Risk of severe COVID-19 outcomes associated with rheumatoid arthritis and phenotypic subgroups: a retrospective, comparative, multicentre cohort study.

Background: Rheumatoid arthritis has been associated with severe COVID-19, but few studies have investigated how phenotypes of rheumatoid arthritis affect these associations. We aimed to investigate the associations between rheumatoid arthritis and phenotypes of interstitial lung disease, serostatus, and bone erosions with COVID-19 severity. Methods: We did a retrospective, comparative, multicentre cohort study at two large health-care systems (Mayo Clinic [19 hospitals and affiliated outpatient centres] and Mass General Brigham [14 hospitals and affiliated outpatient centres]) in the USA. Consecutive patients with rheumatoid arthritis meeting the 2010 American College of Rheumatology/European League Against Rheumatism classification criteria and who had COVID-19 between March 1, 2020, and June 6, 2021, were matched 1:5 on age, sex, and calendar date with patients without rheumatoid arthritis (comparators). Data were received from electronic health records from Mayo Clinic and Mass General Brigham. We examined subgroups of patients with rheumatoid arthritis by phenotypic features: rheumatoid arthritis-associated interstitial lung disease, seropositivity (for anti-cyclic citrullinated peptide, rheumatoid factor, or both), and bone erosions. Severe COVID-19 was a composite of hospitalisation or death. We used Cox regression to estimate hazard ratios (HR) for severe COVID-19, comparing rheumatoid arthritis and subgroups to the comparator group. Findings: We identified 582 patients with rheumatoid arthritis and 2875 matched comparators, all of whom had COVID-19 within the study dates. The mean age of those with rheumatoid arthritis was 62 [SD 14] years, 421 (72%) of 582 were women and 161 (28%) were men, 457 (79%) were White, 65 (11%) were Hispanic or Latino, and 41 (7%) were Black. Among patients with rheumatoid arthritis, 50 (9%) of 582 had interstitial lung disease, 388 (68%) of 568 were seropositive, and 159 (27%) of 582 had bone erosions. Severe COVID-19 occurred in 126 (22%) of 582 patients with rheumatoid arthritis versus 363 (13%) 2875 in the comparator group. Patients with rheumatoid arthritis had an HR of 1·75 (95% CI 1·45-2·10) for severe COVID-19 versus the comparator group. Patients with rheumatoid arthritis-associated interstitial lung disease had an HR of 2·50 (1·66-3·77) versus the comparator group for severe COVID-19. The risk for severe COVID-19 was also higher in patients with rheumatoid arthritis who were seropositive (HR 1·97 [95% CI 1·58-2·46]) or had erosive disease (1·93 [1·41-2·63]) than for those in the comparator group. Interpretation: Patients with rheumatoid arthritis have an increased risk of severe COVID-19 across phenotypic subgroups, especially among patients with interstitial lung disease. These findings suggest that rheumatoid arthritis with interstitial lung disease, or its treatment, might be a substantial contributor to severe COVID-19 outcomes for patients with rheumatoid arthritis. Funding: None.

Coronavirus Disease 2019 Vaccine-Breakthrough Infections Requiring Hospitalization in Mayo Clinic Florida Through August 2021.

We characterized coronavirus disease 2019 (COVID-19) breakthrough cases admitted to a single center in Florida. With the emergence of delta variant, an increased number of hospitalizations was seen due to breakthrough infections. These patients were older and more likely to have comorbidities. Preventive measures should be maintained even after vaccination.

Low antispike antibody levels correlate with poor outcomes in COVID-19 breakthrough hospitalizations.

BACKGROUND: While COVID-19 immunization programs attempted to reach targeted rates, cases rose significantly since the emergence of the delta variant. This retrospective cohort study describes the correlation between antispike antibodies and outcomes of hospitalized, breakthrough cases during the delta variant surge. METHODS: All patients with positive SARS-CoV-2 polymerase chain reaction hospitalized at Mayo Clinic Florida from 19 June 2021 to 11 November 2021 were considered for analysis. Cases were analyzed by vaccination status. Breakthrough cases were then analyzed by low and high antibody titers against SARS-CoV-2 spike protein, with a cut-off value of ≥132 U/ml. Outcomes included hospital length of stay (LOS), need for intensive care unit (ICU), mechanical ventilation, and mortality. We used 1:1 nearest neighbor propensity score matching without replacement to assess for confounders. RESULTS: Among 627 hospitalized patients with COVID-19, vaccine breakthrough cases were older with more comorbidities compared to unvaccinated. After propensity score matching, the unvaccinated patients had higher mortality (27 [28.4%] vs. 12 [12.6%], p = 0.002) and LOS (7 [1.0-57.0] vs. 5 [1.0-31.0] days, p = 0.011). In breakthrough cases, low-titer patients were more likely to be solid organ transplant recipients (16 [34.0%] vs. 9 [12.3%], p = 0.006), with higher need for ICU care (24 [51.1%] vs. 22 [11.0%], p = 0.034), longer hospital LOS (median 6 vs. 5 days, p = 0.013), and higher mortality (10 [21.3%] vs. 5 [6.8%], p = 0.025) than high-titer patients. CONCLUSIONS: Hospitalized breakthrough cases were more likely to have underlying risk factors than unvaccinated patients. Low-spike antibody titers may serve as an indicator for poor prognosis in breakthrough cases admitted to the hospital.

Quantifying the Importance of COVID-19 Vaccination to Our Future Outlook.

Predictive models have played a critical role in local, national, and international response to the COVID-19 pandemic. In the United States, health care systems and governmental agencies have relied on several models, such as the Institute for Health Metrics and Evaluation, Youyang Gu (YYG), Massachusetts Institute of Technology, and Centers for Disease Control and Prevention ensemble, to predict short- and long-term trends in disease activity. The Mayo Clinic Bayesian SIR model, recently made publicly available, has informed Mayo Clinic practice leadership at all sites across the United States and has been shared with Minnesota governmental leadership to help inform critical decisions during the past year. One key to the accuracy of the Mayo Clinic model is its ability to adapt to the constantly changing dynamics of the pandemic and uncertainties of human behavior, such as changes in the rate of contact among the population over time and by geographic location and now new virus variants. The Mayo Clinic model can also be used to forecast COVID-19 trends in different hypothetical worlds in which no vaccine is available, vaccinations are no longer being accepted from this point forward, and 75% of the population is already vaccinated. Surveys indicate that half of American adults are hesitant to receive a COVID-19 vaccine, and lack of understanding of the benefits of vaccination is an important barrier to use. The focus of this paper is to illustrate the stark contrast between these 3 scenarios and to demonstrate, mathematically, the benefit of high vaccine uptake on the future course of the pandemic.

Providing safe care for patients in the coronavirus disease 2019 (COVID-19) era: A case series evaluating risk for hospital-associated COVID-19.

OBJECTIVE: We evaluated the risk of patients contracting coronavirus disease 2019 (COVID-19) during their hospital stay to inform the safety of hospitalization for a non-COVID-19 indication during this pandemic. METHODS: A case series of adult patients hospitalized for 2 or more nights from May 15 to June 15, 2020 at large tertiary-care hospital in the midwestern United States was reviewed. All patients were screened at admission with the severe acute respiratory coronavirus virus 2 (SARS-CoV-2) polymerase chain reaction (PCR) test. Selected adult patients were also tested by IgG serology. After dismissal, patients with negative serology and PCR at admission were asked to undergo repeat serologic testing at 14-21 days after discharge. The primary outcome was healthcare-associated COVID-19 defined as a new positive SARS-CoV-2 PCR test on or after day 4 of hospital stay or within 7 days of hospital dismissal, or seroconversion in patients previously established as seronegative. RESULTS: Of the 2,068 eligible adult patients, 1,778 (86.0%) completed admission PCR testing, while 1,339 (64.7%) also completed admission serology testing. Of the 1,310 (97.8%) who were both PCR and seronegative, 445 (34.0%) repeated postdischarge serology testing. No healthcare-associated COVID-19 cases were detected during the study period. Of 1,310 eligible PCR and seronegative adults, no patients tested PCR positive during hospital admission (95% confidence interval [CI], 0.0%-0.3%). Of the 445 (34.0%) who completed postdischarge serology testing, no patients seroconverted (0.0%; 95% CI, 0.0%-0.9%). CONCLUSION: We found low likelihood of hospital-associated COVID-19 with strict adherence to universal masking, physical distancing, and hand hygiene along with limited visitors and screening of admissions with PCR.

A haemagglutination test for rapid detection of antibodies to SARS-CoV-2.

Serological detection of antibodies to SARS-CoV-2 is essential for establishing rates of seroconversion in populations, and for seeking evidence for a level of antibody that may be protective against COVID-19 disease. Several high-performance commercial tests have been described, but these require centralised laboratory facilities that are comparatively expensive, and therefore not available universally. Red cell agglutination tests do not require special equipment, are read by eye, have short development times, low cost and can be applied at the Point of Care. Here we describe a quantitative Haemagglutination test (HAT) for the detection of antibodies to the receptor binding domain of the SARS-CoV-2 spike protein. The HAT has a sensitivity of 90% and specificity of 99% for detection of antibodies after a PCR diagnosed infection. We will supply aliquots of the test reagent sufficient for ten thousand test wells free of charge to qualified research groups anywhere in the world.

Deployment of an Interdisciplinary Predictive Analytics Task Force to Inform Hospital Operational Decision-Making During the COVID-19 Pandemic.

In March 2020, our institution developed an interdisciplinary predictive analytics task force to provide coronavirus disease 2019 (COVID-19) hospital census forecasting to help clinical leaders understand the potential impacts on hospital operations. As the situation unfolded into a pandemic, our task force provided predictive insights through a structured set of visualizations and key messages that have helped the practice to anticipate and react to changing operational needs and opportunities. The framework shared here for the deployment of a COVID-19 predictive analytics task force could be adapted for effective implementation at other institutions to provide evidence-based messaging for operational decision-making. For hospitals without such a structure, immediate consideration may be warranted in light of the devastating COVID-19 third-wave which has arrived for winter 2020-2021.