lung pathology is limited. Here we constructed multi-omics and single-nucleus transcriptomic atlases of the lungs of patients with COVID-19, which integrate histological, transcriptomic and proteomic analyses. Our work reveals the molecular basis of pathological hallmarks associated with SARS-CoV-2 infection in different lung and infiltrating immune cell populations. We report molecular fingerprints of hyperinflammation, alveolar epithelial cell exhaustion, vascular changes and fibrosis, and identify parenchymal lung senescence as a molecular state of COVID-19 pathology. Moreover, our data suggest that FOXO3A suppression is a potential mechanism underlying the fibroblast-to-myofibroblast transition associated with COVID-19 pulmonary fibrosis. Our work depicts a comprehensive cellular and molecular atlas of the lungs of patients with COVID-19 and provides insights into SARS-CoV-2-related pulmonary injury, facilitating the identification of biomarkers and development of symptomatic treatments.