Reduce COVID-19 Mortality Research Articles

Assessing the Efficacy and Challenges of Tofacitinib in the Management of COVID-19: A Systematic Review and Meta-analysis of Cohort Studies

Objectives: Global healthcare faces challenges in combating COVID-19, with rising cases despite widespread vaccination. Severe COVID-19 cases, marked by acute respiratory distress and cytokine release syndrome, highlight the importance of managing cytokine storms. Janus kinase (JAK) inhibitors, such as tofacitinib, show promise in this regard. While tofacitinib is recommended for severe cases, challenges include adverse effects, conflicting studies, and the need for further investigation of new virus strains. Overcoming these hurdles is crucial for developing an effective treatment protocol and reducing COVID-19 mortality. Methods: This study conducted a comprehensive search across PubMed, Scopus, and ISI Web of Science for observational studies on tofacitinib treatment in human adults with COVID-19. The search covered a specified period up to 2024. Data extraction, including study characteristics and quality assessment, employed the Newcastle Ottawa Scale and a modified Cochrane tool. Statistical analysis, conducted with Comprehensive Meta-Analysis Software, assessed heterogeneity and significance levels. Results: The meta-analysis of the three studies showed a significant reduction in mortality (Risk Ratio: 0.372, 95% CI: 0.213–0.649, P-value = 0.001) with low heterogeneity (Cochrane P-value = 0.793), while no significant reduction in the need for mechanical ventilation was observed (Cochrane P-value = 0.194). Conclusion: Tofacitinib administration shows a significant reduction in COVID-19 mortality. However, the limited studies on its efficacy highlight the need for cautious interpretation in clinical assessments.

Social Cohesion in Different Periods of the Pandemic: What Works in Reducing COVID-19 Mortality

Abstract Background Inconsistent results were reported regarding the effects of social cohesion dimensions on COVID-19 mortality rate (MR). Most studies have indicated that trust in civil services, but not participating in social activities, reduces COVID-19 MR, possibly because of the highly contagious nature of this disease. In this study, we investigated how the effects of various dimensions of social cohesion on COVID-19 MR vary across different phases of the pandemic. Methods This longitudinal study was conducted using data from 86 countries that participated in the 2017-2020 World Values Survey. The measures of social cohesion were identified by aggregating survey responses to the country level. COVID-19 MR was calculated-using data from the European Centre for Disease Prevention and Control-separately for three distinct periods during the pandemic (January 2021-June 2022). A factor analysis was conducted to identify the dimensions of social cohesion, and negative binomial regression models were constructed to analyze data for each period. Results The factor analysis revealed three dimensions of social cohesion: social participation and inclusion, health service equality, and trust in civil services and democracy. Our findings suggest that each dimension of social cohesion uniquely affected COVID-19 outcomes in different periods of the pandemic. Trust in civil services and a positive attitude toward democracy are strongly associated with a low COVID-19 MR. However, this association was observed only in the early and middle stages of the pandemic. By contrast, our study demonstrated that in the late stage of the pandemic, increased social participation and inclusion helped combat COVID-19. Conclusions This study underscores the importance of collective cohesion and solidarity in mobilizing public responses against pandemic situations. This study emphasizes that preventive or regulatory interventions should be tailored to different phases of a pandemic. Key messages • COVID-19 mortality rate was negatively associated with trust in civil services during the early two periods and with social participation and social inclusion during the late period of the pandemic. • This study emphasizes that preventive or regulatory interventions should be tailored to different phases of a pandemic.

Health insurance system fragmentation and COVID-19 mortality: Evidence from Peru.

Peru has a fragmented health insurance system in which most insureds can only access the providers in their insurer's network. The two largest sub-systems covered about 53% and 30% of the population at the start of the pandemic; however, some individuals have dual insurance and can thereby access both sets of providers. We use data on 24.7 million individuals who belonged to one or both sub-systems to investigate the effect of dual insurance on COVID-19 mortality. We estimate recursive bivariate probit models using the difference in the distance to the nearest hospital in the two insurance sub-systems as Instrumental Variable. The effect of dual insurance was to reduce COVID-19 mortality risk by 0.23% compared with the sample mean risk of 0.54%. This implies that the 133,128 COVID-19 deaths in the sample would have been reduced by 56,418 (95%CI: 34,894, 78,069) if all individuals in the sample had dual insurance.

Risk Factors Associated with Mortality in Hospitalized COVID-19 Patients: A Case Study from Southeast Iran

Background: This study aimed to identify mortality risk factors among hospitalized COVID-19 patients in southeast Iran. Methods: This cross-sectional study used data from the COVID-19 patients admitted to Afzalipour teaching hospital in Kerman, Iran, from February 2020 to September 2021. The demographic and clinical data of 6,057 patients were analyzed using Bayesian network and logistic regression models. Results: Out of 6,057 patients, 333 (5.5%) died. The most important risk factors for COVID-19 mortality were age, gender, fever, headache, decreased level of consciousness (LoC), chronic liver disease, blood oxygen level (BOL), admission season, and length of stay (LoS). Fever, headache, and longer LoS were protective and mortality-reducing variables. Conclusion: Following model estimation results, it is recommended that older male patients with low oxygen levels and a lower LoC, as well as patients with chronic liver disease, receive additional medical care and not be discharged prematurely. Early medical interventions for high-risk patients may reduce COVID-19 mortality risk.

Screening Strategies to Reduce COVID-19 Mortality in Nursing Homes

Nursing home residents continue to bear a disproportionate share of COVID-19 morbidity and mortality, accounting for 9% of all US COVID-19 deaths in 2023, despite comprising only 0.4% of the population. To evaluate the cost-effectiveness of screening strategies in reducing COVID-19 mortality in nursing homes. An agent-based model was developed to simulate SARS-CoV-2 transmission in the nursing home setting. Parameters were determined using SARS-CoV-2 virus data and COVID-19 data from the Centers for Medicare & Medicaid Services and US Centers for Disease Control and Prevention that were published between 2020 and 2023, as well as data on nursing homes published between 2010 and 2023. The model used in this study simulated interactions and SARS-CoV-2 transmission between residents, staff, and visitors in a nursing home setting. The population used in the simulation model was based on the size of the average US nursing home and recommended staffing levels, with 90 residents, 90 visitors (1 per resident), and 83 nursing staff members. Screening frequency (none, weekly, and twice weekly) was varied over 30 days against varying levels of COVID-19 community incidence, booster uptake, and antiviral use. The main outcomes were SARS-CoV-2 infections, detected cases per 1000 tests, and incremental cost of screening per life-year gained. Nursing home interactions were modeled between 90 residents, 90 visitors, and 83 nursing staff over 30 days, completing 4000 to 8000 simulations per parameter combination. The incremental cost-effectiveness ratios of weekly and twice-weekly screening were less than $150 000 per resident life-year with moderate (50 cases per 100 000) and high (100 cases per 100 000) COVID-19 community incidence across low-booster uptake and high-booster uptake levels. When COVID-19 antiviral use reached 100%, screening incremental cost-effectiveness ratios increased to more than $150 000 per life-year when booster uptake was low and community incidence was high. The results of this cost-effectiveness analysis suggest that screening may be effective for reducing COVID-19 mortality in nursing homes when COVID-19 community incidence is high and/or booster uptake is low. Nursing home administrators can use these findings to guide planning in the context of widely varying levels of SARS-CoV-2 transmission and intervention measures across the US.

Association of Social Vulnerability and COVID-19 Mortality Rates in Texas between 15 March 2020, and 21 July 2022: An Ecological Analysis.

Despite the key role of social vulnerability such as economic disadvantage in health outcomes, research is limited on the impact of social vulnerabilities on COVID-19-related deaths, especially at the state and county level in the USA. We conducted a cross-sectional ecologic analysis of COVID-19 mortality by the county-level Minority Health Social Vulnerability Index (MH SVI) and each of its components in Texas. Negative binomial regression (NBR) analyses were used to estimate the association between the composite MH SVI (and its components) and COVID-19 mortality. A 0.1-unit increase in the overall MH SVI (IRR, 1.27; 95% CI, 1.04-1.55; p = 0.017) was associated with a 27% increase in the COVID-19 mortality rate. Among the MH SVI component measures, only low socioeconomic status (IRR, 1.55; 95% CI, 1.28-1.89; p = 0.001) and higher household composition (e.g., proportion of older population per county) and disability scores (IRR, 1.47; 95% CI, 1.29-1.68; p < 0.001) were positively associated with COVID-19 mortality rates. This study provides further evidence of disparities in COVID-19 mortality by social vulnerability and can inform decisions on the allocation of social resources and services as a strategy for reducing COVID-19 mortality rates and similar pandemics in the future.

Highest Risk Groups and the Distribution of COVID-19 Vaccine in Milwaukee County, Wisconsin: A Census Tract Level Evaluation

Introduction: COVID-19 vaccinations are the most effective approach to COVID-19 control. The vaccine deployment was designed to support the at-risk individuals and reduce COVID-19 mortality. This study asked, “Were vaccine doses successfully administered to Milwaukee County Wisconsin census tracts with the highest at-risk populations?”

Liver Iron Overload Drives COVID-19 Mortality: a Two-Sample Mendelian Randomization Study.

Iron overload has been associated with an increased risk of COVID-19 severity and mortality in observational studies, but it remains unclear whether these associations represent causal effects. We performed a two-sample Mendelian randomization (MR) to determine associations between genetic liability to iron overload and the risk of COVID-19 severity and mortality. From genome-wide association studies of European ancestry, single-nucleotide polymorphisms associated with liver iron (n = 32,858) and ferritin (n = 23,986) were selected as exposure instruments, and summary statistics of the hospitalization (n = 16,551) and mortality (n = 15,815) of COVID-19 were utilized as the outcome. We used the inverse-variance weighted (IVW) method as the primary analysis to estimate causal effects, and other alternative approaches as well as comprehensive sensitivity analysis were conducted for estimating the robustness of identified associations. Genetically predicted high liver iron levels were associated with an increased risk of COVID-19 mortality based on the results of IVW analysis (OR = 1.38, 95% CI: 1.05-1.82, P = 0.02). Likewise, sensitivity analyses showed consistent and robust results in general (all P > 0.05). A higher risk of COVID-19 hospitalization trend was also observed in patients with high liver iron levels without statistical significance. This study suggests that COVID-19 mortality might be partially driven by the iron accumulation in the liver, supporting the classification of iron overload as one of the independent death risk factors. Therefore, avoiding iron overload and maintaining normal iron levels may be a powerful measure to reduce COVID-19 mortality.

The Severity of COVID-19 in Diabetes Patients.

Diabetic patients are significantly stimulated by COVID-19 infection. The dreadful risk of COVID-19 mortality may be affected. In order to preserve precious lives, it is essential to comprehend how diabetes and COVID-19 are related, as well as how to manage diabetes. We aimed to focus on the mechanism, impact, and drug treatment of diabetes in COVID-19 patients. A comprehensive scrutiny of the published literature in diverse pharmaceutical and medical databases such as Google Scholar, PubMed, Science Direct, DOAJ etc., were successfully conducted and classified accordingly. We discussed the severity of COVID-19 in diabetes patients. A patient with diabetes has a higher risk of COVID-19 mortality by influencing the development and prognosis of the disease. The recommended drugs for diabetes treatment in COVID-19 may reduce COVID-19 mortality. Metabolic syndrome diabetes is a risk factor enhancing the development and diagnosis of COVID-19. In order to treat diabetic patients who have COVID-19 infection, insulin is preferable over oral hypoglycemic medications.

Effectiveness of a multicomponent intervention to face the COVID-19 pandemic in Rio de Janeiro’s favelas: difference-in-differences analysis

IntroductionFew community-based interventions addressing the transmission control and clinical management of COVID-19 cases have been reported, especially in poor urban communities from low-income and middle-income countries. Here, we analyse the...

Associations between the stringency of COVID-19 containment policies and health service disruptions in 10 countries

BackgroundDisruptions in essential health services during the COVID-19 pandemic have been reported in several countries. Yet, patterns in health service disruption according to country responses remain unclear. In this paper, we investigate associations between the stringency of COVID-19 containment policies and disruptions in 31 health services in 10 low- middle- and high-income countries in 2020.MethodsUsing routine health information systems and administrative data from 10 countries (Chile, Ethiopia, Ghana, Haiti, Lao People’s Democratic Republic, Mexico, Nepal, South Africa, South Korea, and Thailand) we estimated health service disruptions for the period of April to December 2020 by dividing monthly service provision at national levels by the average service provision in the 15 months pre-COVID (January 2019-March 2020). We used the Oxford COVID-19 Government Response Tracker (OxCGRT) index and multi-level linear regression analyses to assess associations between the stringency of restrictions and health service disruptions over nine months. We extended the analysis by examining associations between 11 individual containment or closure policies and health service disruptions. Models were adjusted for COVID caseload, health service category and country GDP and included robust standard errors.FindingsChronic disease care was among the most affected services. Regression analyses revealed that a 10% increase in the mean stringency index was associated with a 3.3 percentage-point (95% CI -3.9, -2.7) reduction in relative service volumes. Among individual policies, curfews, and the presence of a state of emergency, had the largest coefficients and were associated with 14.1 (95% CI -19.6, 8.7) and 10.7 (95% CI -12.7, -8.7) percentage-point lower relative service volumes, respectively. In contrast, number of COVID-19 cases in 2020 was not associated with health service disruptions in any model.ConclusionsAlthough containment policies were crucial in reducing COVID-19 mortality in many contexts, it is important to consider the indirect effects of these restrictions. Strategies to improve the resilience of health systems should be designed to ensure that populations can continue accessing essential health care despite the presence of containment policies during future infectious disease outbreaks.

Different clinical guidelines, common goal: To reduce COVID-19 mortality.

The tendency of the Omicron variant to rapidly became the dominant SARS-CoV-2 strain and its weaker virulence than other strains worldwide has prompted many countries to adjust their public health strategies. This work summarizes all appropriate clinical interventions to reduce the public health burden caused by COVID-19 according to guidelines from the World Health Organization and 10 countries, i.e., the United States of America (USA), India, France, Germany, Brazil, South Korea, Japan, Italy, the United Kingdom (UK), and China. Five stages of COVID-19 were identified: asymptomatic infection and mild, moderate, severe, and critical illness. Most guidelines recommend antivirals starting with mild cases for those from Germany and India. Since more drugs are being developed and are becoming available to COVID-19 patients, guidelines are increasingly being updated with new pharmacological intervention strategies. Thus, a global view needs to be adopted to provide helpful options and precise treatment strategies during the lasting fight against the COVID-19 pandemic.

Tough Talks COVID-19 Digital Health Intervention for Vaccine Hesitancy Among Black Young Adults: Protocol for a Hybrid Type 1 Effectiveness Implementation Randomized Controlled Trial.

Interventions for increasing the uptake of COVID-19 vaccination among Black young adults are central to ending the pandemic. Black young adults experience harms from structural forces, such as racism and stigma, that reduce receptivity to traditional public health messaging due to skepticism and distrust. As such, Black young adults continue to represent a priority population on which to focus efforts for promoting COVID-19 vaccine uptake. In aims 1 and 2, the Tough Talks digital health intervention for HIV disclosure will be adapted to address COVID-19 vaccine hesitancy and tailored to the experiences of Black young adults in the southern United States (Tough Talks for COVID-19). In aim 3, the newly adapted Tough Talks for COVID-19 digital health intervention will be tested across the following three southern states: Alabama, Georgia, and North Carolina. Our innovative digital health intervention study will include qualitative and quantitative assessments. A unique combination of methodological techniques, including web-based surveys, choose-your-own-adventures, digital storytelling, user acceptability testing, and community-based participatory approaches, will culminate in a 2-arm hybrid type 1 effectiveness implementation randomized controlled trial, wherein participants will be randomized to the Tough Talks for COVID-19 intervention arm or a standard-of-care control condition (N=360). Logistic regression will be used to determine the effect of the treatment arm on the probability of vaccination uptake (primary COVID-19 vaccine series or recommended boosters). Concurrently, the inner and outer contexts of implementation will be ascertained and catalogued to inform future scale-up. Florida State University's institutional review board approved the study (STUDY00003617). Our study was funded at the end of April 2021. Aim 1 data collection concluded in early 2022. The entire study is expected to conclude in January 2025. If effective, our digital health intervention will be poised for broad, rapid dissemination to reduce COVID-19 mortality among unvaccinated Black young adults in the southern United States. Our findings will have the potential to inform efforts that seek to address medical mistrust through participatory approaches. The lessons learned from the conduct of our study could be instrumental in improving health care engagement among Black young adults for several critical areas that disproportionately harm this community, such as tobacco control and diabetes prevention. ClinicalTrials.gov NCT05490329; https://clinicaltrials.gov/ct2/show/NCT05490329. DERR1-10.2196/41240.

Are We #Stayinghome to Flatten the Curve?

The recent spread of COVID-19 across the U.S. led to concerted efforts by states to ``flatten the curve through the adoption of stay-at-home mandates that encourage individuals to reduce travel and maintain social distance. Combining data on changes in travel activity with COVID-19 health outcomes and state policy adoption timing, we characterize nationwide changes in mobility patterns and isolate the portion attributable to statewide mandates. We find evidence of dramatic nationwide declines in mobility prior to adoption of any statewide mandates. Once states adopt a mandate, we estimate further mandate-induced declines between 2.1 and 7.0 percentage points across methods that account for states' differences in travel behavior prior to policy adoption. In addition, we investigate the effects of stay-at-home mandates on changes in COVID-19 health outcomes while controlling for pre-trends and observed pre-treatment mobility patterns. We estimate mandate-induced declines between 0.13 and 0.17 in deaths (5.6 to 6.0 in hospitalizations) per 100 thousand across methods. Across 43 adopting states, this represents 23,366-30,144 fewer deaths (and roughly one million averted hospitalizations) for the months of March and April - which indicates that death rates could have been 42-54% higher had states not adopted statewide policies. We further find evidence that changes in mobility patterns prior to adoption of statewide policies also played a role in reducing COVID-19 mortality and morbidity. Adding in averted deaths due to pre-mandate social distancing behavior, we estimate a total of 48-71,000 averted deaths from COVID-19 for the two-month period. Given that the actual COVID-19 death toll for March and April was 55,922, our estimates suggest that deaths would have been 1.86-2.27 times what they were absent any stay-at-home mandates during this period. These estimates represent a lower bound on the health impacts of stay-at-home policies, as they do not account for spillovers or undercounting of COVID-19 mortality. Our findings indicate that early behavior changes and later statewide policies reduced death rates and helped attenuate the negative consequences of COVID-19. Further, our findings of substantial reductions in mobility prior to state-level policies convey important policy implications for re-opening. Take Away Link https://are.berkeley.edu/sites/are.berkeley.edu/files/PolicyTakeAway_Web.pdf

Health Benefits of Residence at Moderate Altitude Do Not Reduce COVID-19 Mortality.

Moderate altitude (1000−2000 m above sea level) residence is emerging as a protective factor from the mortality of various causes, including of cardiovascular diseases. Conversely, mortality from certain respiratory diseases is higher at these altitudes than in lowlands. These divergent outcomes could indicate either beneficial or detrimental effects of altitude on the mortality of COVID-19 that primarily infects the respiratory tract but results in multi-organ damage. Previous epidemiological data indeed suggest divergent outcomes of moderate to high altitude residence in various countries. Confounding factors, such as variations in the access to clinical facilities or selection biases of investigated populations, may contribute to the equivocation of these observations. We interrogated a dataset of the complete population of an Alpine country in the center of Europe with relatively similar testing and clinical support conditions across altitude-levels of residence (up to around 2000 m) to assess altitude-dependent mortality from COVID-19 throughout 2020. While a reduced all-cause mortality was confirmed for people living higher than 1000 m, no differences in the mortality from COVID-19 between the lowest and the highest altitude regions were observed for the overall population and the population older than 60 years as well. Conversely, COVID-19 mortality seems to have been reduced in the very old (>85 years) women at moderate altitudes.

The evidence from clinical trials on Gout medicines effect on COVID-19: A protocol for systematic review and meta-analysis.

To evaluate the available evidence from clinical trials on the efficacy of gout medicines against COVID-19. Systematic review and Meta-analysis. We are systematically searching five databases [PubMed, Embase, CT.gov, ICTRP, CINAHL (EBSCO)]. We are following the PRISMA statement and the EPOC guidelines. The meta-analysis will be conducted using Revman-5.4.1 from Cochrane collaboration, UK. This review's protocol was also registered in PROSPERO, University of York, UK (CRD42022299718). In this meta-analysis, we plan to give a conclusive overview of the available evidence on the efficacy of the medications used to manage gout in reducing COVID-19 mortality, ICU admission, ventilation rate and hospitalization duration. If the results were positive, these drugs would greatly add to the scarce treatment options against COVID-19. Furthermore, these drugs might provide an excellent alternative to inconvenient and expensive drugs. Additionally, most of these drugs have a well-established safety profile for use during nursing, making them a much safer option for nursing mothers with COVID-19.

Outcomes and Management of Sars-CoV2 Omicron Variant in Recipients of Hematopoietic Cell Transplantation (HCT) and CAR-T Therapy

HCT or CAR-T therapy recipients who develop COVID-19 have decreased overall survival (68% Sharma et al. Lancet Haematol. 2021), likely due to disease-inherent and therapy-related immunodeficiency. The development of specific COVID therapies and vaccination during the pandemic has changed the prevention and management of COVID-19 infection. Nevertheless, with the appearance of new SARS-CoV2 variants and omicron, in particular, questions about the severity of disease in high-risk patients are yet to be defined. Methods We included retrospectively 218 adult HCT/CAR-T recipients diagnosed with presumed COVID-19 omicron variant at Memorial Sloan Kettering Cancer Center (MSK), New York, between December 2021 and February 2022. Data were analyzed using SPSS version 26 (IBM, Armonk, NY). Results Among 218 patients, 107 received an autologous HCT, 92 allogeneic HCT, and 19 CAR-T. The median age was 58 years (range, 21 to 87), and 56% were male. The median time from HCT/CAR-T to SARS-CoV2 infection was 44 months (range, 0.1 to 272 months), and 27 patients (12%) acquired SARS-CoV2 within the first 100 days post-HCT/CAR-T. The primary hematologic diagnoses were plasma cell (37%), myeloid (37%), and lymphoid (26%) malignancies. Myeloablative conditioning was performed in 90 patients (41%). In the alloHCT recipients, 17 donors (18%) were matched related, 51 (55%) matched unrelated, 19 (21%) umbilical cord blood, and 5 (6%) haploidentical. 31% patients had 2 or more comorbidities and 9% received IVIG for hypogammaglobulinemia within the prior month. Of note, 40% of the patients were on active cancer therapy (maintenance or treatment) at the time of COVID-19 infection, and 47% of the alloHCT recipients were on GVHD immunosuppressive treatment. Characteristics of COVID-19 omicron variant infection are shown in Table1. Specific COVID-19 treatment was administered in 111 patients (51%) and included MoAbs in 63 patients (56.7%), sotrovimab being the most frequently used (n=55, 87 %), remdesivir (n= 23, 21%), and other antivirals (n= 15, 14%). Risk factors for admission were presence of > 3 comorbidities (HR, 6.2; 95% CI 2.3-16.3, p= 0.000), progression of disease (POD) (HR, 3.8; 95% CI 1.6-8.9, p= 0.002,) and CAR-T therapy (HR 4.5; 95% CI 1.4-14.1p= 0.008). Regarding vaccination status, 84% (n= 183) of the cohort was vaccinated with mRNA Covid-19 vaccines, with 42% of patients having 1 booster before COVID19 infection. Interestingly, only 15/35 (42%) of the booster patients with measured serologic response showed SARS-CoV-19 Antibodies value> 2500 BAU/I. Nineteen patients showed low titer COVID-19 antibodies (<2500 BAU/I) testing after COVID-19 infection: 3 patients died (2 for POD, 1 for COVID), 3 were not vaccinated, 1 patient had to be revaccinated after HCT, and 6 patients were administered tixagevimab/cilgavimab for future prophylaxis. Persistent/Long COVID were present in 8 (3.5%) patients, showing fatigue and URI symptoms 5/8 (62%) had a previous admission for COVID-19 infection and 5/8 (62%) had a negative spike antibody titer before infection. The median duration of viral shedding (positive SARS-CoV-2 PCR) was 11 weeks (range, 3.2 to 19.1 weeks). After a median follow up of 4 months (range, 0 to 8 months), 21 patients (9.6%) had recurrent COVID infection, and two of them died. The overall mortality rate was 10% in all patients (22/218 patients), with 2.5% (6/22) attributable to COVID. Other causes of death were progressive disease (n=13), sepsis (n=1), and hepatic VOD (n=1). Comparing our omicron cohort with the initial 77 HCT & CAR-T patients infected with first wave COVID-19 in March 2020 at our institution, overall survival at 30 days was 94% vs 78% (p=0.010) (Figure 1). Conclusions In our cohort, mortality attributable to the COVID-19 omicron variant was low (2.5%), and only 19% required admission. Risk factors for admission were presence of 3 or more comorbidities, POD at time of COVID infection, and CAR-T therapy. Altogether our date confirms enhanced overall survival (94%) in HCT, and CART recipients infected with the omicron variant of SARS-CoV2 as compared to prior waves of the COVID-19 pandemic. Access to SARS-COV-2 specific treatments and the higher incidence of vaccination in this cohort appear to be key factors to successfully managing patients in order to reduce COVID-19 mortality. Pre-exposure prophylaxis became available later and is also likely important. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

A predictive model for hospitalization and survival to COVID-19 in a retrospective population-based study

The development of tools that provide early triage of COVID-19 patients with minimal use of diagnostic tests, based on easily accessible data, can be of vital importance in reducing COVID-19 mortality rates during high-incidence scenarios. This work proposes a machine learning model to predict mortality and risk of hospitalization using both 2 simple demographic features and 19 comorbidities obtained from 86,867 electronic medical records of COVID-19 patients, and a new method (LR-IPIP) designed to deal with data imbalance problems. The model was able to predict with high accuracy (90–93%, ROC-AUC = 0.94) the patient's final status (deceased or discharged), while its accuracy was medium (71–73%, ROC-AUC = 0.75) with respect to the risk of hospitalization. The most relevant characteristics for these models were age, sex, number of comorbidities, osteoarthritis, obesity, depression, and renal failure. Finally, to facilitate its use by clinicians, a user-friendly website has been developed (https://alejandrocisterna.shinyapps.io/PROVIA).

Does ivermectin reduce COVID-19 mortality and progression of disease severity? – A retrospective study.

Objective: To investigate the efficacy of ivermectin in the treatment of mild, moderate, and severe COVID-19 infection. Study Design: Retrospective Cohort study. Setting: COVID-19 Treatment Centre, Dr. Ruth K M Pfau Civil Hospital Karachi. Period: July 2020 to December 2020. Material &amp; Methods: Medical records of 423 patients during a selected duration of 6 months were reviewed. Patients were stratified into two groups based on whether or not they received a 6-day course of ivermectin in addition to the standard treatment for COVID-19. Primary outcome measures were rate of mortality, days from the start of treatment to negative SARS-CoV-2 PCR, and rate of step-up to the intensive care unit. Results: Patients who received ivermectin required a lesser number of days (8.39 days ± 2.04) to become COVID negative than the patients who didn't receive ivermectin (20.38 days ± 6.32), (p &lt; 0.001). Multinomial logistic regression showed that the patients who were given ivermectin for COVID 19 infection were four times more likely to be discharged home than stepping up to ICU. The ICU step-up rate in the ivermectin group was found to be 3.7% compared to 13.04% in the non-ivermectin group. No significant differences in mortality were found. Conclusion: Treatment with ivermectin in COVID-19 infection is associated with improved outcomes in terms of reduction in duration of illness as well as the progression of disease severity.

Should RECOVERY have used response adaptive randomisation? Evidence from a simulation study

BackgroundThe Randomised Evaluation of COVID-19 Therapy (RECOVERY) trial is aimed at addressing the urgent need to find effective treatments for patients hospitalised with suspected or confirmed COVID-19. The trial has had many successes, including discovering that dexamethasone is effective at reducing COVID-19 mortality, the first treatment to reach this milestone in a randomised controlled trial. Despite this, it continues to use standard or ‘fixed’ randomisation to allocate patients to treatments. We assessed the impact of implementing response adaptive randomisation within RECOVERY using an array of performance measures, to learn if it could be beneficial going forward. This design feature has recently been implemented within the REMAP-CAP platform trial.MethodsTrial data was simulated to closely match the data for patients allocated to standard care, dexamethasone, hydroxychloroquine, or lopinavir-ritonavir in the RECOVERY trial from March-June 2020, representing four out of five arms tested throughout this period. Trials were simulated in both a two-arm trial setting using standard care and dexamethasone, and a four-arm trial setting utilising all above treatments. Two forms of fixed randomisation and two forms of response-adaptive randomisation were tested. In the two-arm setting, response-adaptive randomisation was implemented across both trial arms, whereas in the four-arm setting it was implemented in the three non-standard care arms only. In the two-arm trial, randomisation strategies were performed at the whole trial level as well as within three pre-specified patient subgroups defined by patients’ respiratory support level.ResultsAll response-adaptive randomisation strategies led to more patients being given dexamethasone and a lower mortality rate in the trial. Subgroup specific response-adaptive randomisation reduced mortality rates even further. In the two-arm trial, response-adaptive randomisation reduced statistical power compared to FR, with subgroup level adaptive randomisation exhibiting the largest power reduction. In the four-arm trial, response-adaptive randomisation increased statistical power in the dexamethasone arm but reduced statistical power in the lopinavir arm. Response-adaptive randomisation did not induce any meaningful bias in treatment effect estimates nor did it cause any inflation in the type 1 error rate.ConclusionsUsing response-adaptive randomisation within RECOVERY could have increased the number of patients receiving the optimal COVID-19 treatment during the trial, while reducing the number of patients needed to attain the same study power as the original study. This would likely have reduced patient deaths during the trial and lead to dexamethasone being declared effective sooner. Deciding how to balance the needs of patients within a trial and future patients who have yet to fall ill is an important ethical question for the trials community to address. Response-adaptive randomisation deserves to be considered as a design feature in future trials of COVID-19 and other diseases.