ABSTRACT
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.
ABSTRACT
BACKGROUND: The clinical presentation of European patients with mild-to-moderate COVID-19 infection is still unknown. OBJECTIVE: To study the clinical presentation of COVID-19 in Europe. METHODS: Patients with positive diagnosis of COVID-19 were recruited from 18 European hospitals. Epidemiological and clinical data were obtained through a standardized questionnaire. Bayesian analysis was used for analysing the relationship between outcomes. RESULTS: A total of 1,420 patients completed the study (962 females, 30.7% of healthcare workers). The mean age of patients was 39.17 ± 12.09 years. The most common symptoms were headache (70.3%), loss of smell (70.2%), nasal obstruction (67.8%), cough (63.2%), asthenia (63.3%), myalgia (62.5%), rhinorrhea (60.1%), gustatory dysfunction (54.2%) and sore throat (52.9%). Fever was reported by 45.4%. The mean duration of COVID-19 symptoms of mild-to-moderate cured patients was 11.5 ± 5.7 days. The prevalence of symptoms significantly varied according to age and sex. Young patients more frequently had ear, nose and throat complaints, whereas elderly individuals often presented fever, fatigue and loss of appetite. Loss of smell, headache, nasal obstruction and fatigue were more prevalent in female patients. The loss of smell was a key symptom of mild-to-moderate COVID-19 patients and was not associated with nasal obstruction and rhinorrhea. Loss of smell persisted at least 7 days after the disease in 37.5% of cured patients. CONCLUSION: The clinical presentation of mild-to-moderate COVID-19 substantially varies according to the age and the sex characteristics of patients. Olfactory dysfunction seems to be an important underestimated symptom of mild-to-moderate COVID-19 that needs to be recognized as such by the WHO.