Factors Associated With Enrollment into Inpatient Coronavirus Disease 2019 Randomized Controlled Trials: A Cross-sectional Analysis.

Background: Clinical trials for coronavirus disease 2019 (COVID-19) have struggled to achieve diverse patient enrollment, despite underrepresented groups bearing the largest burden of the disease and, presumably, being most in need of the treatments under investigation. Methods: To assess the willingness of patients to enroll into inpatient COVID-19 clinical trials when invited, we conducted a cross-sectional analysis of adults hospitalized with COVID-19 who were approached regarding enrollment. Associations between patient and temporal factors and enrollment were assessed by multivariable logistic regression analysis. Results: A total of 926 patients were included in this analysis. Overall, Hispanic/Latinx ethnicity was associated with a nearly half-fold decrease in the likelihood to enroll (adjusted odds ratio [aOR], 0.60 [95% confidence interval {CI}, .41-.88]). Greater baseline disease severity (aOR, 1.09 [95% CI, 1.02-1.17]), age 40-64 years (aOR, 1.83 [95% CI, 1.03-3.25]), and age ≥65 years (aOR, 1.92 [95% CI, 1.08-3.42]) were each independently associated with higher likelihood to enroll. Over the course of the pandemic, patients were less likely to enroll during the summer 2021 wave in COVID-19-related hospitalizations (aOR, 0.14 [95% CI, .10-.19]) compared with patients from the first wave in winter 2020. Conclusions: The decision to enroll into clinical trials is multifactorial. Amid a pandemic disproportionately affecting vulnerable groups, Hispanic/Latinx patients were less likely to participate when invited, whereas older adults were more likely. Future recruitment strategies must consider the nuanced perceptions and needs of diverse patient populations to ensure equitable trial participation that advances the quality of healthcare for all.

Clinical Outcomes Associated with SARS-CoV-2 Co-Infection with Rhinovirus and Adenovirus in Adults-A Retrospective Matched Cohort Study.

(1) Background: Respiratory co-infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other viruses are common, but data on clinical outcomes and laboratory biomarkers indicative of disease severity are limited. We aimed to compare clinical outcomes and laboratory biomarkers of patients with SARS-CoV-2 alone to those of patients with SARS-CoV-2 and either rhinovirus or adenovirus. (2) Methods: Hospitalized patients co-infected with SARS-CoV-2 and rhinovirus and patients co-infected with SARS-CoV-2 and adenovirus were matched to patients infected with SARS-CoV-2 alone. Outcomes of interest were the cumulative incidences of mechanical ventilation use, intensive care unit (ICU) admission, 30-day all-cause mortality, and 30-day all-cause readmission from the day of discharge. We also assessed differences in laboratory biomarkers from the day of specimen collection. (3) Results: Patients co-infected with SARS-CoV-2 and rhinovirus, compared with patients infected with SARS-CoV-2, had significantly greater 30-day all-cause mortality (8/23 (34.8%) vs. 8/69 (11.6%), p = 0.02). Additionally, median alanine transaminase (13 IU/L vs. 24 IU/L, p = 0.03), aspartate transaminase (25 IU/L vs. 36 IU/L, p = 0.04), and C-reactive protein (34.86 mg/L vs. 94.68 mg/L, p = 0.02) on day of specimen collection were significantly lower in patients co-infected with SARS-CoV-2 and rhinovirus in comparison to patients infected with SARS-CoV-2 alone. Clinical outcomes and laboratory markers did not differ significantly between patients with SARS-CoV-2 and adenovirus co-infection and patients with SARS-CoV-2 mono-infection. (4) Conclusion: SARS-CoV-2 and rhinovirus co-infection, compared with SARS-CoV-2 mono-infection alone, is positively associated with 30-day all-cause mortality among hospitalized patients. However, our lack of significant findings in our analysis of patients with SARS-CoV-2 and adenovirus co-infection may suggest that SARS-CoV-2 co-infections have variable significance, and further study is warranted.

Effect of Thymalfasin (Thymosin-α-1) on Reversing Lymphocytopenia among Patients with COVID-19

Introduction Thymosin-α-1 (Tα1) elevates lymphocyte counts among patients with COVID-19, but its effect on reversing lymphocytopenia is unknown. Methods 24 patients treated with Tα1 and 100 patients in the control arm were included in this analysis. The incidence rate of reversing lymphocytopenia, overall and stratified by baseline oxygen support, above the threshold for classification of lymphocytopenia (i.e., Total Lymphocyte Count (TLC) < 1.5 x 109/L) and severe lymphocytopenia (i.e., TLC < 1.0 x 109/L) within 3, 5, and 7 days of treatment initiation was calculated, along with incidence rate ratios (IRRs) and 95% confidence intervals (CIs). Results Compared with the standard of care, the rate of reversing lymphocytopenia (IRR: 2.38, 95% CI: 0.92 – 5.81) and severe lymphocytopenia (IRR: 1.57, 95% CI: 0.59 – 3.72), especially among patients with severe lymphocytopenia on high flow oxygen support (IRR: 3.64, 95% CI: 0.71 – 23.44), was greater for patients treated with Tα1 within 3 days of treatment initiation, although analyses were not significant. Conclusion Among patients with hypoxemia and lymphocytopenia, Tα1 may reverse lymphocytopenia and severe lymphocytopenia, particularly within 3 days of treatment initiation, faster than the standard of care.

A Pilot Trial of Thymalfasin (Tα1) to Treat Hospitalized Patients with Hypoxemia and Lymphocytopenia due to COVID-19 Infection.

BACKGROUND: Thymosin-α-1 (Tα1) may be a treatment option for COVID-19, but efficacy and safety data remain limited. METHODS: Prospective, open-label, randomized trial assessing preliminary efficacy and safety of thymalfasin (synthetic form of Tα1), compared with standard of care, among hospitalized patients with hypoxemia and lymphocytopenia due to COVID-19. RESULTS: 49 patients were included in this analysis. Compared with control patients, the incidence of clinical recovery was higher for treated patients with either baseline low flow oxygen (subdistribution hazard ratio [SHR]: 1.48; 95% CI: 0.68-3.25) or baseline high flow oxygen (SHR: 1.28; 95% CI: 0.35-4.63), although neither were significant. Among patients with baseline low flow oxygen, treated patients, compared with control patients, had an average difference of 3.84 times more CD4+ T cells on Day 5 than on Day 1 (p = 0.0113). Nine serious adverse events among treated patients were deemed not related to Tα1. CONCLUSION: Tα1 increases CD4+ T cell count among patients with baseline low flow oxygen support faster than standard of care and may have a role in the management of hospitalized patients with hypoxemia and lymphocytopenia due to COVID-19.

Real-Time Spatiotemporal Analysis of Microepidemics of Influenza and COVID-19 Based on Hospital Network Data: Colocalization of Neighborhood-Level Hotspots.

Objectives. To identify spatiotemporal patterns of epidemic spread at the community level.Methods. We extracted influenza cases reported between 2016 and 2019 and COVID-19 cases reported in March and April 2020 from a hospital network in Rhode Island. We performed a spatiotemporal hotspot analysis to simulate a real-time surveillance scenario.Results. We analyzed 6527 laboratory-confirmed influenza cases and identified microepidemics in more than 1100 neighborhoods, and more than half of the neighborhoods that had hotspots in a season became hotspots in the next season. We used data from 731 COVID-19 cases, and we found that a neighborhood was 1.90 times more likely to become a COVID-19 hotspot if it had been an influenza hotspot in 2018 to 2019.Conclusions. The use of readily available hospital data allows the real-time identification of spatiotemporal trends and hotspots of microepidemics.Public Health Implications. As local governments move to reopen the economy and ease physical distancing, the use of historic influenza hotspots could guide early prevention interventions, while the real-time identification of hotspots would enable the implementation of interventions that focus on small-area containment and mitigation.

Association of Obesity with Disease Severity Among Patients with Coronavirus Disease 2019.

OBJECTIVE: The aim of this study was to explore the potential association of obesity and other chronic diseases with severe outcomes, such as intensive care unit (ICU) admission and invasive mechanical ventilation (IMV), in patients hospitalized with coronavirus disease 2019 (COVID-19). METHODS: This study analyzed a retrospective cohort of 103 patients hospitalized with COVID-19. Demographic data, past medical history, and hospital course were collected and analyzed. A multivariate logistic regression analysis was implemented to examine associations. RESULTS: From February 17 to April 5, 103 consecutive patients were hospitalized with COVID-19. Among them, 44 patients (42.7%) were admitted to the ICU, and 29 (65.9%) required IMV. The prevalence of obesity was 47.5% (49 of 103). In a multivariate analysis, severe obesity (BMI ≥ 35 kg/m2 ) was associated with ICU admission (adjusted odds ratio [aOR]: 5.39, 95% CI: 1.13-25.64). Moreover, patients who required IMV were more likely to have had heart disease (aOR: 3.41, 95% CI: 1.05-11.06), obesity (BMI = 30-34.9 kg/m2 ; aOR: 6.85, 95% CI: 1.05-44.82), or severe obesity (BMI ≥ 35 kg/m2 ; aOR: 9.99, 95% CI: 1.39-71.69). CONCLUSIONS: In our analysis, severe obesity (BMI ≥ 35 kg/m2 ) was associated with ICU admission, whereas history of heart disease and obesity (BMI ≥ 30 kg/m2 ) were independently associated with the use of IMV. Increased vigilance and aggressive treatment of patients with obesity and COVID-19 are warranted.