ArticleEndocrinology and metabolism (Seoul, Korea)2023
Mortality and Severity of Coronavirus Disease 2019 in Patients with Long-Term Glucocorticoid Therapy: A Korean Nationwide Cohort Study.
Article in Endocrinology and metabolism (Seoul, Korea), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Severe COVID-19 in the Republic of Korea: Epidemiology, Risk Factors, Therapeutics, and Prognostic Models From Nationwide Data.Journal of Korean medical science · 2026Review
- Article
- Hormonal Implications of SARS-CoV-2: A Review of Endocrine Disruptions.Scientifica · 2025Review
- Pituitary Diseases and COVID-19 Outcomes in South Korea: A Nationwide Cohort Study.Journal of clinical medicine · 2023Article
- Glucocorticoids as a Double-Edged Sword in the Treatment of COVID-19: Mortality and Severity of COVID-19 in Patients Receiving Long-Term Glucocorticoid Therapy.Endocrinology and metabolism (Seoul, Korea) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgruoundThe severity of coronavirus disease 2019 (COVID-19) among patients with long-term glucocorticoid treatment (LTGT) has not been established. We aimed to evaluate the association between LTGT and COVID-19 prognosis.
methodsA Korean nationwide cohort database of COVID-19 patients between January 2019 and September 2021 was used. LTGT was defined as exposure to at least 150 mg of prednisolone (≥5 mg/day and ≥30 days) or equivalent glucocorticoids 180 days before COVID-19 infection. The outcome measurements were mortality, hospitalization, intensive care unit (ICU) admission, length of stay, and mechanical ventilation.
resultsAmong confirmed patients with COVID-19, the LTGT group (n=12,794) was older and had a higher proportion of comorbidities than the control (n=359,013). The LTGT group showed higher in-hospital, 30-day, and 90-day mortality rates than the control (14.0% vs. 2.3%, 5.9% vs. 1.1%, and 9.9% vs. 1.8%, respectively; all P<0.001). Except for the hospitalization rate, the length of stay, ICU admission, and mechanical ventilation proportions were significantly higher in the LTGT group than in the control (all P<0.001). Overall mortality was higher in the LTGT group than in the control group, and the significance remained in the fully adjusted model (odds ratio [OR], 5.75; 95% confidence interval [CI], 5.31 to 6.23) (adjusted OR, 1.82; 95% CI, 1.67 to 2.00). The LTGT group showed a higher mortality rate than the control within the same comorbidity score category.
conclusionLong-term exposure to glucocorticoids increased the mortality and severity of COVID-19. Prevention and early proactive measures are inevitable in the high-risk LTGT group with many comorbidities.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.