ABSTRACT
Background: Early empiric antibiotics were prescribed to numerous patients during the Coronavirus disease 2019(COVID-19) pandemic. However, the potential impact of empiric antibiotic therapy on the clinical outcomes of patients hospitalized with COVID-19 is yet unknown. Methods: We conducted a retrospective cohort study in West China Hospital of Sichuan University between Dec 2022 to Mar 2023. The 1:2 propensity score matched patient populations were further developed to adjust confounding factors. Results: We included a total of 1472 COVID-19 hospitalized patients, of whom 87.4% (1287 patients) received early antibiotic prescriptions. In propensity-score-matched datasets, our analysis comprised 139 patients withnon-antibiotic use(with 278 matched controls) and 27 patients withdeferred-antibiotic use(with 54 matched controls). Patients with older ages, multiple comorbidities, severe and critical COVID-19 subtypes, higher serum infection indicators and inflammatory indicators at admission were more likely to receive early antibiotic prescriptions. After adjusting confounding factors likely to influence the prognosis, no significant difference in all-cause mortality(HR=1.000(0.246-4.060), p=1.000) and ICU admission(HR=0.436(0.093-2.04), p=0.293)), need for mechanical ventilation(HR=0.723(0.296-1.763), p=0.476)) and tracheal intubation(HR=1.338(0.221-8.103), p=0.751)) were observed between early antibiotics use cohort and non-antibiotic use cohort. Conclusions: Early antibiotics were frequently prescribed to patients in more severe disease condition at admission. However, early antibiotic treatment failed to demonstrate better clinical outcomes in hospitalized patients with COVID-19 in the propensity-score-matched cohorts.
Subject(s)
COVID-19ABSTRACT
Coronavirus disease 2019 (COVID-19) caused by acute respiratory syndrome coronavirus 2 (SARS-Cov-2) is still threatening the human life and society throughout the world. For those critically ill patients, mechanical ventilation (MV) is essential to provide life support during treatment. However, both the virus infection and MV disrupt the balance between secretion and elimination of airway mucus and lead to mucus accumulation in the lung. Postmortem examination verified that the lungs in patients died of COVID-19 are indeed filled with sticky mucus, suggesting a great need to improve airway mucus clearance in critically ill COVID-19 patients. Therefore, it may be helpful to comprehensively review the current understanding regarding the changes of biochemical and rheological features of airway mucus associated with the disease, as well as the physiological principles and algorithm to decide airway clearance techniques suitable for the critically ill COVID-19 patients. Based on these considerations, optimized strategies may be developed to eliminate the airway mucus accumulated in the airways of critically ill COVID-19 patients.
ABSTRACT
As of August 27, 2020, the number of cumulative cases of COVID-19 in the US exceeded 5,863,363 and included 180,595 deaths, thus causing a serious public health crisis. Curbing the spread of Covid-19 is still urgently needed. Given the lack of potential vaccines and effective medications, non-pharmaceutical interventions are the major option to curtail the spread of COVID-19. An accurate estimate of the potential impact of different non-pharmaceutical measures on containing, and identify risk factors influencing the spread of COVID-19 is crucial for planning the most effective interventions to curb the spread of COVID-19 and to reduce the deaths. Additive model-based bivariate causal discovery for scalar factors and multivariate Granger causality tests for time series factors are applied to the surveillance data of lab-confirmed Covid-19 cases in the US, University of Maryland Data (UMD) data, and Google mobility data from March 5, 2020 to August 25, 2020 in order to evaluate the contributions of social-biological factors, economics, the Google mobility indexes, and the rate of the virus test to the number of the new cases and number of deaths from COVID-19. We found that active cases/1000 people, workplaces, tests done/1000 people, imported COVID-19 cases, unemployment rate and unemployment claims/1000 people, mobility trends for places of residence (residential), retail and test capacity were the most significant risk factor for the new cases of COVID-19 in 23, 7, 6, 5, 4, 2, 1 and 1 states, respectively, and that active cases/1000 people, workplaces, residential, unemployment rate, imported COVID cases, unemployment claims/1000 people, transit stations, mobility trends (transit) , tests done/1000 people, grocery, testing capacity, retail, percentage of change in consumption, percentage of working from home were the most significant risk factor for the deaths of COVID-19 in 17, 10, 4, 4, 3, 2, 2, 2, 1, 1, 1, 1 states, respectively. We observed that no metrics showed significant evidence in mitigating the COVID-19 epidemic in FL and only a few metrics showed evidence in reducing the number of new cases of COVID-19 in AZ, NY and TX. Our results showed that the majority of non-pharmaceutical interventions had a large effect on slowing the transmission and reducing deaths, and that health interventions were still needed to contain COVID-19.
Subject(s)
COVID-19ABSTRACT
Background: The clinical significance of cardiac troponin measurement in patients hospitalised for coronavirus disease-2019 (covid-19) is uncertain. We investigated the prevalence of elevated troponins in these patients and its prognostic value for predicting mortality. Methods: : Studies were identified by searching electronic databases and preprint servers. We included studies of hospitalised covid-19 patients that reported the frequency of troponin elevations above the upper reference limit and/or the association between troponins and mortality. Meta-analyses were performed using random-effects models. Results: : Forty-four studies were included. Elevated troponins were found in 21.3% (95% confidence interval [CI] 18.0-24.9 %) of patients who received troponin test on hospital admission. Elevated troponins on admission were associated with a higher risk of subsequent death (risk ratio 2.81, 95% CI 2.01-3.93) after adjusting for confounders in multivariable analysis. The pooled sensitivity of elevated admission troponins for predicting death was 0.64 (95% CI 0.58-0.70), and the specificity was 0.88 (0.82-0.92). The post-test probability of death was about 50% for patients with elevated admission troponins, and was about 7% for those with non-elevated troponins on admission. There were significant heterogeneity and publication bias in the analyses, and many included studies were at risk of selection bias due to the lack of systematic troponin measurement and inadequate follow-up. Conclusion: Elevated troponins were relatively common in patients hospitalised for covid-19. Troponin measurement on admission might help in risk stratification, especially in identifying patients at high risk of death when troponin levels are elevated. High-quality prospective studies are needed to validate these findings. Systematic Review Registration: PROSPERO (CRD42020176747).