Factors associated with non-invasive positive pressure ventilation failure in a COVID-19 Intermediate Care Unit (preprint)

Background: The use of non-invasive positive pressure ventilation (NIPPV) in COVID-19 patients with hypoxaemia is still under debate. The aim was to evaluate the efficacy of NIPPV (CPAP, HELMET-CPAP or NIV) in COVID-19 patients treated in the dedicated COVID-19 Intermediate Care Unit of Coimbra Hospital and University Centre, Portugal, and to assess factors associated with NIPPV failure. Patients admitted from December 1st 2020 to February 28th 2021, treated with NIPPV due to COVID-19 were included. Failure was defined as orotracheal intubation (OTI) or death during hospital stay. Factors associated with NIPPV failure were included in a univariate binary logistic regression analysis; those with a significance level of p < 0.001 entered a multivariate logistic regression model.Results: A total of 163 patients were included, 64.4% were males (n = 105). The median age was 66 years (IQR 56–75). NIPPV failure was observed in 66 (40.5%) patients, 26 (39.4%) were intubated and 40 (60.6%) died during hospital stay. Highest CRP (OR 1.164; 95%CI 1.036–1.308) and morphine use (OR 24.771; 95%CI 1.809-339.241) were identified as predictors of failure after applying multivariate logistic regression. Adherence to prone positioning (OR 0.109; 95%CI 0.017-0.700) and a higher value of the lowest platelet count during hospital stay (OR 0.977; 95%CI 0.960–0.994) were associated with a favourable outcome.Conclusions: NIPPV was successful in 59.5% of patients. Highest CRP during hospital stay and morphine use were predictors of failure. Adherence to prone positioning and a higher value of the lowest platelet count during hospital stay were associated with a favourable outcome.

Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases.

The novel of COVID-19 disease started in late 2019 making the worldwide governments came across a high number of critical and death cases, beyond constant fear of the collapse in their health systems. Since the beginning of the pandemic, researchers and authorities are mainly concerned with carrying out quantitative studies (modeling and predictions) overcoming the scarcity of tests that lead us to under-reporting cases. To address these issues, we introduce a Bayesian approach to the SIR model with correction for under-reporting in the analysis of COVID-19 cases in Brazil. The proposed model was enforced to obtain estimates of important quantities such as the reproductive rate and the average infection period, along with the more likely date when the pandemic peak may occur. Several under-reporting scenarios were considered in the simulation study, showing how impacting is the lack of information in the modeling.

Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases (preprint)

The novel of COVID-19 disease started in late 2019 making the worldwide governments came across a high number of critical and death cases, beyond constant fear of the collapse in their health systems. Since the beginning of the pandemic, researchers and authorities are mainly concerned with carrying out quantitative studies (modeling and predictions) overcoming the scarcity of tests that lead us to under-reporting cases. To address these issues, we introduce a Bayesian approach to the SIR model with correction for under-reporting in the analysis of COVID-19 cases in Brazil. The proposed model was enforced to obtain estimates of important quantities such as the reproductive rate and the average infection period, along with the more likely date when the pandemic peak may occur. Several under-reporting scenarios were considered in the simulation study, showing how impacting is the lack of information in the modeling.