Background: The 2019 novel coronavirus disease (COVID-19) emerged in Wuhan, Hubei province, China, and was characterized as pandemic by the World Health Organization. Diabetes mellitus is an established risk factor for poor clinical outcomes, but the association of diabetes with the prognosis of COVID-19 have not been reported yet. Methods: In this cohort study, we retrospectively reviewed 258 consecutive hospitalized COVID-19 patients with or without diabetes at the West Court of Union Hospital of Huazhong University of Science and Technology in Wuhan, China, recruited from January 29 to February 12, 2020. The cases were confirmed by real-time PCR and the demographic, clinical, laboratory, radiological, and treatment data were collected and analyzed. Prognosis was defined as hospitalization, discharged survivor and death, which was followed up until March 12, 2020. Results: Of the 258 hospitalized patients (63 with diabetes) with COVID-19, the median age was 64 years (range 23-91), and 138 (53.5%) were male. No significant differences in age and sex were identified between patients with and without diabetes. Common symptoms included fever (82.2%), dry cough (67.1%), polypnea (48.1%), and fatigue (38%). Patients with diabetes had significantly higher leucocyte and neutrophil counts, and higher levels of fasting blood glucose, serum creatinine, urea nitrogen and creatine kinase isoenzyme MB at admission compared with those without diabetes. COVID-19 patients with diabetes were more likely to develop severe or critical disease condition with more complications at presentation, and had higher incidence rates of antibiotic therapy, non-invasive and invasive mechanical ventilation, and death (11.1% vs. 4.1%). Cox proportional hazard model showed that diabetes (adjusted hazard ratio [aHR]=3.64; 95% confidence interval [CI]: 1.09, 12.21) and fasting blood glucose (aHR=1.19; 95% CI: 1.08, 1.31) were associated with the fatality of COVID-19, adjusting for potential confounders. Conclusions: Diabetes mellitus is associated with greater disease severity and a higher risk of mortality in patients with COVID-19. Primary and secondary prevention strategies are needed for COVID-19 patients with diabetes.

To identify potential predictors for invasive and non-invasive mechanical ventilation in coronavirus disease 2019 (COVID-19) patients. This study retrospectively analyzes data of 516 patients with confirmed COVID-19, who were categorized into three groups based on which mechanical ventilation method was used during the hospitalization period. Among 516 confirmed cases with COVID-19, 446 patients did not receive mechanical ventilation, 38 patients received invasive mechanical ventilation (IMV) and 32 received non-invasive mechanical ventilation (NIMV). The median age of the included patients was 61 years old (interquartile range, 52–69). A total of 432 patients had one or more coexisting illnesses. The main clinical symptoms included fever (79.46%), dry cough (66.47%) and shortness of breath (46.90%). IMV and NIMV patients included more men, more coexisting illnesses and received more medication. Patients in the IMV group and NIMV had higher leukocyte and neutrophil count, lower lymphocyte count, higher aspartate aminotransferase (AST), lactate dehydrogenase (LDH), C-reactive protein (CRP), procalcitonin (PCT) and D-dimer levels and lower albumin (ALB) level. The univariate and multiple logistic regression analysis showed that the use of glucocorticoid, increased neutrophil count and LDH had a predictive role as indicators for IMV, and the use of glucocorticoid, increased neutrophil count and PCT had a predictive role as indicators for NIMV. The area under the curve (AUC) of use of glucocorticoid, increased neutrophil count and LDH was 0.885 (95% confidence interval (CI) 0.838–0.933, p < 0.0001), which provided the specificity and sensitivity 77.7% and 90.9%, respectively. AUC of the use of glucocorticoid, increased neutrophil count and PCT for NIMV was 0.888 (95% CI 0.825–0.952, p < 0.0001), which provided the specificity and sensitivity 70.3% and 96.4%, respectively. Glucocorticoid, increased neutrophil and LDH were predictive indicators for IMV, whereas glucocorticoid, increased neutrophil and PCT were predictive indicators for NIMV. In addition, the above-mentioned mediators had the most predictive meaning for mechanical ventilation when combined. The reviews of this paper are available via the supplemental material section.

Ferroptosis has recently been associated with immunological changes in sepsis. However, the clinical significance of ferroptosis-associated genes (FAGs) remains unknown. In this paper, a FAG-based prognostic model was constructed for sepsis patients using an integrated machine learning approach. The prognosis model was composed of 14 FAGs that classify the patients as high or low risk. Based on immunological study, it was found that the immune status differed between the high-risk and low-risk clusters. Cox regression analysis revealed that FAGs were independent risk factors for the overall survival of sepsis patients. ROC curves and nomograms revealed that the FAG-based model was robust for prognosis prediction. Lastly, NEDD4L was identified from the 14 FAGs as a potential hub gene for sepsis prediction.