Clinical Trial Participation and COVID-19: a Descriptive Analysis from the American Heart Association's Get With The Guidelines Registry.

As COVID-19 cases begin to decrease in the USA, learning from the pandemic experience will provide insights regarding disparities of care delivery. We sought to determine if specific populations hospitalized with COVID-19 are equally likely to be enrolled in clinical trials. We examined patients hospitalized with COVID-19 at centers participating in the American Heart Association's COVID-19 CVD Registry. The primary outcome was odds of enrollment in a clinical trial, according to sex, race, and ethnicity. Among 14,397 adults hospitalized with COVID-19, 9.5% (n = 1,377) were enrolled in a clinical trial. The proportion of enrolled patients was the lowest for Black patients (8%); in multivariable analysis, female and Black patients were less likely to be enrolled in a clinical trial related to COVID-19 compared to men and other racial groups, respectively. Determination of specific reasons for the disparities in trial participation related to COVID-19 in these populations should be further investigated.

Global landscape of SARS-CoV-2 genomic surveillance and data sharing.

Genomic surveillance has shaped our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. We performed a global landscape analysis on SARS-CoV-2 genomic surveillance and genomic data using a collection of country-specific data. Here, we characterize increasing circulation of the Alpha variant in early 2021, subsequently replaced by the Delta variant around May 2021. SARS-CoV-2 genomic surveillance and sequencing availability varied markedly across countries, with 45 countries performing a high level of routine genomic surveillance and 96 countries with a high availability of SARS-CoV-2 sequencing. We also observed a marked heterogeneity of sequencing percentage, sequencing technologies, turnaround time and completeness of released metadata across regions and income groups. A total of 37% of countries with explicit reporting on variants shared less than half of their sequences of variants of concern (VOCs) in public repositories. Our findings indicate an urgent need to increase timely and full sharing of sequences, the standardization of metadata files and support for countries with limited sequencing and bioinformatics capacity.

Neutralizing Antibodies Against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variants Induced by Natural Infection or Vaccination: A Systematic Review and Pooled Analysis.

Recently emerged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants may pose a threat to immunity. A systematic landscape of neutralizing antibodies against emerging variants is needed. We systematically searched for studies that evaluated neutralizing antibody titers induced by previous infection or vaccination against SARS-CoV-2 variants and collected individual data. We identified 106 studies meeting the eligibility criteria. Lineage B.1.351 (beta), P.1 (gamma) and B.1.617.2 (delta) significantly escaped natural infection-mediated neutralization, with an average of 4.1-fold (95% confidence interval [CI]: 3.6-4.7-fold), 1.8-fold (1.4-2.4-fold), and 3.2-fold (2.4-4.1-fold) reduction in live virus neutralization assay, while neutralizing titers against B.1.1.7 (alpha) decreased slightly (1.4-fold [95% CI: 1.2-1.6-fold]). Serum from vaccinees also led to significant reductions in neutralization of B.1.351 across different platforms, with an average of 7.1-fold (95% CI: 5.5-9.0-fold) for nonreplicating vector platform, 4.1-fold (3.7-4.4-fold) for messenger RNA platform, and 2.5-fold (1.7-2.9-fold) for protein subunit platform. Neutralizing antibody levels induced by messenger RNA vaccines against SARS-CoV-2 variants were similar to, or higher, than that derived from naturally infected individuals.

Serological evidence of human infection with SARS-CoV-2: a systematic review and meta-analysis.

BACKGROUND: A rapidly increasing number of serological surveys for antibodies to SARS-CoV-2 have been reported worldwide. We aimed to synthesise, combine, and assess this large corpus of data. METHODS: In this systematic review and meta-analysis, we searched PubMed, Embase, Web of Science, and five preprint servers for articles published in English between Dec 1, 2019, and Dec 22, 2020. Studies evaluating SARS-CoV-2 seroprevalence in humans after the first identified case in the area were included. Studies that only reported serological responses among patients with COVID-19, those using known infection status samples, or any animal experiments were all excluded. All data used for analysis were extracted from included papers. Study quality was assessed using a standardised scale. We estimated age-specific, sex-specific, and race-specific seroprevalence by WHO regions and subpopulations with different levels of exposures, and the ratio of serology-identified infections to virologically confirmed cases. This study is registered with PROSPERO, CRD42020198253. FINDINGS: 16 506 studies were identified in the initial search, 2523 were assessed for eligibility after removal of duplicates and inappropriate titles and abstracts, and 404 serological studies (representing tests in 5 168 360 individuals) were included in the meta-analysis. In the 82 studies of higher quality, close contacts (18·0%, 95% CI 15·7-20·3) and high-risk health-care workers (17·1%, 9·9-24·4) had higher seroprevalence than did low-risk health-care workers (4·2%, 1·5-6·9) and the general population (8·0%, 6·8-9·2). The heterogeneity between included studies was high, with an overall I2 of 99·9% (p<0·0001). Seroprevalence varied greatly across WHO regions, with the lowest seroprevalence of general populations in the Western Pacific region (1·7%, 95% CI 0·0-5·0). The pooled infection-to-case ratio was similar between the region of the Americas (6·9, 95% CI 2·7-17·3) and the European region (8·4, 6·5-10·7), but higher in India (56·5, 28·5-112·0), the only country in the South-East Asia region with data. INTERPRETATION: Antibody-mediated herd immunity is far from being reached in most settings. Estimates of the ratio of serologically detected infections per virologically confirmed cases across WHO regions can help provide insights into the true proportion of the population infected from routine confirmation data. FUNDING: National Science Fund for Distinguished Young Scholars, Key Emergency Project of Shanghai Science and Technology Committee, Program of Shanghai Academic/Technology Research Leader, National Science and Technology Major project of China, the US National Institutes of Health. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Combining antibody markers for serosurveillance of SARS-CoV-2 to estimate seroprevalence and time-since-infection.

Serosurveillance is an important epidemiologic tool for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), used to estimate infection rates and the degree of population immunity. There is no general agreement on which antibody biomarker(s) should be used, especially with the rollout of vaccines globally. Here, we used random forest models to demonstrate that a single spike or receptor-binding domain (RBD) antibody was adequate for classifying prior infection, while a combination of two antibody biomarkers performed better than any single marker for estimating time-since-infection. Nucleocapsid antibodies performed worse than spike or RBD antibodies for classification, but can be useful for estimating time-since-infection, and in distinguishing infection-induced from vaccine-induced responses. Our analysis has the potential to inform the design of serosurveys for SARS-CoV-2, including decisions regarding a number of antibody biomarkers measured.

The Effects of Using a Clinical Prediction Rule to Prioritize Diagnostic Testing on Transmission and Hospital Burden: A Modeling Example of Early Severe Acute Respiratory Syndrome Coronavirus 2.

BACKGROUND: Prompt identification of infections is critical for slowing the spread of infectious diseases. However, diagnostic testing shortages are common in emerging diseases, low resource settings, and during outbreaks. This forces difficult decisions regarding who receives a test, often without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions. METHODS: Using early severe acute respiratory syndrome coronavirus disease 2 (SARS-CoV-2) as an example, we used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive. To consider the implications of gains in daily case detection at the population level, we incorporated testing using the CPR into a compartmentalized model of SARS-CoV-2. RESULTS: We found that applying this CPR (area under the curve, 0.69; 95% confidence interval, .68-.70) to prioritize testing increased the proportion of those testing positive in settings of limited testing capacity. We found that prioritized testing led to a delayed and lowered infection peak (ie, "flattens the curve"), with the greatest impact at lower values of the effective reproductive number (such as with concurrent community mitigation efforts), and when higher proportions of infectious persons seek testing. In addition, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit burden. CONCLUSION: We highlight the population-level benefits of evidence-based allocation of limited diagnostic capacity.SummaryWhen the demand for diagnostic tests exceeds capacity, the use of a clinical prediction rule to prioritize diagnostic testing can have meaningful impact on population-level outcomes, including delaying and lowering the infection peak, and reducing healthcare burden.

Modeling reductions in SARS-CoV-2 transmission and hospital burden achieved by prioritizing testing using a clinical prediction rule

Prompt identification of cases is critical for slowing the spread of COVID-19. However, many areas have faced diagnostic testing shortages, requiring difficult decisions to be made regarding who receives a test, without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions. We used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive, and found that its application to prioritize testing increases the proportion of those testing positive in settings of limited testing capacity. To consider the implications of these gains in daily case detection on the population level, we incorporated testing using the CPR into a compartmentalized disease transmission model. We found that prioritized testing led to a delayed and lowered infection peak (i.e. 'flattens the curve'), with the greatest impact at lower values of the effective reproductive number (such as with concurrent social distancing measures), and when higher proportions of infectious persons seek testing. Additionally, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit (ICU) burden. In conclusion, we present a novel approach to evidence-based allocation of limited diagnostic capacity, to achieve public health goals for COVID-19.

SARS-CoV-2 Innate Effector Associations and Viral Load in Early Nasopharyngeal Infection.

To examine innate immune responses in early SARS-CoV-2 infection that may change clinical outcomes, we compared nasopharyngeal swab data from 20 virus-positive and 20 virus-negative individuals. Multiple innate immune-related and ACE-2 transcripts increased with infection and were strongly associated with increasing viral load. We found widespread discrepancies between transcription and translation. Interferon proteins were unchanged or decreased in infected samples suggesting virally-induced shut-off of host anti-viral protein responses. However, IP-10 and several interferon-stimulated gene proteins increased with viral load. Older age was associated with modifications of some effects. Our findings may characterize the disrupted immune landscape of early disease.

SARS-CoV-2 innate effector associations and viral load in early nasopharyngeal infection.

COVID-19 causes severe disease with poor outcomes. We tested the hypothesis that early SARS-CoV-2 viral infection disrupts innate immune responses. These changes may be important for understanding subsequent clinical outcomes. We obtained residual nasopharyngeal swab samples from individuals who requested COVID-19 testing for symptoms at drive-through COVID-19 clinical testing sites operated by the University of Utah. We applied multiplex immunoassays, real-time polymerase chain reaction assays and quantitative proteomics to 20 virus-positive and 20 virus-negative samples. ACE-2 transcripts increased with infection (OR =17.4, 95% CI [CI] =4.78-63.8) and increasing viral N1 protein transcript load (OR =1.16, CI =1.10-1.23). Transcripts for two interferons (IFN) were elevated, IFN-λ1 (OR =71, CI =7.07-713) and IFN-λ2 (OR =40.2, CI =3.86-419), and closely associated with viral N1 transcripts (OR =1.35, CI =1.23-1.49 and OR =1.33 CI =1.20-1.47, respectively). Only transcripts for IP-10 were increased among systemic inflammatory cytokines that we examined (OR =131, CI =1.01-2620). We found widespread discrepancies between transcription and translation. IFN proteins were unchanged or decreased in infected samples (IFN-γ OR =0.90 CI =0.33-0.79, IFN-λ2,3 OR =0.60 CI =0.48-0.74) suggesting viral-induced shut-off of host antiviral protein responses. However, proteins for IP-10 (OR =3.74 CI =2.07-6.77) and several interferon-stimulated genes (ISG) increased with viral load (BST-1 OR =25.1, CI =3.33-188; IFIT1 OR =19.5, CI =4.25-89.2; IFIT3 OR =245, CI =15-4020; MX-1 OR =3.33, CI =1.44-7.70). Older age was associated with substantial modifications of some effects. Ambulatory symptomatic patients had an innate immune response with SARS-CoV-2 infection characterized by elevated IFN, proinflammatory cytokine and ISG transcripts, but there is evidence of a viral-induced host shut-off of antiviral responses. Our findings may characterize the disrupted immune landscape common in patients with early disease.

Robust Testing in Outpatient Settings to Explore COVID-19 Epidemiology: Disparities in Race/Ethnicity and Age, Salt Lake County, Utah, 2020.

OBJECTIVE: US-based descriptions of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have focused on patients with severe disease. Our objective was to describe characteristics of a predominantly outpatient population tested for SARS-CoV-2 in an area receiving comprehensive testing. METHODS: We extracted data on demographic characteristics and clinical data for all patients (91% outpatient) tested for SARS-CoV-2 at University of Utah Health clinics in Salt Lake County, Utah, from March 10 through April 24, 2020. We manually extracted data on symptoms and exposures from a subset of patients, and we calculated the adjusted odds of receiving a positive test result by demographic characteristics and clinical risk factors. RESULTS: Of 17 662 people tested, 1006 (5.7%) received a positive test result for SARS-CoV-2. Hispanic/Latinx people were twice as likely as non-Hispanic White people to receive a positive test result (adjusted odds ratio [aOR] = 2.0; 95% CI, 1.3-3.1), although the severity at presentation did not explain this discrepancy. Young people aged 0-19 years had the lowest rates of receiving a positive test result for SARS-CoV-2 (<4 cases per 10 000 population), and adults aged 70-79 and 40-49 had the highest rates of hospitalization per 100 000 population among people who received a positive test result (16 and 11, respectively). CONCLUSIONS: We found disparities by race/ethnicity and age in access to testing and in receiving a positive test result among outpatients tested for SARS-CoV-2. Further research and public health outreach on addressing racial/ethnic and age disparities will be needed to effectively combat the coronavirus disease 2019 pandemic in the United States.

Comparison of International Classification of Diseases and Related Health Problems, Tenth Revision Codes With Electronic Medical Records Among Patients With Symptoms of Coronavirus Disease 2019.

Importance: International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes are used to characterize coronavirus disease 2019 (COVID-19)-related symptoms. Their accuracy is unknown, which could affect downstream analyses. Objective: To compare the performance of fever-, cough-, and dyspnea-specific ICD-10 codes with medical record review among patients tested for COVID-19. Design, Setting, and Participants: This cohort study included patients who underwent quantitative reverse transcriptase-polymerase chain reaction testing for severe acute respiratory syndrome coronavirus 2 at University of Utah Health from March 10 to April 6, 2020. Data analysis was performed in April 2020. Main Outcomes and Measures: The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of ICD-10 codes for fever (R50*), cough (R05*), and dyspnea (R06.0*) were compared with manual medical record review. Performance was calculated overall and stratified by COVID-19 test result, sex, age group (<50, 50-64, and >64 years), and inpatient status. Bootstrapping was used to generate 95% CIs, and Pearson χ2 tests were used to compare different subgroups. Results: Among 2201 patients tested for COVD-19, the mean (SD) age was 42 (17) years; 1201 (55%) were female, 1569 (71%) were White, and 282 (13%) were Hispanic or Latino. The prevalence of fever was 66% (1444 patients), that of cough was 88% (1930 patients), and that of dyspnea was 64% (1399 patients). For fever, the sensitivity of ICD-10 codes was 0.26 (95% CI, 0.24-0.29), specificity was 0.98 (95% CI, 0.96-0.99), PPV was 0.96 (95% CI, 0.93-0.97), and NPV was 0.41 (95% CI, 0.39-0.43). For cough, the sensitivity of ICD-10 codes was 0.44 (95% CI, 0.42-0.46), specificity was 0.88 (95% CI, 0.84-0.92), PPV was 0.96 (95% CI, 0.95-0.97), and NPV was 0.18 (95% CI, 0.16-0.20). For dyspnea, the sensitivity of ICD-10 codes was 0.24 (95% CI, 0.22-0.26), specificity was 0.97 (95% CI, 0.96-0.98), PPV was 0.93 (95% CI, 0.90-0.96), and NPV was 0.42 (95% CI, 0.40-0.44). ICD-10 code performance was better for inpatients than for outpatients for fever (χ2 = 41.30; P < .001) and dyspnea (χ2 = 14.25; P = .003) but not for cough (χ2 = 5.13; P = .16). Conclusions and Relevance: These findings suggest that ICD-10 codes lack sensitivity and have poor NPV for symptoms associated with COVID-19. This inaccuracy has implications for any downstream data model, scientific discovery, or surveillance that relies on these codes.

Modeling reductions in SARS-CoV-2 transmission and hospital burden achieved by prioritizing testing using a clinical prediction rule.

Prompt identification of cases is critical for slowing the spread of COVID-19. However, many areas have faced diagnostic testing shortages, requiring difficult decisions to be made regarding who receives a test, without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions. We used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive, and found that its application to prioritize testing increases the proportion of those testing positive in settings of limited testing capacity. To consider the implications of these gains in daily case detection on the population level, we incorporated testing using the CPR into a compartmentalized disease transmission model. We found that prioritized testing led to a delayed and lowered infection peak (i.e. 'flattens the curve'), with the greatest impact at lower values of the effective reproductive number (such as with concurrent social distancing measures), and when higher proportions of infectious persons seek testing. Additionally, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit (ICU) burden. In conclusion, we present a novel approach to evidence-based allocation of limited diagnostic capacity, to achieve public health goals for COVID-19.