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Background Early detection of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2)‐infected patients who could develop a severe form of COVID‐19 must be considered of great importance to carry out adequate care and optimise the use of limited resources. Aims To use several machine learning classification models to analyse a series of non‐critically ill COVID‐19 patients admitted to a general medicine ward to verify if any clinical variables recorded could predict the clinical outcome. Methods We retrospectively analysed non‐critically ill patients with COVID‐19 admitted to the general ward of the hospital in Pordenone from 1 March 2020 to 30 April 2020. Patients' characteristics were compared based on clinical outcomes. Through several machine learning classification models, some predictors for clinical outcome were detected. Results In the considered period, we analysed 176 consecutive patients admitted: 119 (67.6%) were discharged, 35 (19.9%) dead and 22 (12.5%) were transferred to intensive care unit. The most accurate models were a random forest model (M2) and a conditional inference tree model (M5) (accuracy = 0.79; 95% confidence interval 0.64–0.90, for both). For M2, glomerular filtration rate and creatinine were the most accurate predictors for the outcome, followed by age and fraction‐inspired oxygen. For M5, serum sodium, body temperature and arterial pressure of oxygen and inspiratory fraction of oxygen ratio were the most reliable predictors. Conclusions In non‐critically ill COVID‐19 patients admitted to a medical ward, glomerular filtration rate, creatinine and serum sodium were promising predictors for the clinical outcome. Some factors not determined by COVID‐19, such as age or dementia, influence clinical outcomes.

Objective: Ceftobiprole is an advance generation cephalosporin which has broad-spectrum bacterial activity (both against Gram-positive and negative pathogens) and was approved for the treatment of communityacquired pneumonia (CAP) and non-ventilated hospital-acquired pneumonia (HAP) in most European countries. We aimed to evaluate the efficacy and safety of ceftobiprole in the treatment of pneumonia in a cohort of severely ill patients admitted to the emergency department (ED). Methods: 1-year observational retrospective mono-centric study. Were defined two primary endpoints: first, to evaluate the clinical cure at the test-of-cure (TOC); the second, to evaluate the early improvement, defined as a reduction of symptoms and inflammatory parameters 72 hours after the start of treatment. The secondary endpoint is to evaluate the reduction of antibiotic "burden" using ceftobiprole despite standard of care in severe hospital-acquired pneumonia. Results: During the study period, a total of 48 patients with severe pneumonia received ceftobiprole: twenty-two patients (45.8%) as empiric therapy, 9 (18.5%) as a de-escalation option from previous combination therapies, 13 patients (27.1%) as an escalation therapy from ceftriaxone or amoxicillin/clavulanate and four patients (8.3%) as a targeted therapy based on microbiological results. Ceftobiprole mean duration therapy was 10.2 days. Forty-six patients with severe pneumonia had an early clinical improvement 72 hours after the start of treatment (95.8%). In general, ceftobiprole was well tolerated; only one patient suspended the drug because of poor tolerability. The clinical cure at TOC was 85.4% and 30-days crude mortality was 10.4%. Conclusions: This study confirms that ceftobiprole is effective in severely ill patients with pneumonia at risk of poor outcomes.

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Background Recently many serological assays for detection of antibodies to SARS-COV-2 virus were introduced on the market. Aim of this study was to assess the diagnostic performance of an automated CLIA for quantitative detection of anti-SARS-CoV-2 IgM and IgG antibodies. Methods A total of 354 sera, 89 from consecutive patients diagnosed with COVID-19 (43 mild, 32 severe and 13 critical) and 265 from asymptomatic and negative on rRT-PCR testing healthcare workers, were evaluated for IgM and IgG anti-SARS-CoV-2 antibodies with MAGLUMI immunoasay. Results The overall sensitivity and specificity were 86.5% (95%CI: 77.6-92.8) and 98.5% (95%CI:96.2-99.6), respectively. PPV, PPN, LR+, LR- and OR were 95.1 (95%CI: 87.8-98.6), 95.6 (95%CI: 92.4-97.7), 57.3 (95%CI: 21.6-152.1), 7.3 (95%CI: 4.31-12.4) and 418.6 (95%CI: 131.2-1335.2), respectively. The levels of SARS-CoV-2 IgM and IgG antibodies were 1.22±1.2 AU/mL and 15.86±24.83 AU/mL, 2.86±2.4 AU/mL and 69.3±55.5 AU/mL, 2.47±1.33 AU/mL and 83.9±83.9 AU/mL in mild, severe and critical COVID-19 groups, respectively. A significant difference in antibody levels between mild and severe/critical subjects has been shown. Conclusions The CLIA assay showed good diagnostic performance and a significant association between antibody levels and severity of the disease was found.