Search

Background/objectives: We investigated whether behavioral precautions adopted during Coronavirus disease (COVID-19) pandemic also influenced the spreading and multidrug resistance (MDR) of ESKAPEEc (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii [AB], Pseudomonas aeruginosa, Enterobacter spp and Escherichia Coli, [EC]) among Intensive Care Unit (ICU) patients., Subjects/methods: We performed a single-center retrospective study in adult patients admitted to our COVID-19-free surgical ICU. Only patients staying in ICU for more than 48 hours were included. The ESKAPEEc infections recorded during the COVID-19 period (June 1, 2020 - February 28, 2021) and in the corresponding pre-pandemic period (June 1, 2019 - February 28, 2020) were compared. An interrupted time series analysis was performed to rule out possible confounders., Results: Overall, 173 patients in the COVID-19 period and 132 in the pre-COVID-19 period were investigated. The ESKAPEEc infections were documented in 23 (13.3%) and 35 (26.5%) patients in the pandemic and the pre-pandemic periods, respectively (p = 0.005). Demographics, diagnosis, comorbidities, type of surgery, Simplified Acute Physiology Score II, length of mechanical ventilation, hospital and ICU length of stay, ICU death rate, and 28-day hospital mortality were similar in the two groups. In comparison with the pre-pandemic period, no AB was recorded during COVID-19 period, (p = 0.017), while extended-spectrum beta-lactamase-producing EC infections significantly decreased (p = 0.017). Overall, the ESKAPEEc isolates during pandemic less frequently exhibited multidrug-resistant (p = 0.014)., Conclusions: These findings suggest that a robust adherence to hygiene measures together with human contact restrictions in a COVID-19 free ICU might also restrain the transmission of ESKAPEEc pathogens., (© 2021 The Authors. European Journal of Clinical Investigation published by John Wiley & Sons Ltd on behalf of Stichting European Society for Clinical Investigation Journal Foundation.)

Objective: To develop a deep learning-based decision tree for the primary care setting, to stratify adult patients with confirmed and unconfirmed coronavirus disease 2019 (COVID-19), and to predict the need for hospitalization or home monitoring., Patients and Methods: We performed a retrospective cohort study on data from patients admitted to a COVID hospital in Rome, Italy, between 5 March 2020 and 5 June 2020. A confirmed case was defined as a patient with a positive nasopharyngeal RT-PCR test result, while an unconfirmed case had negative results on repeated swabs. Patients' medical history and clinical, laboratory and radiological findings were collected, and the dataset was used to train a predictive model for COVID-19 severity., Results: Data of 198 patients were included in the study. Twenty-eight (14.14%) had mild disease, 62 (31.31%) had moderate disease, 64 (32.32%) had severe disease, and 44 (22.22%) had critical disease. The G2 value assessed the contribution of each collected value to decision tree building. On this basis, SpO2 (%) with a cut point at 92 was chosen for the optimal first split. Therefore, the decision tree was built using values maximizing G2 and LogWorth. After the tree was built, the correspondence between inputs and outcomes was validated., Conclusions: We developed a machine learning-based tool that is easy to understand and apply. It provides good discrimination in stratifying confirmed and unconfirmed COVID-19 patients with different prognoses in every context. Our tool might allow general practitioners visiting patients at home to decide whether the patient needs to be hospitalized.