Safety of High-Dose Vitamin C in Non-Intensive Care Hospitalized Patients with COVID-19: An Open-Label Clinical Study.

Background: Vitamin C has been used as an antioxidant and has been proven effective in boosting immunity in different diseases, including coronavirus disease (COVID-19). An increasing awareness was directed to the role of intravenous vitamin C in COVID-19. Methods: In this study, we aimed to assess the safety of high-dose intravenous vitamin C added to the conventional regimens for patients with different stages of COVID-19. An open-label clinical trial was conducted on patients with COVID-19. One hundred four patients underwent high-dose intravenous administration of vitamin C (in addition to conventional therapy), precisely 10 g in 250 cc of saline solution in slow infusion (60 drops/min) for three consecutive days. At the same time, 42 patients took the standard-of-care therapy. Results: This study showed the safety of high-dose intravenous administration of vitamin C. No adverse reactions were found. When we evaluated the renal function indices and estimated the glomerular filtration rate (eGRF, calculated with the CKD-EPI Creatinine Equation) as the main side effect and contraindication related to chronic renal failure, no statistically significant differences between the two groups were found. High-dose vitamin C treatment was not associated with a statistically significant reduction in mortality and admission to the intensive care unit, even if the result was bound to the statistical significance. On the contrary, age was independently associated with admission to the intensive care unit and in-hospital mortality as well as noninvasive ventilation (N.I.V.) and continuous positive airway pressure (CPAP) (OR 2.17, 95% CI 1.41-3.35; OR 7.50, 95% CI 1.97-28.54; OR 8.84, 95% CI 2.62-29.88, respectively). When considering the length of hospital stay, treatment with high-dose vitamin C predicts shorter hospitalization (OR -4.95 CI -0.21--9.69). Conclusions: Our findings showed that an intravenous high dose of vitamin C is configured as a safe and promising therapy for patients with moderate to severe COVID-19.

Development and validation of a prognostic model based on comorbidities to predict COVID-19 severity: a population-based study.

BACKGROUND: The prognosis of patients with COVID-19 infection is uncertain. We derived and validated a new risk model for predicting progression to disease severity, hospitalization, admission to intensive care unit (ICU) and mortality in patients with COVID-19 infection (Gal-COVID-19 scores). METHODS: This is a retrospective cohort study of patients with COVID-19 infection confirmed by reverse transcription polymerase chain reaction (RT-PCR) in Galicia, Spain. Data were extracted from electronic health records of patients, including age, sex and comorbidities according to International Classification of Primary Care codes (ICPC-2). Logistic regression models were used to estimate the probability of disease severity. Calibration and discrimination were evaluated to assess model performance. RESULTS: The incidence of infection was 0.39% (10 454 patients). A total of 2492 patients (23.8%) required hospitalization, 284 (2.7%) were admitted to the ICU and 544 (5.2%) died. The variables included in the models to predict severity included age, gender and chronic comorbidities such as cardiovascular disease, diabetes, obesity, hypertension, chronic obstructive pulmonary disease, asthma, liver disease, chronic kidney disease and haematological cancer. The models demonstrated a fair-good fit for predicting hospitalization {AUC [area under the receiver operating characteristics (ROC) curve] 0.77 [95% confidence interval (CI) 0.76, 0.78]}, admission to ICU [AUC 0.83 (95%CI 0.81, 0.85)] and death [AUC 0.89 (95%CI 0.88, 0.90)]. CONCLUSIONS: The Gal-COVID-19 scores provide risk estimates for predicting severity in COVID-19 patients. The ability to predict disease severity may help clinicians prioritize high-risk patients and facilitate the decision making of health authorities.

Single and multiple respiratory virus infections and severity of respiratory disease: a systematic review.

INTRODUCTION: There are suggestions that virus co-infections may influence the clinical outcome of respiratory virus illness. We performed a systematic review of the literature to summarise the evidence. METHODS: MEDLINE, EMBASE, Ovid and WEB of Science databases, major organisation websites and reference lists of published studies were searched. The quality of studies was assessed using the STROBE tool (von Elm et al., 1) Individual study data was analyzed using odds ratios and 95% confidence intervals as a measure of association between exposure (co-infection), patient outcome and results summarised using forest plots and tables RESULTS: Nineteen (19) studies from all over the world were identified and included in the review. Most of the studies 73.7% (14/19) recruited children ≤ 6 years old. Evidence on the role of co-infection in increasing disease severity was inconclusive. In five out of eight studies, co-infection significantly increased risk of admission to general ward (OR: 2.4, 95% CI: 1.3 - 4.4, p = 0.005; OR: 2.4, 95% CI: 1.1 - 7.7, P = 0.04; OR: 3.1, 95% CI: 2.0 - 5.1, p = <0.001; OR: 2.4, 95% CI: 1.7-3.4, p = <0.0001 and OR: 2.3, 95% CI: 1.1 - 5.1, p = 0.34), one found it did not (OR: 0.59, 95% CI: 0.4 - 0.9, p = 0.02) and the other 2 had insignificant results. Similarly on risk of admission to ICU, some studies found that co-infection significantly increased risk of admission to ICU (OR: 2.9, 95% CI: 1.4 - 5.9, p = 0.004 and OR: 3.0, 95% CI: 1.7 - 5.6, p = <0.0001), whereas others did not (OR: 0.18, 95% CI: 0.05 - 0.75, p = 0.02 and OR: 0.3, 95% CI: 0.2 - 0.6, p = <0.0001). There was no evidence for or against respiratory virus co-infections and risk of bronchiolitis or pneumonia. CONCLUSION: The influence of co-infections on severe viral respiratory disease is still unclear. The observed conflict in outcomes could be because they were conducted in different seasons and covered different years and periods. It could also be due to bias towards the null, especially in studies where only crude analysis was conducted. Future studies should employ stratified analysis.