An observational study on the safety of COVID-19 vaccination in patients with myasthenia gravis.

OBJECTIVE: There is concern that the coronavirus disease (COVID-19) vaccine may trigger or worsen autoimmune diseases. The objective of this study was to determine the impacts of COVID-19 vaccination on symptom severity in patients with myasthenia gravis (MG). METHODS: A total of 106 enrolled patients with MG who were vaccinated against COVID-19 were followed up, and a questionnaire was used to document in detail the exacerbation of muscle weakness after vaccination and all other uncomfortable reactions after vaccination. Demographic, clinical characteristics, medication, and vaccination data were collected by follow-up interview. The main observation outcome was whether the MG symptoms of patients were exacerbated. The definition of exacerbation is according to the subjective feeling of the patient or a 2-point increase in daily life myasthenia gravis activity score relative to before vaccination, within 30 days after vaccination. RESULTS: Of 106 enrolled patients [median age (SD) 41.0 years, 38 (35.8%) men, 53 (50.0%) with generalized MG, 74 (69.8%) positive for acetylcholine receptor antibody, and 21 (19.8%) with accompanying thymoma], muscle weakness symptoms were stable in 102 (96.2%) patients before vaccine inoculation. Muscle weakness worsened in 10 (9.4%) people after vaccination, of which 8 patients reported slight symptom worsening that resolved quickly (within a few days). Two (1.9%) of patients showed serious symptom aggravation that required hospitalization. CONCLUSION: Our results suggest that inactivated virus vaccines against COVID-19 may be safe for patients with MG whose condition is stable. Patients with generalized MG may be more likely to develop increased muscle weakness after vaccination.

A Quantitative Study to Examine the Comprehensiveness of Palliative Care Consultations During COVID-19 (Sch406)

Outcomes: 1. Evaluate the comprehensiveness of palliative care consultations in different clinical settings and time periods quantitatively by utilizing national consensus project guidelines. 2. Analyze patient sociodemographic and administrative data and palliative care consultation charts to infer variables that predict the comprehensiveness of consultations. Background(s): COVID-19 has caused unprecedented suffering, strained healthcare systems, and jeopardized the well-being of healthcare providers. Few studies exist to characterize how inpatient specialist palliative care (PC) teams managed an increase in demand for consultations while maintaining the quality of their service. Objective(s): This quantitative study aims to: (1) examine the comprehensiveness of inpatient PC consultations conducted from 2019 through 2022 at a tertiary academic hospital in Toronto, Canada, and (2) assess the predictors of comprehensiveness of PC consultations. Method(s): We are conducting a retrospective cohort study examining a 33% random sample of inpatient palliative care consultation notes (n=600) completed for adult (>18 years old) inpatients at Mount Sinai Hospital between January 1, 2019, and July 1, 2022. Our definition of comprehensiveness is informed by a recent publication addressing necessary clinical domains (eg, performing a spiritual history, code status discussion) in a palliative care assessment. We will code each inpatient palliative care consult note for the completion of individual clinical domains using template analysis. We will dual code a random selection of 10% of the transcripts to evaluate interrater reliability. Informed by Andersen's behavioral model for health services utilization, we will sociodemographic (age, gender, rurality, English language proficiency) and clinical pathophysiological variables (primary diagnosis, referral service, reason for referral, disposition, etc.) from the consult notes and linked administrative data. We will calculate frequencies and distributions of all study variables and use multiple linear regressions to examine predictors of comprehensiveness of PC consultations. Conclusion(s): This study will provide novel data on the clinical work completed by inpatient palliative care clinicians during the COVID-19 pandemic. The data will offer critical insights into how palliative care clinicians prioritize clinical domains during initial assessments of inpatients with varying sociodemographic and clinical backgrounds.Copyright © 2023

Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays

A critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause for the coronavirus disease 2019 (COVID-19) pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S, are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments. The COVID-Net S deep neural networks yielded R[Formula: see text] of [Formula: see text] and [Formula: see text] between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing COVID-Net S networks achieved R[Formula: see text] of 0.739 and 0.741 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.