This code is related to article "The antibody response to SARS-CoV-2 infection persists over at least 8 months in symptomatic patients" Abstract 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 th 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.

This code is related to article "The antibody response to SARS-CoV-2 infection persists over at least 8 months in symptomatic patients" Abstract 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 th 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.

*These authors contributed equally to the work ^#Corresponding author This record contains raw data related to article “A ‘Multiomic’ Approach of Saliva Metabolomics, Microbiota, and Serum Biomarkers to Assess the Need of Hospitalization in COVID-19" Abstract: The SARS-CoV-2 pandemic has overwhelmed the treatment capacity of the healthcare systems during the highest viral diffusion rate. Patients reaching the emergency department had to be either hospitalized or discharged. Still, the decision was taken based on the individual assessment of the actual clinical condition, without specific biomarkers to predict future improvement or deterioration. Often discharged patients returned to the hospital for aggravation of their condition. Here we have developed a new combined approach of omics to identify factors that could distinguish COVID-19 inpatients from outpatients. We tested the metabolome in the saliva and identified nine metabolites that separated the inpatient from the outpatient population, but not completely. When combined with serum biomarkers, just two salivary metabolites (myo-inositol and 2-pyrollidine acetic acid) and one serum protein, Chitinase 3-like-1(CHI3L1) were sufficient to separate the two groups completely. These metabolites positively or negatively correlated with four modulated microbiota taxa. This is a proof-of-concept that a combined omic analysis can be used to stratify patients.

*These authors contributed equally to the work ^#Corresponding author This record contains raw data related to article “A ‘multiomic’ approach to separate COVID-19 inpatients from outpatients." Abstract: The SARS-CoV-2 pandemic has overwhelmed the treatment capacity of the healthcare systems during the highest viral diffusion rate. Patients reaching the emergency department had to be either hospitalized or discharged. Still, the decision was taken based on the individual assessment of the actual clinical condition, without specific biomarkers to predict future improvement or deterioration. Often discharged patients returned to the hospital for aggravation of their condition. Here we have developed a new combined approach of omics to identify factors that could distinguish COVID-19 inpatients from outpatients. We tested the metabolome in the saliva and identified nine metabolites that separated the inpatient from the outpatient population, but not completely. When combined with serum biomarkers, just two salivary metabolites (myo-inositol and 2-pyrollidine acetic acid) and one serum protein, Chitinase 3-like-1(CHI3L1) were sufficient to separate the two groups completely. These metabolites positively or negatively correlated with four modulated microbiota taxa. This is a proof-of-concept that a combined omic analysis can be used to stratify patients.

*These authors contributed equally to the work ^#Corresponding author This record contains raw data related to article “A ‘Multiomic’ Approach of Saliva Metabolomics, Microbiota, and Serum Biomarkers to Assess the Need of Hospitalization in COVID-19" Abstract: The SARS-CoV-2 pandemic has overwhelmed the treatment capacity of the healthcare systems during the highest viral diffusion rate. Patients reaching the emergency department had to be either hospitalized or discharged. Still, the decision was taken based on the individual assessment of the actual clinical condition, without specific biomarkers to predict future improvement or deterioration. Often discharged patients returned to the hospital for aggravation of their condition. Here we have developed a new combined approach of omics to identify factors that could distinguish COVID-19 inpatients from outpatients. We tested the metabolome in the saliva and identified nine metabolites that separated the inpatient from the outpatient population, but not completely. When combined with serum biomarkers, just two salivary metabolites (myo-inositol and 2-pyrollidine acetic acid) and one serum protein, Chitinase 3-like-1(CHI3L1) were sufficient to separate the two groups completely. These metabolites positively or negatively correlated with four modulated microbiota taxa. This is a proof-of-concept that a combined omic analysis can be used to stratify patients.

This record contains raw data related to article “Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation" 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 hospitalised from January 25, 2020, to April 28, 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, 100 HU). We collected patient's clinical data including oxygenation support throughout hospitalisation. 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 < 0.001) and was a risk factor for in-hospital mortality (p < 0.001). 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. Key points: • Quantitative computer-aided analysis of chest CT (QCT) provides new metrics of COVID-19. • The compromised lung volume measured in the - 500, 100 HU interval predicts oxygenation support and intubation and is a risk factor for in-hospital death. • Compromised lung values in the 6-23% range prompt oxygenation therapy; values above 23% increase the need for intubation.

This record contains raw data related to article “Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation" 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 hospitalised from January 25, 2020, to April 28, 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, 100 HU). We collected patient's clinical data including oxygenation support throughout hospitalisation. 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 < 0.001) and was a risk factor for in-hospital mortality (p < 0.001). 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. Key points: • Quantitative computer-aided analysis of chest CT (QCT) provides new metrics of COVID-19. • The compromised lung volume measured in the - 500, 100 HU interval predicts oxygenation support and intubation and is a risk factor for in-hospital death. • Compromised lung values in the 6-23% range prompt oxygenation therapy; values above 23% increase the need for intubation.

This set of data is related to article "The antibody response to SARS-CoV-2 infection persists over at least 8 months in symptomatic patients" Abstract 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 th 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.

This set of data is related to article "The antibody response to SARS-CoV-2 infection persists over at least 8 months in symptomatic patients" Abstract 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 th 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.

This set of data is related to article "The antibody response to SARS-CoV-2 infection persists over at least 8 months in symptomatic patients" Abstract 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 th 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.