People with HIV Have Higher Risk of COVID-19 Diagnosis but Similar Outcomes than the General Population

BackgroundWe investigated the effect of HIV on COVID-19 outcomes with attention to selection bias due to differential testing and to comorbidity burden. MethodsRetrospective cohort analysis using four hierarchical outcomes: positive SARS-CoV-2 test, COVID-19 hospitalization, intensive care unit (ICU) admission, and hospital mortality. The effect of HIV status was assessed using traditional covariate-adjusted, inverse probability weighted (IPW) analysis based on covariate distributions for testing bias (testing IPWs), HIV infection status (HIV IPWs), and combined models. Among PWH, we evaluated whether CD4 count and HIV plasma viral load (pVL) discriminated between those who did or did not develop study outcomes using receiver operating characteristic analysis. ResultsBetween March and November 2020, 63,319 people were receiving primary care services at UCSD, of whom 4,017 were people living with HIV (PWH). PWH had 2.1 times the odds of a positive SARS-CoV-2 test compared to those without HIV after weighting for potential testing bias, comorbidity burden, and HIV-IPW (95% CI 1.6-2.8). Relative to persons without HIV, PWH did not have an increased rate of COVID-19 hospitalization after controlling for comorbidities and testing bias [adjusted incidence rate ratio (aIRR): 0.5, 95% CI: 0.1 - 1.4]. PWH had neither a different rate of ICU admission (aIRR:1.08, 95% CI; 0.31 - 3.80) nor in-hospital death (aIRR:0.92, 95% CI; 0.08 - 10.94) in any examined model. Neither CD4 count nor pVL predicted any of the hierarchical outcomes among PWH. ConclusionsPWH have a higher risk of COVID-19 diagnosis but similar outcomes compared to those without HIV. Summary pointAfter considering the effects of potential bias due to differential testing, comorbidities, and other patient characteristics, people with HIV had an increased rate of SARS-CoV-2 positivity and similar rates of hospitalization, ICU admission, and death.

Development of a COVID-19 Application Ontology for the ACT Network

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that that are critical to COVID-19 research. The ontology contains over 50,000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for SARS-CoV-2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of nine academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

A CONTRAST OBSERVATION ON APPLYING TWO DIFFERENT KINDS OF RESIN SPLINT TO THE FIXATION OF LOOSED TEETH / 西安交通大学学报(医学版)

Two kinds of splint consisted of light-cured orselfcuring resin with nylon yarn were applied to fixthe loosed teeth caused by periodontitis. The re-sults showed that the light-cured resin with nylonyarn splint is much better than the self-cured resinwith nylon yarn splint (P