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1.
Crit Care Med ; 51(1): 103-116, 2023 01 01.
Article in English | MEDLINE | ID: covidwho-2161200

ABSTRACT

OBJECTIVES: Severe cases of COVID-19 pneumonia can lead to acute respiratory distress syndrome (ARDS). Release of interleukin (IL)-33, an epithelial-derived alarmin, and IL-33/ST2 pathway activation are linked with ARDS development in other viral infections. IL-22, a cytokine that modulates innate immunity through multiple regenerative and protective mechanisms in lung epithelial cells, is reduced in patients with ARDS. This study aimed to evaluate safety and efficacy of astegolimab, a human immunoglobulin G2 monoclonal antibody that selectively inhibits the IL-33 receptor, ST2, or efmarodocokin alfa, a human IL-22 fusion protein that activates IL-22 signaling, for treatment of severe COVID-19 pneumonia. DESIGN: Phase 2, double-blind, placebo-controlled study (COVID-astegolimab-IL). SETTING: Hospitals. PATIENTS: Hospitalized adults with severe COVID-19 pneumonia. INTERVENTIONS: Patients were randomized to receive IV astegolimab, efmarodocokin alfa, or placebo, plus standard of care. The primary endpoint was time to recovery, defined as time to a score of 1 or 2 on a 7-category ordinal scale by day 28. MEASUREMENTS AND MAIN RESULTS: The study randomized 396 patients. Median time to recovery was 11 days (hazard ratio [HR], 1.01 d; p = 0.93) and 10 days (HR, 1.15 d; p = 0.38) for astegolimab and efmarodocokin alfa, respectively, versus 10 days for placebo. Key secondary endpoints (improved recovery, mortality, or prevention of worsening) showed no treatment benefits. No new safety signals were observed and adverse events were similar across treatment arms. Biomarkers demonstrated that both drugs were pharmacologically active. CONCLUSIONS: Treatment with astegolimab or efmarodocokin alfa did not improve time to recovery in patients with severe COVID-19 pneumonia.


Subject(s)
COVID-19 , Respiratory Distress Syndrome , Adult , Humans , Interleukin-33 , SARS-CoV-2 , Interleukin-1 Receptor-Like 1 Protein , Treatment Outcome
2.
PLoS Comput Biol ; 17(1): e1008470, 2021 01.
Article in English | MEDLINE | ID: covidwho-1058291

ABSTRACT

Finding medications or vaccines that may decrease the infectious period of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could potentially reduce transmission in the broader population. We developed a computational model of the U.S. simulating the spread of SARS-CoV-2 and the potential clinical and economic impact of reducing the infectious period duration. Simulation experiments found that reducing the average infectious period duration could avert a median of 442,852 [treating 25% of symptomatic cases, reducing by 0.5 days, reproductive number (R0) 3.5, and starting treatment when 15% of the population has been exposed] to 44.4 million SARS-CoV-2 cases (treating 75% of all infected cases, reducing by 3.5 days, R0 2.0). With R0 2.5, reducing the average infectious period duration by 0.5 days for 25% of symptomatic cases averted 1.4 million cases and 99,398 hospitalizations; increasing to 75% of symptomatic cases averted 2.8 million cases. At $500/person, treating 25% of symptomatic cases saved $209.5 billion (societal perspective). Further reducing the average infectious period duration by 3.5 days averted 7.4 million cases (treating 25% of symptomatic cases). Expanding treatment to 75% of all infected cases, including asymptomatic infections (R0 2.5), averted 35.9 million cases and 4 million hospitalizations, saving $48.8 billion (societal perspective and starting treatment after 5% of the population has been exposed). Our study quantifies the potential effects of reducing the SARS-CoV-2 infectious period duration.


Subject(s)
COVID-19 Drug Treatment , COVID-19/transmission , Models, Biological , Pandemics , SARS-CoV-2 , COVID-19/epidemiology , COVID-19 Vaccines/therapeutic use , Computational Biology , Computer Simulation , Humans , Pandemics/prevention & control , Pandemics/statistics & numerical data , SARS-CoV-2/drug effects , Time Factors , United States/epidemiology , Virus Shedding/drug effects
3.
Am J Prev Med ; 59(4): 493-503, 2020 10.
Article in English | MEDLINE | ID: covidwho-645862

ABSTRACT

INTRODUCTION: Given the continuing COVID-19 pandemic and much of the U.S. implementing social distancing owing to the lack of alternatives, there has been a push to develop a vaccine to eliminate the need for social distancing. METHODS: In 2020, the team developed a computational model of the U.S. simulating the spread of COVID-19 coronavirus and vaccination. RESULTS: Simulation experiments revealed that to prevent an epidemic (reduce the peak by >99%), the vaccine efficacy has to be at least 60% when vaccination coverage is 100% (reproduction number=2.5-3.5). This vaccine efficacy threshold rises to 70% when coverage drops to 75% and up to 80% when coverage drops to 60% when reproduction number is 2.5, rising to 80% when coverage drops to 75% when the reproduction number is 3.5. To extinguish an ongoing epidemic, the vaccine efficacy has to be at least 60% when coverage is 100% and at least 80% when coverage drops to 75% to reduce the peak by 85%-86%, 61%-62%, and 32% when vaccination occurs after 5%, 15%, and 30% of the population, respectively, have already been exposed to COVID-19 coronavirus. A vaccine with an efficacy between 60% and 80% could still obviate the need for other measures under certain circumstances such as much higher, and in some cases, potentially unachievable, vaccination coverages. CONCLUSIONS: This study found that the vaccine has to have an efficacy of at least 70% to prevent an epidemic and of at least 80% to largely extinguish an epidemic without any other measures (e.g., social distancing).


Subject(s)
Communicable Disease Control , Computer Simulation , Coronavirus Infections , Pandemics , Pneumonia, Viral , Vaccination , Viral Vaccines/pharmacology , Betacoronavirus/isolation & purification , COVID-19 , COVID-19 Vaccines , Communicable Disease Control/methods , Communicable Disease Control/statistics & numerical data , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Disease Eradication/methods , Disease Eradication/statistics & numerical data , Humans , Needs Assessment , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , SARS-CoV-2 , Treatment Outcome , United States/epidemiology , Vaccination/methods , Vaccination/statistics & numerical data , Vaccination Coverage , Viral Vaccines/standards
4.
Health Aff (Millwood) ; 39(6): 927-935, 2020 06.
Article in English | MEDLINE | ID: covidwho-123936

ABSTRACT

With the coronavirus disease 2019 (COVID-19) pandemic, one of the major concerns is the direct medical cost and resource use burden imposed on the US health care system. We developed a Monte Carlo simulation model that represented the US population and what could happen to each person who got infected. We estimated resource use and direct medical costs per symptomatic infection and at the national level, with various "attack rates" (infection rates), to understand the potential economic benefits of reducing the burden of the disease. A single symptomatic COVID-19 case could incur a median direct medical cost of $3,045 during the course of the infection alone. If 80 percent of the US population were to get infected, the result could be a median of 44.6 million hospitalizations, 10.7 million intensive care unit (ICU) admissions, 6.5 million patients requiring a ventilator, 249.5 million hospital bed days, and $654.0 billion in direct medical costs over the course of the pandemic. If 20 percent of the US population were to get infected, there could be a median of 11.2 million hospitalizations, 2.7 million ICU admissions, 1.6 million patients requiring a ventilator, 62.3 million hospital bed days, and $163.4 billion in direct medical costs over the course of the pandemic.


Subject(s)
Coronavirus Infections/economics , Disease Outbreaks/economics , Health Care Costs/statistics & numerical data , Health Resources/economics , Hospital Costs/statistics & numerical data , Pandemics/economics , Pneumonia, Viral/economics , COVID-19 , Delivery of Health Care/economics , Disease Outbreaks/statistics & numerical data , Female , Health Resources/statistics & numerical data , Humans , Intensive Care Units/economics , Intensive Care Units/statistics & numerical data , Length of Stay/economics , Male , Monte Carlo Method , Pandemics/statistics & numerical data , United States
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