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Background: Limited and wide-ranging data are available on the recurrent Clostridioides difficile infection (rCDI) incidence rate. Methods: We performed a cohort study with the aim to assess the incidence of and risk factors for rCDI. Adult patients with a first CDI, hospitalized in 15 Italian hospitals, were prospectively included and followed-up for 30 d after the end of antimicrobial treatment for their first CDI. A case–control study was performed to identify risk factors associated with 30-day onset rCDI. Results: Three hundred nine patients with a first CDI were included in the study; 32% of the CDI episodes (99/309) were severe/complicated; complete follow-up was available for 288 patients (19 died during the first CDI episode, and 2 were lost during follow-up). At the end of the study, the crude all-cause mortality rate was 10.7% (33 deaths/309 patients). Two hundred seventy-one patients completed the follow-up; rCDI occurred in 21% of patients (56/271) with an incidence rate of 72/10,000 patient-days. Logistic regression analysis identified exposure to cephalosporin as an independent risk factor associated with rCDI (RR: 1.7; 95% CI: 1.1–2.7, p = 0.03). Conclusion: Our study confirms the relevance of rCDI in terms of morbidity and mortality and provides a reliable estimation of its incidence.

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.

Skin and soft tissue infections (SSTIs) represent a wide range of clinical conditions characterized by a considerable variety of clinical presentations and severity. Their aetiology can also vary, with numerous possible causative pathogens. While other authors previously published analyses on several types of SSTI and on restricted types of patients, we conducted a large nationwide surveillance programme on behalf of the Italian Society of Infectious and Tropical Diseases to assess the clinical and microbiological characteristics of the