A Descriptive, Retrospective Analysis of Covid-19 Antibody Therapy and Its Effects on Morbidity and Mortality in Patients Receiving B-Lymphocyte Depleting Therapies

Intro: Patients receiving B-cell depleting or inhibiting therapies (BCDT), such as anti-CD20 monoclonal antibodies (CD20-MAB), are at risk for severe COVID-19. BCDT decreases production of neutralizing antibodies, causing delayed viral clearance and prolonged viral shedding. Passive antibody therapy (PAT), including COVID-19 convalescent plasma (CCP) and monoclonal antibodies (MAB), is hypothesized to be an effective Findings: * At the time of treatment, all patients (19/19) receiving CCP were hospitalized compared with10/53 patients treated with MAB. 2/10(20%) hospitalized patients treated with MAB died, compared with 3/19(15%) treated with CCP. **5/43 patients treated outpatient with MAB were hospitalized for COVID following CCP/MAB treatment with no COVID related deaths. Conclusion(s): Our data suggest that patients with COVID-19 who received BCDT within the last year may have improved outcomes after treatment with MAB or CCP. Elderly patients with >3 comorbidities and underlying hematological malignancy who contracted COVID-19 within 30 days of last BCDT had increased morbidity and mortality. To improve clinical outcomes, passive antibody therapy should be administered prior to the development of severe disease requiring hospitalization. Further prospective studies and comparisons to COVID-19 patients that did not receive MAB or CCP are needed to help confirm this association.Copyright © 2023

POSA310 Artificial Intelligence Applied on Administrative Big Data to Predict the Severity of SARS-COV-2 Infection

Objectives: To estimate the prognostic factors underlying severity of Sars-Cov-2 infection using a machine learning approach. Methods: The analysis is based on administrative databases of Italian Entities. Patients who were hospitalized with COVID-19 diagnosis (ICD-9 078.89) after 1st January 2020 were included into the dataset together with 13 relevant features representing age, sex and clinical history of each patient. Each record was labelled as 0 (hospitalized patients) or 1 (patients in intensive care or deceased). KerasTuner was used to define the architecture of the Neural Network achieving good accuracy score. To identify prognostic factors underlying severity of Sars Cov-2 infection, feature’s importance was evaluated starting from a Random Forest Classifier. Results: The preliminary dataset built contains 10.448 records from 9.346 hospitalized patients. The selected neural network is made of 13 input nodes, each one representing a feature, 1024 nodes in the hidden layer, processing information that comes from the input layer, and 2 nodes in the output layer, each one representing a label to define patient’s condition. The neural network obtained was able to achieve 64% of accuracy on the testing set. The condition of approximately 2 out of 3 patients was correctly predicted just by analysing their features. The feature’s importance computed from the Random Forest Classifier indicated that patient’s age is the primary prognostic factor underlying severity of Sars Cov-2 infection. The combination of the other features slightly improved model’s performance. Conclusions: The preliminary analysis shows that age is a prognostic factor of fundamental importance in defining the severity of Sars Cov-2 infection. The model obtained could be used to predict disease progression in patients most at risk by analysing their information in the databases. The model will be further improved through a process of feature selection to increase its accuracy and to allow the identification of other prognostic factors.

Risk factors for burnout and depression in healthcare workers: The national AMADEUS study protocol.

CONTEXT: Burnout is an international phenomenon defined as a state of professional exhaustion. It can lead to depression and have major economic and organizational impacts. Previous studies of healthcare professionals in France have focused on physicians, but none to date have explored other healthcare professions. OBJECTIVES: The main objective of our study is to determine the prevalence of burnout among healthcare workers. The secondary objectives are to explore the associations of burnout with professional and psycho-social factors and the risk of depression, professional harassment, sexual harassment, sexual-orientation based discrimination, consumption of antidepressants, anxiolytics and also the lifestyle of the individual: smoking, alcohol consumption, coffee consumption, physical activity and sleep quality. MATERIALS AND METHODS: The survey will take the form of a voluntary and anonymous online questionnaire carried out on the FramaForm1® platform and will be disseminated via social networks, professional networks and mailings. STUDY POPULATION: Senior doctors, interns, directors of care, nurses, head nurses and senior head nurses, physiotherapists and occupational therapists, dieticians, radiology technicians, laboratory technicians, psychologists, nurses' aides, auxiliary nurses and midwives will be included. COLLECTED DATA: Burnout will be measured with the Maslach Inventory burnout (MBI) questionnaire, work environment with the Karasek questionnaire and anxiety, depression risk with the Center for Epidemiologic Studies- Depression (CES-D), physical activity with the Global Physical Activity Questionnaire (GPAQ) and sleep quality with the Pittsburgh Sleep Quality Index (PSQI). ETHICS: This protocol has been accepted by the ethical committee (IRB n°C08/21.01.06.93911, CNIL). EXPECTED RESULTS: Based on international studies, we expect a high rate of burnout with disparities according to profession, socio-demographic data, seniority and type of service. We also expect a significant rate of untreated depression. This study will provide evidence for policy makers to implement collective strategies to reduce burnout and depression in the different populations studied.

Diabetes and dialysis:Proposal for a contagion prevention protocol in COVID-19 pandemia

BACKGROUND AND AIMS: After the official coronavirus (SARS-CoV-2) pandemic declaration by the WHO, Italy had the second-largest number of confirmed cases, after China. The Italian government introduced progressive infection-mitigation measurements, thus dramatically reducing social interactions and preventing virus spread. During the summer, infection containment measures progressively loosened until, due to an unjustified interpretation of some permissions and the excessive utilization of public transportation at school reopening, the contagion rate progressively increased until causing a severe challenge for our NHS again. Aim of the study: to assess the efficiency of our previously described protocol in 18 Campania region-located Nefrocenter Consortium DCs as further adapted to new knowledge under the new ubiquitous contagion conditions and to identify SARS-CoV-2-infection mortality rate and risk factors. METHOD: Dialysis patients did so too during that period according to the expected shifting prevalence over time (mean±SD: 853±30 per month;range 825 to 873)± (11.8% in April, and 14.8% in November vs. a pre-COVID-19 12.0% rate in January). RESULTS: More patients got infected in November (10.19%) than in April (0,24%), and 22 patients of the 89 from the SARS-CoV-2 November positive subjects required hospitalization for moderate-severe symptoms (24.72%), with death unavoidably coming in 19 (86.36% of hospitalized and 21.35% of infected patients) compared to the only one recorded in April (0.12%). The pandemic's two periods showed a strong association between mortality rate and often co-existing comorbidities, primarily represented by arterial hypertension, type 2 diabetes mellitus (T2DM), and chronic kidney disease (CKD). CONCLUSION: The prevously efficient contagion containment measures adopted by our DCs were not enough in November to fight the global infection risk pending on the whole Italian social community around. The Authors discuss possible reasons and put forward further suggestions for the best handling of any future infection waves.