Linear mixed model analysis to evaluate correlations between remdesivir adverse effects with age and gender of patients with mild Covid-19 pneumonia.

We aimed to assess longitudinal changes in clinical indexes of corona disease 2019 (Covid-19) patients with mild pulmonary infection during 5 days of remdesivir therapy and determine the effect of age and gender on remdesivir adverse effects (AE). Patients' clinical data including inflammatory markers, liver and renal function tests, and heart rate (HR) were extracted from medical records. Linear mixed model (LMM) was used to analyze longitudinal changes in patients' clinical indexes. Gender and age were inserted in LMM as covariates to find their correlation with AE and clinical indexes. Of 84 patients, 35 patients met our criteria for the study. There were significant increases in mean levels of white blood cell (WBC; p = 0.005), alanine aminotransferase (ALT; p = 0.001), aspartate aminotransferase (p = 0.001), blood urea nitrogen (BUN; p = 0.001), and creatinine (p = 0.006), whereas mean levels of erythrocyte sedimentation rate (p = 0.005), C-reactive protein (p = 0.001), alkaline phosphatase (p = 0.001), and potassium (p = 0.003) decreased significantly. Estimated glomerular filtration rate (p = 0.001) and HR (p = 0.001) showed a notable decline over the course of treatment. LMM analysis showed that mean changes in WBC (ß = 0.94, p = 0.029), creatinine (ß = 0.12, p = 0.020), and HR (ß = 6.47, p = 0.008) were greater in males than in females. Also, age of patients had a significant effect on the mean changes of WBC (ß = -0.02, p = 0.023), sodium (ß = -0.06, p = 0.010), BUN (ß = 0.23, p = 0.001), and HR (ß = -0.29, p = 0.001). Despite no renal and liver dysfunction, Covid-19 patients with mild pulmonary infection may develop some remdesivir AE and attributed side effects might be affected by gender and age of patients.

Which criteria were used to describe patients with COVID-19? A systematic review and meta analysis of clinical, laboratory, and imaging features.

Background: The unknowingness of COVID-19 compared to other respiratory diseases and gaining an overview of its diagnostic criteria led to this study, which was designed to summarize the signs and symptoms along with the clinical tests that described these patients. Methods: PubMed\MEDLINE, Web of Science, Core Collection, Scopus, and Google Scholar were systematically searched on September 27, 2020. After screening, we selected 56 articles based on clinical characteristics and laboratory and imaging findings in confirmed COVID-19 patients as eligibility criteria. To evaluate risk of bias, the Newcastle Ottawa scale, for publication bias, Egger's test, and for heterogeneity, I2 and tau test were used; and finally, random-effects models were used for pooled estimation. Results: Pooled estimates for frequently clinical symptoms were as follows: fever (78% [95% CI, 74-82]), cough (60% [95% CI, 57-63]), and fatigue (31% [95% CI, 26-36]); and they were as follows for laboratory findings in lymphocyte (1.02 [95% CI, 0.92-1.12]), CRP (19.64 [95% CI, 13.96- 25.32]), and platelet count (175.2 [95% CI, 165.2-185.2]); they were as follows for imaging findings in bilateral pneumonia (64% [95% CI, 56-72]), and ground glass opacity (60% [95% CI, 48-7]). Also, in the subgroup analysis, bilateral pneumonia with 18% and fatigue with 15%, had the highest difference in values between the groups. Conclusion: According to Forest plots, the CI and dispersion among studies were smaller in laboratory findings than in symptom and imaging findings, which might indicate a high alignment in the laboratory findings among studies.