The association between immunosuppressants use and COVID-19 adverse outcomes: national COVID-19 cohort in South Korea
Bibliographic record
Abstract
BACKGROUND: There is uncertainty of the effect of immunosuppression, including corticosteroids, before COVID-19 infection on COVID-19 outcomes. The aim of this study was to investigate the relationship between prehospitalization immunosuppressants use (exposure) and COVID-19 patient outcomes. METHODS: We conducted a population-based retrospective cohort study using a nationwide healthcare claims database of South Korea as of May 15, 2020. Confirmed COVID-19 infection in hospitalized individuals aged 40 years or older were included for analysis. We defined exposure variable by using inpatient and outpatient prescription records of immunosuppressants from the database. Our primary endpoint was a composite endpoint of all-cause death, intensive care unit (ICU) admission, and mechanical ventilation use. Inverse probability of treatment weighting (IPTW)-adjusted logistic regression analyses were used, to estimate odds ratio (OR) and 95% confidence intervals (CI), comparing immunosuppressants users and non-users. RESULTS: We identified 4,349 patients, for which 1,356 were immunosuppressants users and 2,993 were non-users. Patients who used immunosuppressants were at increased odds of the primary endpoint of all-cause death, ICU admission and mechanical ventilation use (IPTW OR =1.32; 95% CI: 1.06-1.63), driven by higher odds of all-cause mortality (IPTW OR =1.63; 95% CI: 1.21-2.26). Patients who used corticosteroids (n=1,340) were at increased odds of the primary endpoint (IPTW OR =1.33; 95% CI: 1.07-1.64). CONCLUSIONS: Immunosuppressant use was associated with worse outcomes among COVID-19 patients. These findings support the latest guidelines from the CDC that people on immunosuppressants are at high risk of severe COVID-19 and that immunocompromised people may benefit from booster COVID-19 vaccinations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".