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Data on the burden of Clostridioides difficile infection (CDI) in Coronavirus Disease 2019 (COVID-19) patients are scant. We conducted an observational, retrospective, multicenter, 1:3 case (COVID-19 patients with CDI)-control (COVID-19 patients without CDI) study in Italy to assess incidence and outcomes, and to identify risk factors for CDI in COVID-19 patients. From February through July 2020, 8402 COVID-19 patients were admitted to eight Italian hospitals; 38 CDI cases were identified, including 32 hospital-onset-CDI (HO-CDI) and 6 community-onset, healthcare-associated-CDI (CO-HCA-CDI). HO-CDI incidence was 4.4 × 10,000 patient-days. The percentage of cases recovering without complications at discharge (i.e., pressure ulcers, chronic heart decompensation) was lower than among controls (p = 0.01); in-hospital stays was longer among cases, 35.0 versus 19.4 days (p = 0.0007). The presence of a previous hospitalisation (p = 0.001), previous steroid administration (p = 0.008) and the administration of antibiotics during the stay (p = 0.004) were risk factors associated with CDI. In conclusions, CDI complicates COVID-19, mainly in patients with co-morbidities and previous healthcare exposures. Its association with antibiotic usage and hospital acquired bacterial infections should lead to strengthen antimicrobial stewardship programmes and infection prevention and control activities.

Carbapenems such as imipenem and meropenem are not rapidly hydrolyzed by commonly occurring Plactamases. Nevertheless, it was possible, by mutagenesis and selection, to isolate mutant strains of Enterobacter cloacae and Proteus rettgeri that are highly resistant to meropenem and imipenem. Two alterations were noted in the E. cloacae mutants. First, the mutant strains appeared to be strongly derepressed in the production of I8-lactamases, which reached a very high level when the strains were grown in the presence of imipenein. Second, these mutants were deficient in the production of nonspecific porins, as judged by the pattern of outer membrane proteins as well as by reconstitution assays of permeability. As with most porindeficient mutants, their cultures were unstable, and their cultivation in the absence of carbapenems rapidly led to an overgrowth of porin-producing revertants. Analysis of the data suggests that the synergism between the lowered outer membrane permeability and the slow but significant hydrolysis of carbapenems by the overproduced enzymes can explain the resistance phenotypes quantitatively, although the possibility of alteration of the target cannot be excluded at present. With P. reffgeri mutants, there was no indication of further derepression of j8-lactamase, but the enzyme hydrolyzed imipenem much more efficiently than the E. cloacae enzyme did. In addition, the major porin was absent in one mutant strain. These results suggest that a major factor for the carbapenem resistance of these enteric bacteria is the porin deficiency, and this conclusion forms a contrast to the situation in Pseudomonas aeruginosa, in which the most prevalent class of imipenem-resistant mutants appears to lack the specific channel protein D2 yet retains the major nonspecific porin F.

Aims The aim of this study was to estimate a 48 hour prediction of moderate to severe respiratory failure, requiring mechanical ventilation, in hospitalized patients with COVID-19 pneumonia. Methods This was an observational prospective study that comprised consecutive patients with COVID-19 pneumonia admitted to hospital from 21 February to 6 April 2020. The patients’ medical history, demographic, epidemiologic and clinical data were collected in an electronic patient chart. The dataset was used to train predictive models using an established machine learning framework leveraging a hybrid approach where clinical expertise is applied alongside a data-driven analysis. The study outcome was the onset of moderate to severe respiratory failure defined as PaO2/FiO2 ratio <150 mmHg in at least one of two consecutive arterial blood gas analyses in the following 48 hours. Shapley Additive exPlanations values were used to quantify the positive or negative impact of each variable included in each model on the predicted outcome. Results A total of 198 patients contributed to generate 1068 usable observations which allowed to build 3 predictive models based respectively on 31-variables signs and symptoms, 39-variables laboratory biomarkers and 91-variables as a composition of the two. A fourth “boosted mixed model” included 20 variables was selected from the model 3, achieved the best predictive performance (AUC = 0.84) without worsening the FN rate. Its clinical performance was applied in a narrative case report as an example. Conclusion This study developed a machine model with 84% prediction accuracy, which is able to assist clinicians in decision making process and contribute to develop new analytics to improve care at high technology readiness levels.