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Recent reports linked acute COVID-19 infection in hospitalized patients to cardiac abnormalities. Studies have not evaluated presence of abnormal cardiac structure and function before scanning in setting of COVD-19 infection. We sought to examine cardiac abnormalities in consecutive group of patients with acute COVID-19 infection according to the presence or absence of cardiac disease based on review of health records and cardiovascular imaging studies. We looked at independent contribution of imaging findings to clinical outcomes. After excluding patients with previous left ventricular (LV) systolic dysfunction (global and/or segmental), 724 patients were included. Machine learning identified predictors of in-hospital mortality and in-hospital mortality + ECMO. In patients without previous cardiovascular disease, LV EF

In the high-temperature reactor design, it is common practice to leave gaps between the graphite blocks of the reflectors to accommodate thermal dilatation and material swelling, as well as to prov...

Background: Covid-19 has caused significant global mortality. Multiple vaccines have demonstrated efficacy in clinical trials though real-world effectiveness of vaccines against Covid-19 mortality in clinically and demographically diverse populations has not yet been reported. Methods: We used a retrospective cohort assembled from a cross-institution comprehensive data repository. Established patients of the health care system were categorized as not immunized, partially immunized, or fully immunized against SARS-CoV-2 with an mRNA vaccine through April 4, 2021. Outcomes were Covid-19 related hospitalization and death. Results: Of the 91,134 established patients, 70.2% were not immunized, 4.5% were partially immunized and 25.4% were fully immunized. Among the fully immunized 0.7% had a Covid-19 hospitalization, whereas 3.4% among the partially immunized and 2.7% non-immunized individuals were hospitalized with Covid-19. Of the 225 deaths among Covid-19 hospitalizations, 219 (97.3%) were in the not immunized, 5 (2.2%) in the partially immunized, and 1 (0.0041%) in the fully immunized group. mRNA vaccines were 96% (95%CI: 95 - 99) effective at preventing Covid-19 related hospitalization and 98.7% (95%CI: 91.0 - 99.8) effective at preventing Covid-19 related death when participants were fully vaccinated. Partial vaccination was 77% (95%CI: 71 - 82) effective at preventing hospitalization and 64.2% (95%CI: 13.0 - 85.2) effective at preventing death. Vaccine effectiveness at preventing hospitalization was conserved across subgroups of age, race, ethnicity, Area Deprivation Index, and Charlson Comorbidity Index. Conclusions: In a large, diverse cohort in the United States, full immunization with mRNA vaccines was highly effective in the real-world scenario at preventing Covid-19 related hospitalization and death.

The reactor cavity cooling system (RCCS) is a common reactor safety system in high-temperature gas-cooled Reactors (HTGR) that removes heat from the reactor pressure vessel (RPV) by radiation ( ∼ 80 % ) and natural convection ( ∼ 20 % ). For the simulation of accident scenarios of HTGRs, intermediate fidelity and system codes models must be employed to limit the models’ execution time. While an accurate quantification of the radiative heat transfer is available in these models, the quantification of natural convection must rely on correlations of questionable accuracy for the Nusselt number. Commonly used correlations are based in experiments performed at low Rayleigh numbers and/or using isothermal walls in simplified geometries. This work improves on the accuracy of natural convection heat transfer correlations to support HTGR designs.These correlations include both local and average Nusselt numbers as a function of the global Rayleigh number, the local Rayleigh number, and the temperature profile at the hot wall of the RCCS. In the absence of dedicated experiments and the difficulty of performing high-fidelity simulations at realistic Rayleigh numbers, the data to fit the correlations are generated with computational fluid dynamics (CFD) using Reynolds Averaged Navier-Stokes (RANS) models. First, a careful selection of the RANS turbulence model is performed by comparing the results obtained with different RANS turbulence models against high-fidelity simulations of natural convection at Ra 1 × 10 11 in a rectangular cavity. Next, the selected model is used to perform simulations of an HTGR cavity at different high Rayleigh numbers ∈ [ 6.1 × 10 11 , 2.9 × 10 13 ] to encompass several HTGR designs, assuming an isothermal RPV wall. The results obtained are used to fit a correlation for the average and space-varying Nusselt number as a function of the global and local Rayleigh numbers via a sparsity-promoting, least-squares method. The selected RANS model is then used to perform simulations of a PBMR-400 (Pebble Bed Modular Reactor) HTGR cavity with the temperature profiles at the RPV wall obtained during a PLOFC (Pressurized loss of forced cooling) transient. We use the results obtained to fit a temperature-dependent correction to the space-varying Nusselt number with the sparsity-promoting, least-squares method. The results obtained in this work enable system-level codes, such as Pronghorn, to perform higher-fidelity simulations of the heat exchange process in the RCCS while still maintaining a low computational cost.

Background The COVID-19 pandemic has exacerbated the challenges of meaningful health care digitization. The need for rapid yet validated decision-making requires robust data infrastructure. Organizations with a focus on learning health care (LHC) systems tend to adapt better to rapidly evolving data needs. Few studies have demonstrated a successful implementation of data digitization principles in an LHC context across health care systems during the COVID-19 pandemic. Objective We share our experience and provide a framework for assembling and organizing multidisciplinary resources, structuring and regulating research needs, and developing a single source of truth (SSoT) for COVID-19 research by applying fundamental principles of health care digitization, in the context of LHC systems across a complex health care organization. Methods Houston Methodist (HM) comprises eight tertiary care hospitals and an expansive primary care network across Greater Houston, Texas. During the early phase of the pandemic, institutional leadership envisioned the need to streamline COVID-19 research and established the retrospective research task force (RRTF). We describe an account of the structure, functioning, and productivity of the RRTF. We further elucidate the technical and structural details of a comprehensive data repository—the HM COVID-19 Surveillance and Outcomes Registry (CURATOR). We particularly highlight how CURATOR conforms to standard health care digitization principles in the LHC context. Results The HM COVID-19 RRTF comprises expertise in epidemiology, health systems, clinical domains, data sciences, information technology, and research regulation. The RRTF initially convened in March 2020 to prioritize and streamline COVID-19 observational research; to date, it has reviewed over 60 protocols and made recommendations to the institutional review board (IRB). The RRTF also established the charter for CURATOR, which in itself was IRB-approved in April 2020. CURATOR is a relational structured query language database that is directly populated with data from electronic health records, via largely automated extract, transform, and load procedures. The CURATOR design enables longitudinal tracking of COVID-19 cases and controls before and after COVID-19 testing. CURATOR has been set up following the SSoT principle and is harmonized across other COVID-19 data sources. CURATOR eliminates data silos by leveraging unique and disparate big data sources for COVID-19 research and provides a platform to capitalize on institutional investment in cloud computing. It currently hosts deeply phenotyped sociodemographic, clinical, and outcomes data of approximately 200,000 individuals tested for COVID-19. It supports more than 30 IRB-approved protocols across several clinical domains and has generated numerous publications from its core and associated data sources. Conclusions A data-driven decision-making strategy is paramount to the success of health care organizations. Investment in cross-disciplinary expertise, health care technology, and leadership commitment are key ingredients to foster an LHC system. Such systems can mitigate the effects of ongoing and future health care catastrophes by providing timely and validated decision support.