Estetrol Is Safe and Well Tolerated during Treatment of Hospitalized Men and Women with Moderate COVID-19 in a Randomized, Double-Blind Study.
Jean Michel FoidartKrzysztof SimonWulf H UtianFranck Mauvais-JarvisJonathan DouxfilsGraham DixonPhilip BarringtonPublished in: Journal of clinical medicine (2023)
Epidemiological data suggest that the severe acute respiratory syndrome coronavirus 2 infection rate is higher in women than in men, but the death rate is lower, while women (>50 years) on menopausal hormone therapy (MHT) have a higher survival rate than those not on MHT. Classical oral estrogen enhances the synthesis of coagulation markers and may increase the risk of thromboembolic events that are common in coronavirus disease 2019 (COVID-19). The favorable hemostatic profile of estetrol (E4) might be suitable for use in women who are receiving estrogen treatment and contract COVID-19. A multicenter, randomized, double-blind, placebo-controlled, phase 2 study (NCT04801836) investigated the efficacy, safety, and tolerability of E4 versus placebo in hospitalized patients with moderate COVID-19. Eligible postmenopausal women and men (aged ≥ 18 years old) were randomized to E4 15 mg or placebo, once daily for 21 days, in addition to the standard of care (SoC). The primary efficacy endpoint of improvement in COVID-19 (percentage of patients recovered at day 28) between the placebo and E4 arms was not met. E4 was well tolerated, with no safety signals or thromboembolic events, suggesting that postmenopausal women can safely continue E4-based therapy in cases of moderate COVID-19 managed with SoC.
Keyphrases
- double blind
- coronavirus disease
- placebo controlled
- postmenopausal women
- respiratory syndrome coronavirus
- sars cov
- clinical trial
- phase iii
- phase ii
- bone mineral density
- phase ii study
- study protocol
- polycystic ovary syndrome
- open label
- high intensity
- healthcare
- stem cells
- randomized controlled trial
- ejection fraction
- bone marrow
- pregnancy outcomes
- skeletal muscle
- metabolic syndrome
- machine learning
- type diabetes
- high resolution
- body composition
- artificial intelligence