Objectives: Assess the predictive accuracy of the WHO COVID-19 severity classification on COVID-19 hospitalized patients. The secondary aim was to compare its predictive power with a new prediction model, named COVID-19 EPI-SCORE, based on a Bayesian network analysis. Methods: We retrospectively analyzed a population of 295 COVID-19 RT-PCR positive patients hospitalized at Epicura Hospital Center, Belgium, admitted between March 1st and April 30th, 2020. Results: Our cohort’s median age was 73 (62–83) years, and the female proportion was 43%. All patients were classified following WHO severity classification at admission. In total, 125 (42.4%) were classified as Moderate, 69 (23.4%) as Severe, and 101 (34.2%) as Critical. Death proportions through these three classes were 11.2%, 33.3%, and 67.3%, respectively, and the proportions of critically ill patients (dead or needed Invasive Mechanical Ventilation) were 11.2%, 34.8%, and 83.2%, respectively. A Bayesian network analysis was used to create a model to analyze predictive accuracy of the WHO severity classification and to create the EPI-SCORE. The six variables that have been automatically selected by our machine learning algorithm were the WHO severity classification, acute kidney injury, age, Lactate Dehydrogenase Levels (LDH), lymphocytes and activated prothrombin time (aPTT). Receiver Operation Characteristic (ROC) curve indexes hereby obtained were 83.8% and 91% for the models based on WHO classification only and our EPI-SCORE, respectively. Conclusions: Our study shows that the WHO severity classification is reliable in predicting a severe outcome among COVID-19 patients. The addition to this classification of a few clinical and laboratory variables as per our COVID-19 EPI-SCORE has demonstrated to significantly increase its accuracy.

Objective: We investigate the prevalence of the self-reported and objective sudden loss of smell (SLS) in patients with severe coronavirus disease 2019 (COVID-19). Methods: Severe COVID-19 patients with self-reported SLS were recruited at hospitalization discharge. Epidemiological and clinical data were collected. The Sino-nasal Outcome Test-22 (SNOT-22) was used to evaluate rhinological complaints. Subjective olfactory and gustatory functions were assessed with the National Health and Nutrition Examination Survey (NHNES). Objective SLS was evaluated using psychophysical tests. Potential associations between olfactory evaluation and the clinical outcomes (duration of hospitalization; admission biology; one month serology (IgG), and chest computed tomography findings) were studied. Results: Forty-seven patients completed the study (25 females). Subjectively, eighteen (38.3%) individuals self-reported subjective partial or total SLS. Among them, only three and four were anosmic and hyposmic, respectively (38.9%). Considering the objective evaluation in the entire cohort, the prevalence of SLS was 21.3%. Elderly patients and those with diabetes had lower objective olfactory evaluation results than young and non-diabetic individuals. Conclusions: The prevalence of SLS in severe COVID-19 patients appears to be lower than previously estimated in mild-to-moderate COVID-19 forms. Future comparative studies are needed to explore the predictive value of SLS for COVID-19 severity.

Legionella pneumophila can cause a large panel of symptoms besides the classic pneumonia presentation. Here we present a case of fatal nosocomial cellulitis in an immunocompromised patient followed, a year later, by a second case of Legionnaires’ disease in the same ward. While the first case was easily assumed as nosocomial based on the date of symptom onset, the second case required clear typing results to be assigned either as nosocomial and related to the same environmental source as the first case, or community acquired. To untangle this specific question, we applied core-genome multilocus typing (MLST), whole-genome single nucleotide polymorphism and whole-genome MLST methods to a collection of 36 Belgian and 41 international sequence-type 1 (ST1) isolates using both thresholds recommended in the literature and tailored threshold based on local epidemiological data. Based on the thresholds applied to cluster isolates together, the three methods gave different results and no firm conclusion about the nosocomial setting of the second case could been drawn. Our data highlight that despite promising results in the study of outbreaks and for large-scale epidemiological investigations, next-generation sequencing typing methods applied to ST1 outbreak investigation still need standardization regarding both wet-lab protocols and bioinformatics. A deeper evaluation of the L. pneumophila evolutionary clock is also required to increase our understanding of genomic differences between isolates sampled during a clinical infection and in the environment.

Aims. Health care workers (HCWs) are at risk of acquiring the Severe Acute Respiratory Syndrome Coronavirus 2 Infection (SARS-CoV-2). The aim of the study is to determine the SARS-CoV-2 positivity rates during the first epidemiologic peak among HCWs of a south Belgian hospital and to identify risks factors for infection. Methods. All hospital staff who worked during the first epidemiological peak were asked to answer a questionnaire regarding demographical data, function, type of working unit, type of contact with patients, eventual symptomatology, and the positivity of reverse transcription-polymerase chain reaction (RT-PCR) testing or immunoassay. Results. A total of 235 questionnaires were collected; 90 (38%) HCWs tested positive for SARS-CoV-2 from either RT-PCR or immunoassay testing. The positivity rate of HCWs between wards was statistically different (p = 0.004) and was higher in COVID-19 wards than Intensive Care Unit (ICU) and Emergency Department (ED). A total of 114 (49%) HCWs presented SARS-CoV-2-compatible symptomatology; 79 (88%) were positive on either RT-PCR or immunoassay testing; 74 (37%) HCWs were unable to work during the studied period; 5 were hospitalized. No deaths were reported. Multivariate logistic regression modeling showed that having symptoms was highly associated with test positivity (OR 23.3, CI 11.1, 53.1, p-value < 0.001). Working in a COVID-19 ward against working in ICU or ED was also predictive of positivity among HCWs (OR 3.25, CI 1.50, 7.28, p-value = 0.003). Discussion and Conclusions. This study shows a higher positivity rate compared to already reported positivity rates among HCWs. Reported differences in positivity rates depend on many factors, such as local crisis intensity, screening strategy, training in use of self-protective equipment, and study selection bias. HCWs working in COVID-19 wards, in comparison to ED and ICU, seemed at greater risk of being infected in this study. This could be explained by the disparity of HCWs’ experience in handling self-protective equipment and knowledge in infection prevention. Hence, care should be taken in proper training for less-experienced HCWs during hospital epidemics. The latter could increase HCWs’ protection and consequently decrease work absenteeism, ensuring enhanced continuity of patient care during hospital crisis. Rapid quarantine of symptomatic HCWs could reduce contamination rates, as having symptoms was highly associated with test positivity in this study.

Legionella pneumophila can cause a large panel of symptoms beside the classic pneumonia presentation. We present here a case of fatal nosocomial cellulitis in an immunocompromised patient followed, a year later, by a second case of legionnaires’ disease in the same ward. While the first one was easily assumed as nosocomial based on the date of symptoms onset, the other case needed to rely on clear typing results to be either assigned as nosocomial and related to the same environmental source as the first one, or community acquired. To untangle this specific question, we applied Core-genome MultiLocus Typing (MLST), whole-genome Single Nucleotide Polymorphism and whole-genome MLST methods to a collection of 36 Belgian and 41 international sequence-type 1 (ST1) isolates using both thresholds recommended in the literature and tailored threshold based on local epidemiological data. Based on the thresholds applied to cluster isolates together, the three methods gave different results and no firm conclusion about the nosocomial setting of the second case could been drawn. Our data highlight that despite promising results in the study of outbreaks and for large scale epidemiological investigations, NGS typing methods applied to ST1 outbreak investigation still need standardization regarding both wet-lab protocols and bioinformatics. A deeper evaluation of L. pneumophila evolutionary clock is also required to increase our understanding of genomic differences between isolates sampled during a clinical infection and in environment.