ABSTRACT
Clinical deterioration of COVID-19 patients is still a challenging event to predict in the emergency department (ED). The present study developed an artificial neural network using textual and tabular data from ED electronic medical reports. Predicted outcomes were 30-day mortality and ICU admission. Consecutive patients between February 20 and May 5, 2020, from Humanitas Research Hospital and San Raffaele Hospital, in the Milan area, were included. COVID-19 patients were 1296. Textual predictors were patient history, physical exam, and radiological reports. Tabular predictors were age, creatinine, C-reactive protein, hemoglobin, and platelet count. Tabular-textual model performance indices were compared to a model implementing only tabular data. For 30-day mortality, the combined model yielded slightly better performances than the tabular model, with AUC 0.84 ± 0.02, F-1 score 0.56 ± 0.04 and an MCC 0.44 ± 0.04. Tabular model performance was: AUC 0.84 ± 0.02, F-1 score 0.55 ± 0.03 and MCC 0.43 ± 0.04. As for ICU admission, the combined model was not superior to the tabular one. The present data points to the effectiveness of a textual and tabular model for COVID-19 prognosis prediction. Also, it may support the ED physician in their decision-making process.
Subject(s)
COVID-19ABSTRACT
The factors involved in the persistence of antibodies to SARS-CoV-2 are unknown. We evaluated the antibody response to SARS-CoV-2 in personnel from 10 healthcare facilities and its association with individuals' characteristics and COVID-19 symptoms in an observational study. We enrolled 4735 subjects (corresponding to 80% of all personnel) over a period of 5 months when the spreading of the virus was drastically reduced. For each participant, we determined the rate of antibody increase or decrease over time in relation to 93 features analyzed in univariate and multivariate analyses through a machine learning approach. In individuals positive for IgG (>= 12 AU/mL) at the beginning of the study, we found an increase [p= 0.0002] in antibody response in symptomatic subjects, particularly with anosmia/dysgeusia (OR 2.75, 95% CI 1.753 - 4.301), in a multivariate logistic regression analysis. This may be linked to the persistence of SARS-CoV-2 in the olfactory bulb.
Subject(s)
COVID-19 , Muscle Hypertonia , DysgeusiaABSTRACT
OBJECTIVE: Lombardy (Italy) was the epicentre of the COVID-19 pandemic in March 2020. The healthcare system suffered from a shortage of ICU beds and oxygenation support devices. In our Institution, most patients received chest CT at admission, only interpreted visually. Given the proven value of Quantitative CT analysis (QCT) in the setting of ARDS, we tested QCT as an outcome predictor for COVID-19.METHODS: We performed a single centre retrospective study on COVID-19 patients hospitalized from January 25th, 2020 to April 28th 2020, who received CT at admission prompted by respiratory symptoms such as dyspnea or desaturation. QCT was performed using a semi-automated method (3D-Slicer). Lungs were divided by Hounsfield Unit intervals. Compromised lung (%CL) volume was the sum of poorly and non-aerated volumes (-500,100HU). We collected patient’s clinical data including oxygenation support throughout hospitalization.RESULTS: Two hundred twenty-two patients (163 males, median age 66, IQR 54-6) were included; 75% received oxygenation support (20% intubation rate). Compromised lung volume was the most accurate outcome predictor (logistic regression, p<0.001). %CL values in the 6-23% range increased risk of oxygenation support; values above 23% were at risk for intubation. %CL showed a negative correlation with PaO2/FiO2 ratio (p<.001) and was a risk factor for in-hospital mortality (p<.001)CONCLUSIONS: QCT provides new metrics of COVID-19. The compromised lung volume is accurate in predicting the need for oxygenation support and intubation and is a significant risk factor for in-hospital death. QCT may serve as a tool for the triaging process of COVID-19.