Introduction A new respiratory virus, SARS-CoV-2, has emerged and spread worldwide since late 2019. This study aims at analysing clinical presentation on admission and the determinants associated with admission in intensive care units (ICUs) in hospitalized COVID-19 patients. Patients and methods In this prospective hospital-based study, socio-demographic, clinical and biological characteristics, on admission, of adult COVID-19 hospitalized patients presenting from the community for their first admission were prospectively collected and analysed. Characteristics of patients hospitalized in medical ward to those admitted in ICU were compared using Mann-Whitney and Chi-square or Fisher exact test when appropriate. Univariate logistic regression was first used to identify variables on admission that were associated with the outcome i.e. admission to an ICU versus total hospital stay in a medical ward. Forward selection was then applied beginning with sex, age and temperature in the multivariable logistic regression model. Results Of the 412 patients included, 325 were discharged and 87 died in hospital. Multivariable regression showed increasing odds of ICU hospitalization with temperature (OR, 1.56 [95% CI, 1.06–2.28] per degree Celsius increase), oxygen saturation <90% (OR, 12.45 [95% CI, 5.27–29.4]), abnormal lung auscultation on admission (OR, 3.58 [95% CI, 1.58–8.11]), elevated level of CRP (OR, 2.7 [95% CI, 1.29–5.66for CRP>100mg/L vs CRP<10mg/L). and monocytopenia (OR, 3.28 [95% CI, 1.4–7.68]) were also associated with increasing odds of ICU hospitalization. Older patients were less likely to be hospitalized in ICU (OR, 0.17 [95%CI, 0.05–0.51]. Conclusions Age and delay between onset of symptoms and hospital admission were associated with the risk of hospitalisation in ICU. Age being a fixed variable, interventions that shorten this delay would improve the prognosis of Covid-19 patients.

Between 2013 and 2014 a Vancomycin-resistant Enterococci (VRE) outbreak occurred in a teaching hospital in France. The outbreak was significant possibly due to the lack of implementation of recommended control measures. The aim of this study was to identify the effect of the lack of adherence to control measures for prevention of VRE acquisition in contact patients taking into account individual risk factors. Contact patients (first two months of the outbreak) with VRE acquisition were compared to patients without VRE acquisition (univariate and logistic regression), in terms of institutional characteristics (unit of hospitalization and isolation measures) and risk factors. Between December 2013 and February 2014, 282 contact patients were included in the study. The prevalence of VRE acquisition was 6.4% (18/282). Significant risk factors for VRE acquisition according to logistic regression analysis were; lack of isolation, hospitalization in the same hospital unit as a VRE carrier patient and lack of isolation (RR=856.8, p=0.001), hospitalization in a specific unit (RR=927.4, p=0.002), McCabe score equal to 2 (RR=5233.6, p=0.008), age (RR=1.2 by year, p=0.011), hemodialysis (RR=36.1, p=0.011), central venous catheter (RR=25.4, p=0.021) and surgery (RR=0.012, p=0.007). Antibiotic use was a significant risk factor for VRE acquisition using univariate analysis (p<10). The findings confirm that the factors focused on by the study (lack of isolation and dedicated unit) had a significant effect on VRE acquisition as patient associated factors. It highlights the importance of observance of the guidelines.

The purpose of this paper is to define guidelines to interpret positive blood cultures (BCs) to distinguish bloodstream infection (BSI) from contamination in BCs drawn with a single venipuncture. During a 2-year period, each positive BC set (comprising six bottles from a single venipuncture) was prospectively categorised by clinicians, bacteriologists and hospital epidemiologists as BSI or contamination. For each case, the number of positive bottles per set, results from Gram staining and microorganism identification were analysed in order to define interpretation guidelines. We analysed 940 positive BC sets. The BSI rate in monomicrobial BC sets was positively correlated with the number of positive bottles. The positive predictive value was 88% with one and 100% with ≥2 positive bottles for Escherichia coli; 100% for Staphylococcus aureus, Pseudomonas and Candida spp., regardless of the number of positive bottles; 3.5%, 61.1%, 78.9% and 100% for coagulase-negative staphylococci (CoNS) with one, two, three and ≥4 positive bottles, respectively. Using a single-sampling strategy, interpretation guidelines for monomicrobial positive BCs are based on the number of positive bottles per set, results from Gram staining and microorganism identification: ≥4 positive bottles (≥2 with Gram-negative bacilli) always led to a diagnosis of BSI. The CoNS BSI rate positively correlates with the number of positive bottles.

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