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.

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.

Clinical Outcomes of Adult Patients Hospitalized with COVID-19 after Vaccination.

Vaccination remains the most effective way to prevent COVID-19. The aim of the present study was to assess the incidence of COVID-19 hospitalizations after vaccination, as well as the effect of prior vaccination on hospitalization outcomes among patients with COVID-19. We analyzed and compared all consecutive patients, with or without prior vaccination, who were admitted to our hospital network due to COVID-19 from January to April 2021. Our primary outcome was to identify and describe cases of COVID-19 hospitalized after vaccination. We also utilized a multivariate logistic regression model to investigate the association of previous vaccination with hospitalization outcomes. We identified 915 consecutive patients hospitalized due to COVID-19 with 91/915 (10%) previously vaccinated with at least one dose of a COVID-19 vaccine. Utilizing our multivariate logistic regression model, we found that prior vaccination, regardless of the number of doses or days since vaccination, was associated with decreased mortality (aOR 0.44, 95% CI: 0.20-0.98) when compared to unvaccinated individuals. Our study showed that COVID-19 related hospitalization after vaccination may occur to a small percentage of patients, mainly those who are partially vaccinated. However, our findings underline that prior vaccination, even when partial, is associated with a decreased risk of death. Ongoing vaccination efforts should remain an absolute priority.

Clinical Presentation, Course, and Risk Factors Associated with Mortality in a Severe Outbreak of COVID-19 in Rhode Island, USA, April-June 2020.

Long-term care facilities (LTCFs) have had a disproportionally high mortality rate due to COVID-19. We describe a rapidly escalating COVID-19 outbreak among 116 LTCF residents in Rhode Island, USA. Overall, 111 (95.6%) residents tested positive and, of these, 48 (43.2%) died. The most common comorbidities were hypertension (84.7%) and cardiovascular disease (84.7%). A small percentage (9%) of residents were asymptomatic, while 33.3% of residents were pre-symptomatic, with progression to symptoms within a median of three days following the positive test. While typical symptoms of fever (80.2%) and cough (43.2%) were prevalent, shortness of breath (14.4%) was rarely found despite common hypoxemia (95.5%). The majority of patients demonstrated atypical symptoms with the most common being loss of appetite (61.3%), lethargy (42.3%), diarrhea (37.8%), and fatigue (32.4%). Many residents had increased agitation (38.7%) and anxiety (5.4%), potentially due to the restriction measures or the underlying mental illness. The fever curve was characterized by an intermittent low-grade fever, often the first presenting symptom. Mortality was associated with a disease course beginning with a loss of appetite and lethargy, as well as one more often involving fever greater than 38 °C, loss of appetite, altered mental status, diarrhea, and respiratory distress. Interestingly, no differences in age or comorbidities were noted between survivors and non-survivors. Taking demographic factors into account, treatment with anticoagulation was still associated with reduced mortality (adjusted OR 0.16; 95% C.I. 0.06-0.39; p < 0.001). Overall, the clinical features of the disease in this population can be subtle and the symptoms are commonly atypical. However, clinical decline among those who did not survive was often rapid with patients expiring within 10 days from disease detection. Further studies are needed to better explain the variability in clinical course of COVID-19 among LTCF residents, specifically the factors affecting mortality, the differences observed in symptom presentation, and rate of clinical decline.

Clinical Presentation, Course, and Risk Factors Associated with Mortality in a Severe Outbreak of COVID-19 in Rhode Island, USA, April–June 2020

Long-term care facilities (LTCFs) have had a disproportionally high mortality rate due to COVID-19. We describe a rapidly escalating COVID-19 outbreak among 116 LTCF residents in Rhode Island, USA. Overall, 111 (95.6%) residents tested positive and, of these, 48 (43.2%) died. The most common comorbidities were hypertension (84.7%) and cardiovascular disease (84.7%). A small percentage (9%) of residents were asymptomatic, while 33.3% of residents were pre-symptomatic, with progression to symptoms within a median of three days following the positive test. While typical symptoms of fever (80.2%) and cough (43.2%) were prevalent, shortness of breath (14.4%) was rarely found despite common hypoxemia (95.5%). The majority of patients demonstrated atypical symptoms with the most common being loss of appetite (61.3%), lethargy (42.3%), diarrhea (37.8%), and fatigue (32.4%). Many residents had increased agitation (38.7%) and anxiety (5.4%), potentially due to the restriction measures or the underlying mental illness. The fever curve was characterized by an intermittent low-grade fever, often the first presenting symptom. Mortality was associated with a disease course beginning with a loss of appetite and lethargy, as well as one more often involving fever greater than 38 °C, loss of appetite, altered mental status, diarrhea, and respiratory distress. Interestingly, no differences in age or comorbidities were noted between survivors and non-survivors. Taking demographic factors into account, treatment with anticoagulation was still associated with reduced mortality (adjusted OR 0.16;95% C.I. 0.06–0.39;p <0.001). Overall, the clinical features of the disease in this population can be subtle and the symptoms are commonly atypical. However, clinical decline among those who did not survive was often rapid with patients expiring within 10 days from disease detection. Further studies are needed to better explain the variability in clinical course of COVID-19 among LTCF residents, specifically the factors affecting mortality, the differences observed in symptom presentation, and rate of clinical decline.

Readmissions among patients with COVID-19.

BACKGROUND: Hospital readmissions are associated with poor patient outcomes and increased health resource utilisation. The need to study readmission patterns is even bigger during a pandemic because the burden is further stretching the healthcare system. METHODS: We reviewed the initial hospitalisation and subsequent readmission for 19 patients with confirmed COVID-19 in the largest statewide hospital network in Rhode Island, US, from March 1st through April 19th, 2020. We also compared the characteristics and clinical outcomes between readmitted and non-readmitted patients. RESULTS: Of the 339 hospitalised patients with COVID-19, 279 discharged alive. Among them, 19/279 were readmitted (6.8%) after a median of 5 days. There was a significantly higher rate of hypertension, diabetes, chronic pulmonary disease, liver disease, cancer and substance abuse among the readmitted compared with non-readmitted patients. The most common reasons of readmissions happening within 12 days from discharge included respiratory distress and thrombotic episodes, while those happening at a later time included psychiatric illness exacerbations and falls. The length of stay during readmission was longer than during index admission and more demanding on healthcare resources. CONCLUSION: Among hospitalised patients with COVID-19, those readmitted had a higher burden of comorbidities than the non-readmitted. Within the first 12 days from discharge, readmission reasons were more likely to be associated with COVID-19, while those happening later were related to other reasons. Readmissions characterisation may help in defining optimal timing for patient discharge and ensuring safe care transition.

Hydroxychloroquine use in hospitalised patients with COVID-19: An observational matched cohort study.

AIM: To assess the efficacy and safety of hydroxychloroquine with or without azithromycin) in hospitalized adult patients with COVID-19. METHODS: We utilized a hospital based prospective data registry. The primary end point was to assess the impact of hydroxychloroquine with or without azithromycin, on outcome, length of hospitalization, and time to clinical improvement. We utilized treatment effects with inverse-probability-weighting and Cox proportional hazards models. All analyses accounted for age, gender, race, severity on admission, days from symptoms onset and chronic comorbidities. RESULTS: 36 patients received hydroxychloroquine and were age- and sex-matched to 72 patients with COVID-19 who received supportive care. Compared to supportive care, the use of HCQ did not shorten the time to clinical improvement (+0.23 days; 95% CI: -1.8-2.3 days) nor did it shorten the duration of hospital stay (+0.91 days; 95% CI: -1.1-2.9 days). Additionally, HCQ did not decrease the risk of COVID-19 in-hospital death (aHR 1.67; 95% CI: 0.29-9.36). Finally, we observed a slight QTc prolongation from a baseline of 444 ± 26 ms to 464 ± 32 ms (mean±SD) among patients receiving hydroxychloroquine with or without azithromycin. CONCLUSION: This study did not yield benefits from hydroxychloroquine use in patients with COVID-19 and monitoring for adverse events is warranted. Nevertheless, the treatment was safely studied under the guidance of an antimicrobial stewardship program.

Emerging Technologies for Use in the Study, Diagnosis, and Treatment of Patients with COVID-19.

INTRODUCTION: The COVID-19 pandemic has caused an unprecedented health and economic worldwide crisis. Innovative solutions are imperative given limited resources and immediate need for medical supplies, healthcare support and treatments. AIM: The purpose of this review is to summarize emerging technologies being implemented in the study, diagnosis, and treatment of COVID-19. RESULTS: Key focus areas include the applications of artificial intelligence, the use of Big Data and Internet of Things, the importance of mathematical modeling for predictions, utilization of technology for community screening, the use of nanotechnology for treatment and vaccine development, the utility of telemedicine, the implementation of 3D-printing to manage new demands and the potential of robotics. CONCLUSION: The review concludes by highlighting the need for collaboration in the scientific community with open sharing of knowledge, tools, and expertise.