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The emergency room (ER) is the first gateway for patients with sepsis to inpatient units, and identifying best practices and benchmarks to be applied in this setting might crucially result in better patient's outcomes. In this study, we want to evaluate the results in terms of decreased the in-hospital mortality of patients with sepsis of a Sepsis Project developed in the ER. All patients admitted to the ER of our Hospital from the 1st January, 2016 to the 31stJuly 2019 with suspect of sepsis (MEWS score ≥ of 3) and positive blood culture upon ER admission were included in this retrospective observational study. The study comprises of two periods: Period A: From the 1st Jan 2016 to the 31st Dec 2017, before the implementation of the Sepsis project. Period B: From the 1st Jan 2018 to the 31stJul 2019, after the implementation of the Sepsis project. To analyze the difference in mortality between the two periods, a univariate and multivariate logistic regression was used. The risk of in-hospital mortality was expressed as an odds ratio (OR) and a 95% confidence interval (95% CI). Overall, 722 patients admitted in ER had positive BC on admissions, 408 in period A and 314 in period B. In-hospital mortality was 18.9% in period A and 12.7% in period B (p = 0.03). At multivariable analysis, mortality was still reduced in period B compared to period A (OR 0.64, 95% CI 0.41-0.98; p = 0.045). Having an infection due to GP bacteria or polymicrobial was associated with an increased risk of death, as it was having a neoplasm or diabetes. A marked reduction in in-hospital mortality of patients with documented BSI associated with signs or symptoms of sepsis after the implementation of a sepsis project based on the application of sepsis bundles in the ER., (© 2023. The Author(s).)

Early detection of the emergence of a new variant of concern (VoC) is essential to develop strategies that contain epidemic outbreaks. For example, knowing in which region a VoC starts spreading enables prompt actions to circumscribe the geographical area where the new variant can spread, by containing it locally. This paper presents 'funnel plots' as a statistical process control method that, unlike tools whose purpose is to identify rises of the reproduction number ([Formula: see text]), detects when a regional [Formula: see text] departs from the national average and thus represents an anomaly. The name of the method refers to the funnel-like shape of the scatter plot that the data take on. Control limits with prescribed false alarm rate are derived from the observation that regional [Formula: see text]'s are normally distributed with variance inversely proportional to the number of infectious cases. The method is validated on public COVID-19 data demonstrating its efficacy in the early detection of SARS-CoV-2 variants in India, South Africa, England, and Italy, as well as of a malfunctioning episode of the diagnostic infrastructure in England, during which the Immensa lab in Wolverhampton gave 43,000 incorrect negative tests relative to South West and West Midlands territories., (© 2023. The Author(s).)

An accurate prediction of the clinical outcomes of European patients requiring hospitalisation for Coronavirus Disease 2019 (COVID-19) is lacking. The aim of the study is to identify predictors of in-hospital mortality and discharge in a cohort of Lombardy patients with COVID-19. All consecutive hospitalised patients from February 21st to March 30th, 2020, with confirmed COVID-19 from the IRCCS Policlinico San Matteo, Pavia, Lombardy, Italy, were included. In-hospital mortality and discharge were evaluated by competing risk analysis. The Fine and Gray model was fitted in order to estimate the effect of covariates on the cumulative incidence functions (CIFs) for in-hospital mortality and discharge. 426 adult patients [median age 68 (IQR 56 to 77 years)] were admitted with confirmed COVID-19 over a 5-week period; 292 (69%) were male. By 21 April 2020, 141 (33%) of these patients had died, 239 (56%) patients had been discharged and 46 (11%) were still hospitalised. Among these 46 patients, updated as of 30 May, 2020, 5 (10.9%) had died, 8 (17.4%) were still in ICU, 12 (26.1%) were transferred to lower intensity care units and 21 (45.7%) were discharged. Regression on the CIFs for in-hospital mortality showed that older age, male sex, number of comorbidities and hospital admission after March 4th were independent risk factors associated with in-hospital mortality. Older age, male sex and number of comorbidities definitively predicted in-hospital mortality in hospitalised patients with COVID-19.