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Objective: Approximately one third of individuals worldwide have not received a COVID-19 vaccine. Although studies have investigated risk factors linked to severe COVID-19 among unvaccinated people with rheumatic diseases (RDs), we know less about whether these factors changed as the pandemic progressed. We aimed to identify risk factors associated with severe COVID-19 in unvaccinated individuals in different pandemic epochs corresponding to major variants of concern. Methods: Patients with RDs and COVID-19 were entered into the COVID-19 Global Rheumatology Alliance Registry between March 2020 and June 2022. An ordinal logistic regression model (not hospitalized, hospitalized, and death) was used with date of COVID-19 diagnosis, age, sex, race and/or ethnicity, comorbidities, RD activity, medications, and the human development index (HDI) as covariates. The main analysis included all unvaccinated patients across COVID-19 pandemic epochs; subanalyses stratified patients according to RD types. Results: Among 19,256 unvaccinated people with RDs and COVID-19, those who were older, male, had more comorbidities, used glucocorticoids, had higher disease activity, or lived in lower HDI regions had worse outcomes across epochs. For those with rheumatoid arthritis, sulfasalazine and B-cell–depleting therapy were associated with worse outcomes, and tumor necrosis factor inhibitors were associated with improved outcomes. In those with connective tissue disease or vasculitis, B-cell–depleting therapy was associated with worse outcomes. Conclusion: Risk factors for severe COVID-19 outcomes were similar throughout pandemic epochs in unvaccinated people with RDs. Ongoing efforts, including vaccination, are needed to reduce COVID-19 severity in this population, particularly in those with medical and social vulnerabilities identified in this study.

Objectives To determine factors associated with COVID-19-related death in people with rheumatic diseases. Methods Physician-reported registry of adults with rheumatic disease and confirmed or presumptive COVID-19 (from 24 March to 1 July 2020). The primary outcome was COVID-19-related death. Age, sex, smoking status, comorbidities, rheumatic disease diagnosis, disease activity and medications were included as covariates in multivariable logistic regression models. Analyses were further stratified according to rheumatic disease category. Results Of 3729 patients (mean age 57 years, 68% female), 390 (10.5%) died. Independent factors associated with COVID-19-related death were age (66-75 years: OR 3.00, 95% CI 2.13 to 4.22; >75 years: 6.18, 4.47 to 8.53; both vs ≤65 years), male sex (1.46, 1.11 to 1.91), hypertension combined with cardiovascular disease (1.89, 1.31 to 2.73), chronic lung disease (1.68, 1.26 to 2.25) and prednisolone-equivalent dosage >10 mg/day (1.69, 1.18 to 2.41; vs no glucocorticoid intake). Moderate/high disease activity (vs remission/low disease activity) was associated with higher odds of death (1.87, 1.27 to 2.77). Rituximab (4.04, 2.32 to 7.03), sulfasalazine (3.60, 1.66 to 7.78), immunosuppressants (azathioprine, cyclophosphamide, ciclosporin, mycophenolate or tacrolimus: 2.22, 1.43 to 3.46) and not receiving any disease-modifying anti-rheumatic drug (DMARD) (2.11, 1.48 to 3.01) were associated with higher odds of death, compared with methotrexate monotherapy. Other synthetic/biological DMARDs were not associated with COVID-19-related death. Conclusion Among people with rheumatic disease, COVID-19-related death was associated with known general factors (older age, male sex and specific comorbidities) and disease-specific factors (disease activity and specific medications). The association with moderate/high disease activity highlights the importance of adequate disease control with DMARDs, preferably without increasing glucocorticoid dos

Background Characteristics and prognosis of patients admitted with strong suspicion of myocardial infarction (MI) but discharged without an MI diagnosis are not well-described. Objectives To compare background characteristics and cardiovascular outcomes in patients discharged with or without MI diagnosis. Methods The DETermination of the role of Oxygen in suspected Acute Myocardial Infarction (DETO2X-AMI) trial compared 6629 patients with strong suspicion of MI randomized to oxygen or ambient air. The main composite end-point of this subgroup analysis was the incidence of all-cause death, rehospitalization with MI, heart failure (HF) or stroke during a follow-up of 2.1 years (median; range: 1-3.7 years) irrespective of randomized treatment. Results 1619 (24%) received a non-MI discharge diagnosis, and 5010 patients (76%) were diagnosed with MI. Groups were similar in age, but non-MI patients were more commonly female and had more comorbidities. At thirty days, the incidence of the composite end-point was 2.8% (45 of 1619) in non-MI patients, compared to 5.0% (250 of 5010) in MI patients with lower incidences in all individual end-points. However, for the long-term follow-up, the incidence of the composite end-point increased in the non-MI patients to 17.7% (286 of 1619) as compared to 16.0% (804 of 5010) in MI patients, mainly driven by a higher incidence of all-cause death, stroke and HF. Conclusions Patients admitted with a strong suspicion of MI but discharged with another diagnosis had more favourable outcomes in the short-term perspective, but from one year onwards, cardiovascular outcomes and death deteriorated to a worse long-term prognosis., Funding Agencies|Swedish HeartLung FoundationSwedish Heart-Lung Foundation [HLF20160688, HLF20180187]; Stockholm County CouncilStockholm County Council [K 20174577]; Swedish Heart-Lung FoundationSwedish Heart-Lung Foundation [HLF20180287]

We analyze the statistical performance of identification of stochastic dynamical systems with non-linear measurement sensors. This includes stochastic Wiener systems, with linear dynamics, process noise and measured by a non-linear sensor with additive measurement noise. There are many possible system identification methods for such systems, including the maximum likelihood (ML) method and the prediction error method. The focus has mostly been on algorithms and implementation, and less is known about the statistical performance and the corresponding Cramér-Rao lower bound (CRLB) for identification of such non-linear systems. We derive expressions for the CRLB and the asymptotic normalized covariance matrix for certain Gaussian approximations of Wiener systems to show how a non-linear sensor affects the accuracy compared to a corresponding linear sensor. The key idea is to take second order statistics into account by using a common parametrization of the mean and the variance of the output process. This analysis also leads to an ML motivated identification method based on the conditional mean predictor and a Gaussian distribution approximation. The analysis is supported by numerical simulations., QC 20190415