Background Asthmatics and COPD patients have more severe outcomes with viral infections than people without obstructive disease. Objective To evaluate if obstructive diseases are risk factors for ICU stay and death due to COVID19. Methods We collected data from the electronic medical record from 596 adult patients hospitalized in University hospital of Liege between 18 th of March and 17th of April 2020 for SARS-CoV2 infection. We classified patients in three groups according to the underlying respiratory disease, present prior to COVID19 pandemics. Results Among patients requiring hospitalization for COVID19, asthma and COPD accounted for 9.6% and 7.7% respectively. The proportions of asthmatics, COPD and patients without obstructive airway disease hospitalized in ICU were 17.5%, 19.6% and 14% respectively. One third of COPD patients died during hospitalization while only 7.0% of asthmatics and 13.6% of patients without airway obstruction died due to SARS-CoV2. The multivariate analysis showed that asthma, COPD, ICS treatment and OCS treatment were not independent risk factors for ICU admission or death. Male gender (OR:1.9; 95%CI: 1.1 to 3.2) and obesity (OR:8.5; 95%CI: 5.1 to 14.1) were predictors of ICU admission while male gender (OR1.9; 95%CI: 1.1-3.2), older age (OR:1.9; 95%CI: 1.6-2.3), cardiopathy (OR: 1.8; 95%CI: 1.1-3.1) and immunosuppressive diseases (OR: 3.6; 95%CI: 1.5-8.4) were independent predictors of death. Conclusion Asthma and COPD are not risk factors for ICU admission and death related to SARS-CoV2 infection.

Background The coronavirus infectious disease 19 (COVID-19) pandemic has resulted in significant morbidities, severe acute respiratory failures and subsequently emergency departments’ (EDs) overcrowding in a context of insufficient laboratory testing capacities. The development of decision support tools for real-time clinical diagnosis of COVID-19 is of prime importance to assist patients’ triage and allocate resources for patients at risk. Methods and principal findings From March 2 to June 15, 2020, clinical patterns of COVID-19 suspected patients at admission to the EDs of Liège University Hospital, consisting in the recording of eleven symptoms (i.e. dyspnoea, chest pain, rhinorrhoea, sore throat, dry cough, wet cough, diarrhoea, headache, myalgia, fever and anosmia) plus age and gender, were investigated during the first COVID-19 pandemic wave. Indeed, 573 SARS-CoV-2 cases confirmed by qRT-PCR before mid-June 2020, and 1579 suspected cases that were subsequently determined to be qRT-PCR negative for the detection of SARS-CoV-2 were enrolled in this study. Using multivariate binary logistic regression, two most relevant symptoms of COVID-19 were identified in addition of the age of the patient, i.e. fever (odds ratio [OR] = 3.66; 95% CI: 2.97–4.50), dry cough (OR = 1.71; 95% CI: 1.39–2.12), and patients older than 56.5 y (OR = 2.07; 95% CI: 1.67–2.58). Two additional symptoms (chest pain and sore throat) appeared significantly less associated to the confirmed COVID-19 cases with the same OR = 0.73 (95% CI: 0.56–0.94). An overall pondered (by OR) score (OPS) was calculated using all significant predictors. A receiver operating characteristic (ROC) curve was generated and the area under the ROC curve was 0.71 (95% CI: 0.68–0.73) rendering the use of the OPS to discriminate COVID-19 confirmed and unconfirmed patients. The main predictors were confirmed using both sensitivity analysis and classification tree analysis. Interestingly, a significant negative correlation was observed between the OPS and the cycle threshold (Ct values) of the qRT-PCR. Conclusion and main significance The proposed approach allows for the use of an interactive and adaptive clinical decision support tool. Using the clinical algorithm developed, a web-based user-interface was created to help nurses and clinicians from EDs with the triage of patients during the second COVID-19 wave.

Objectives: For years, general practitioners (GP) shortage and patients' increasing demand for acute care have been associated with Emergency Department (ED) crowding. Indeed, EDs admissions for non-emergency care seem to constantly increase. Surprisingly, the rationale for patients own decision to directly reach EDs over primary care have been poorly investigated to date. Methods: We conducted a study on patients admitted in two University EDs during nine consecutive days. Patients were asked to answer a survey about their frames for coming and if they were self-referred, referred by a GP, a specialist or after calling the Emergency Number. Results: During the study period, 68.0% of patients were self-referred, 17.0% referred by their GP, 8.5% by a specialist and 7% after an emergency call. 51.0% of the self-referrals thought EDs were the appropriate location to deal with their health problem and 24.0% because of a better accessibility. We noticed that 15.0% of the incomings looked for specialized care and 4.22% reported that the stress had motivated them. Of note, 4.6% of the patients were attracted by the hospital reputation. Financial concerns represented less than 1.0% of the motives invocated. Conclusion: We found that patients' self-perceived severity of illness is the predominant frame to each the ED when they face needs for acute care. EDs' accessibility as compared with other facilities also seems to encourage patients to come to the ED. Other factors such as the hospital reputation or patients' stress tend to influence ED attendance but to a much lesser extent.

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Objectives: Since the beginning of the novel coronavirus outbreak, different strategies have been explored to stem the spread of the disease and appropriately manage patient flow. Triage, an effective solution proposed in disaster medicine, also works well to manage Emergency Department (ED) flow. The aim of this study was to describe the role of an ED Triage Center for patients with suspected novel coronavirus disease (Covid-19) and characterize the patient flow. Methods: In March 2020, we established a Covid-19 triage center close to the Liège University EDs. From March 2 to March 23, we planned to analyze the specific flow of patients admitted to this triage zone and their characteristics in terms of inner specificities, work-up and management. During this period, all patients presented to the ED with symptoms suggestive of Covid-19 were included in the study. Results: A total amount of 1071 patients presented to the triage center during the study period. 41.50% of the patients presented with flu-like symptoms. In 82.00% of the cases, no risk factor of virus transmission was found. The SARS-Cov2 positive patients represented 29.26% of the screened patients. 83.00% of patients were discharged home while 17.00% were admitted to the hospital. Conclusion: Our experience suggests that triage centers for the assessment and management of Covid-19 suspected patients is an essential key strategy to prevent the spread of the disease among non-symptomatic patients who present to the EDs for care. This allows for a diseasecentered work-up and safer diversion of Covid-19 patients to specific hospital units.

Background Since the beginning of the pandemic, hospitals have been constantly overcrowded, with several observed waves of infected cases and hospitalisations. To avoid as much as possible this situation, efficient tools to facilitate the diagnosis of COVID-19 are needed. Objective To evaluate and compare prediction models to diagnose COVID-19 identified in a systematic review published recently using performance indicators such as discrimination and calibration measures. Methods A total of 1618 adult patients present at two Emergency Department triage centers and for whom qRT-PCR tests had been performed were included in this study. Six previously published models were reconstructed and assessed using diagnostic tests as sensitivity (Se) and negative predictive value (NPV), discrimination (Area Under the Roc Curve (AUROC)) and calibration measures. Agreement was also measured between them using Kappa’s coefficient and IntraClass Correlation Coefficient (ICC). A sensitivity analysis has been conducted by waves of patients. Results Among the 6 selected models, those based only on symptoms and/or risk exposure were found to be less efficient than those based on biological parameters and/or radiological examination with smallest AUROC values (< 0.80). However, all models showed good calibration and values above > 0.75 for Se and NPV but poor agreement (Kappa and ICC < 0.5) between them. The results of the first wave were similar to those of the second wave. Conclusion Although quite acceptable and similar results were found between all models, the importance of radiological examination was also emphasized, making it difficult to find an appropriate triage system to classify patients at risk for COVID-19.

Objectives:Due to the persistent primary care physicians shortage and the substantial increase in their workload, the organization of primary care calls during out-of-hours periods has become an everyday challenge. The SALOMON algorithm is an original nurse telephone triage tool allowing to dispatch patients to the best level of care according to their conditions. This study evaluated its reliability and criterion validity in rea-life settings. Methods:In this 5-year study, out-of-hours primary care calls were dispatched into four categories: Emergency Medical Services Intervention (EMSI), Emergency Department referred Consultation (EDRC), Primary Care Physician Home visit (PCPH), and Primary Care Physician Delayed visit (PCPD). We included data of patients' triage category, resources, and destination. Patients included into the primary care cohort were classified undertriaged if they had to be redirected to an emergency department (ED). Patients from the ED cohort were considered overtriaged if they did not require at least three diagnostic resources, one emergency-specific treatment or any hospitalization. In the ED cohort, only patients from the University Hospitals were considered. Results:10,207 calls were triaged using the SALOMON tool: 19.2% were classified as EMSI, 15.8% as EDRC, 62.8% as PCPH, and 2.2% as PCPD. The triage was appropriate for 85.5% of the calls with a 14.5% overtriage rate. In the PCPD/PCPH cohort, 96.9% of the calls were accurately triaged and 3.1% were undertriaged. SALOMON sensitivity and specificity reached 76.6% and 98.3%, respectively. Conclusion:SALOMON algorithm is a valid triage tool that has the potential to improve the organization of out-of-hours primary care work.

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