Risk of Adverse Outcomes in Hospitalized Patients With Autoimmune Disease and COVID-19: A Matched Cohort Study From New York City
Bibliographic record
Abstract
Objective. To examine the effect of autoimmune (AI) disease on the composite outcome of intensive care unit (ICU) admission, intubation, or death from COVID-19 in hospitalized patients. Methods. Retrospective cohort study of 186 patients hospitalized with COVID-19 between March 1, 2020, and April 15, 2020 at NewYork-Presbyterian Hospital/Columbia University Irving Medical Center. The cohort included 62 patients with AI disease and 124 age- and sex-matched controls. The primary outcome was a composite of ICU admission, intubation, and death, with secondary outcome as time to in-hospital death. Baseline demographics, comorbidities, medications, vital signs, and laboratory values were collected. Conditional logistic regression and Cox proportional hazards regression were used to assess the association between AI disease and clinical outcomes. Results. Patients with AI disease were more likely to have at least one comorbidity (87.1% vs 74.2%, P = 0.04), take chronic immunosuppressive medications (66.1% vs 4.0%, P < 0.01), and have had a solid organ transplant (16.1% vs 1.6%, P < 0.01). There were no significant differences in ICU admission (13.7% vs 19.4%, P = 0.32), intubation (13.7% vs 17.7%, P = 0.47), or death (16.1% vs 14.5%, P = 0.78). On multivariable analysis, patients with AI disease were not at an increased risk for a composite outcome of ICU admission, intubation, or death (OR adj 0.79, 95% CI 0.37–1.67). On Cox regression, AI disease was not associated with in-hospital mortality (HR adj 0.73, 95% CI 0.33–1.63). Conclusion. Among patients hospitalized with COVID-19, individuals with AI disease did not have an increased risk of a composite outcome of ICU admission, intubation, or death.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".