Exploiting an early warning nomogram for predicting the risk of ICU admission in COVID-19 patients: A multi-center study in China (preprint)

Background: Novel coronavirus disease 2019 (COVID-19) is a global public health emergency. Here, we developed and validated a practical model based on the data from a multi-center cohort in China for early identification and prediction of which patients will be admitted to the intensive care unit (ICU). Methods: Data of 1087 patients with laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28, 2020, in Sichuan and Wuhan. Patients were randomly categorized into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator and logistic regression analyzes were used to develop the nomogram. The performance of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. Further, the nomogram was externally validated in a different cohort. Results: The individualized prediction nomogram included 6 predictors: age, respiratory rate, systolic blood pressure, smoking status, fever, and chronic kidney disease. The model demonstrated a high discriminative ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for predicting the risk of ICU admission. Decision curve analysis revealed that the prediction nomogram was clinically useful. Conclusion: We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission, even in community health centers. This model can be conveniently used to predict the individual risk for ICU admission of patients with COVID-19 and optimize the use of limited resources.

Can corticosteroids improve the outcomes of patients with Covid-19? A retrospective cohort study of patients within and outside the epicentre (preprint)

Background The novel coronavirus disease 2019 (Covid-19) has been a worldwide pandemic with more than 300,000 deaths. Corticosteroids have been used in some patients with severe Covid-19 in order to control the systemic inflammation or cytokine storm, however, their effects and safety have not yet been elucidated.Methods Patients with confirmed Covid-19 were retrospectively included from both the epicentre and out of the epicentre. Patients were classified into two groups according to the use of systemic corticosteroids, and the mortality and the rate of virus clearance were compared between the two groups. In addition, independent factors associated with death after corticosteroids treatment were also identified.Results A total of 775 patients were included in our final analysis, of which 238 (30.7%) patients received systemic corticosteroids treatment. Compared with patients without corticosteroids treatment, patients with corticosteroids treatment had significantly higher mortality (19.3% vs. 3.7%, P < 0.001) and lower rate of virus clearance (43.2% vs. 66.7%, P < 0.001) although along with increase of SpO2/FiO2 and blood lymphocytes in patients with severe Covid-19. Corticosteroids treatment was associated with longer hospital length of stays and delayed virus clearance time. In patients with corticosteroids treatment, blood lymphocytes (odds ratio (OR) 0.792, 95% confidence interval (CI) 0.672–0.932, P = 0.005) and creatine kinase (CK) (OR 1.006, 95%CI 1.000-1.012, P = 0.038) were independent risk factors associated with death, with a sensitivity of 90.91% and 44.44% and a specificity of 70.75% and 94.05%, respectively.Conclusions In patients with Covid-19, corticosteroids treatment is associated with increased mortality and reduced rate of virus clearance.

Critical Care for Patients with Severe Covid-2019 in Sichuan Province, China: Provincial Cohort Study (preprint)

Background Data regarding critical care for patients with severe COVID-19 are limited. We aimed to describe the clinical course, multi-strategy management, and respiratory support usage for the severe COVID-19 at the provincial level. Methods Using data from Sichuan Provincial Department of Health and the multicentre cohort study, all microbiologically confirmed COVID-19 patients in Sichuan who met the national severe criteria were included and followed-up from the day of inclusion (D1), until discharge, death, or the end of the study. Findings Out of 539 COVID-19 patients, 81 severe cases (15.0%) were identified. The median (IQR) age was 50 (39-65) years, 37% were female, and 53.1% had chronic comorbidities. All severe cases were identified before requiring mechanical ventilation and treated in the intensive care units (ICUs), among whom 51 (63.0%) were treated in provisional ICUs and 77 patients (95.1%) were admitted by D1. On D1, 76 (93.8%) were administered by respiratory support, including 55 (67.9%) by conventional oxygen therapy (COT), 8 (9.9%) by high-flow nasal cannula (HFNC) and 13 (16.0%) by non-invasive ventilation (NIV). By D28, 53 (65.4%) were discharged, three (3.7%) were deceased, and 25 (30.9%) were still hospitalized. COT, administered to 95.1% of the patients, was the most commonly used respiratory support and met 62.7% of the respiratory support needed, followed by HFNC (19.3%), NIV ventilation (9.4%) and IV 8.5%. Interpretation The multi-strategy management for severe COVID-19 patients including early identification and timely critical care may contribute to the low case-fatailty. Preparation of sufficient conventional oxygen equipment should be prioritized.