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.

Regulations of cosmetic ingredients and products have been the most advanced in embracing new approach methodologies (NAMs). Consequently, the cosmetic industry has assumed a forerunner role in the development and implementation of animal-free next-generation risk assessment (NGRA) that incorporates defined approaches (DAs) to assess the skin sensitization potency of ingredients. A Bayesian network DA predicting four potency categories (SkinSens-BN) was constructed against reference Local Lymph Node Assay data for a total of 297 substances, achieving a predictive performance similar to that of other DAs. With the aim of optimally informing risk assessment with a continuous point of departure (PoD), a weighted sum of the SkinSens-BN probabilities for four potency classes (non-, weak, moderate, and strong/extreme sensitizer) was calculated, using fixed weights based on associated LLNA EC3-values. The approach was promising, e.g., the derived PoDs for substances classified as non-sensitizers did not overlap with any others and 77% of PoDs were similar or more conservative than LLNA EC3. In addition, the predictions were assigned a level of confidence based on the probabilities to inform the evaluation of uncertainty in an NGRA context. In conclusion, the PoD derivation approach can substantially contribute to reliable skin sensitization NGRAs.

Olfactory dysfunction (OD) is a key symptom of coronavirus disease 2019 (COVID-19). Currently, a few data are available about the recovery of OD after the infection resolution. In this study, we investigated both prevalence and recovery rate of OD with subjective and objective clinical tools in 2,581 patients. First, our data showed that the prevalence of OD was significantly higher in mild form (85.9%) compared with moderate-to-critical forms (4.5-9.7%; p=0.001). Second, focusing on patients with OD who completed the 2-month follow-up period (N=1,363), we observed that 328 patients (24.1%) did not subjectively recover olfaction 60 days after the onset of the dysfunction. The mean duration of self-reported OD was 21.6±17.9 days. Third, the objective olfactory evaluations performed on a subset of patients (N=233) reported hyposmia or anosmia in 54.7% and 36.6% of mild and moderate-to-critical forms, respectively (p=0.001). At the end of follow-up, 15.3% of anosmic/hyposmic patients did not objectively recover olfaction. The higher baseline severity of objective olfactory evaluations was strongly predictive of persistent OD (p<0.001). OD disappeared in 75% to 85% of patients regarding self-reported or objective olfactory evaluations.