Incidence and Risk Factors of Immediate Hypersensitivity Reactions and Immunisation Stress-related Responses with COVID-19 mRNA Vaccine (preprint)

BackgroundWith the implementation of mass vaccination campaigns against COVID-19, the safety of vaccine needs to be evaluated. ObjectiveWe aimed to assess the incidence and risk factors for immediate hypersensitivity reactions (IHSR) and immunisation stress-related responses (ISRR) with the Moderna COVID-19 vaccine. MethodsThis nested case-control study included recipients who received the Moderna vaccine at a mass vaccination centre, Japan. Recipients with IHSR and ISRR were designated as cases 1 and 2, respectively. Controls 1 and 2 were selected from recipients without IHSR or ISRR and matched (1:4) with cases 1 and cases 2, respectively. Conditional logistic regression analysis was used to identify risk factors associated with IHSR and ISRR. ResultsOf the 614,151 vaccine recipients who received 1,201,688 vaccine doses, 306 recipients (cases 1) and 2,478 recipients (cases 2) showed 318 events of IHSR and 2,558 events of ISRR, respectively. The incidence rates per million doses were estimated as - IHSR: 266 cases, ISRR: 2,129 cases, anaphylaxis: 2 cases, and vasovagal syncope: 72 cases. Risk factors associated with IHSR included female, asthma, atopic dermatitis, thyroid diseases, and history of allergy; for ISRR, they were younger age, female, asthma, thyroid diseases, mental disorders, and a history of allergy and vasovagal reflex. ConclusionIn the mass vaccination settings, the Moderna vaccine can be used safely owing to the low incidence rates of IHSR and anaphylaxis. However, providers should beware of the occurrence of ISRR. Risk factor identification may contribute to the stratification of high-risk recipients for IHSR and ISRR.

Federated Learning used for predicting outcomes in SARS-COV-2 patients (preprint)

‘Federated Learning’ (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the “EXAM” (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.

Non-severe vs severe symptomatic COVID-19: 104 cases from the outbreak on the cruise ship "Diamond Princess" in Japan (preprint)

Background: The ongoing outbreak of the coronavirus disease 2019 (COVID-19) is a global threat. Identification of markers for symptom onset and disease progression is a pressing issue. We compared the clinical features on admission among patients who were diagnosed with asymptomatic, mild, and severe COVID-19 at the end of observation. Methods: This retrospective, single-center study included 104 patients with laboratory-confirmed COVID-19 from the mass infection on the Diamond Princess cruise ship from February 11 to February 25, 2020. Clinical records, laboratory data, and radiological findings were analyzed. Clinical outcomes were followed up until February 26, 2020. Clinical features on admission were compared among those with different disease severity at the end of observation. Univariate analysis identified factors associated with symptom onset and disease progression. Findings: The median age was 68 years, and 54 patients were male. Briefly, 43, 41, and 20 patients on admission and 33, 43, and 28 patients at the end of observation had asymptomatic, mild, and severe COVID-19, respectively. Serum lactate hydrogenase levels were significantly higher in 10 patients who were asymptomatic on admission but developed symptomatic COVID-19 compared with 33 patients who remained asymptomatic throughout the observation period. Older age, consolidation on chest computed tomography, and lymphopenia on admission were more frequent in patients with severe COVID-19 than those with mild COVID-19 at the end of observation. Interpretation: Lactate dehydrogenase level is a potential predictor of symptom onset in COVID-19. Older age, consolidation on chest CT images, and lymphopenia might be risk factors for disease progression of COVID-19 and contribute to the clinical management. Funding: Not applicable.