Electronic health record data shows immunosuppressive conditions are associated with poor outcomes in patients hospitalized for COVID-19
Bibliographic record
Abstract
Abstract We identified 10,713 adults hospitalized for COVID-19 from 03/2020 to 05/2022 in electronic health record data from Northwestern Medicine. Based on extracted diagnosis codes, we identified 214 of these adults with primary immunodeficiency (PI), 669 with history of solid organ transplant, and 183 with HIV. Assessed COVID-19 outcomes included mortality, a seven-point ordinal severity scale (1 = Death) based on maximum oxygen requirements during care, and hospitalization length. Patients with PI had a significantly higher mean hospitalization length of 11.8 days and a significantly lower mean maximum severity of 3.3 compared to 7.8 days and 3.9 in immunocompetent patients when controlling for obesity and T2DM. These effects were not significant when controlling for age. Patients with transplant had a significantly higher mean hospitalization length of 15.8 days when controlling for obesity, T2DM, and age, and a significantly lower mean maximum severity of 3.2 when controlling for obesity. Significantly higher mortalities were observed in PI and transplant (RR = 1.5, 95% CI 1.2 to 2.0; RR = 1.5, 95% CI 1.3 to 1.7), but these effects were not significant when controlling for age. There were no significant differences in any outcomes for patients with HIV. Our results suggest patients with PI or transplant hospitalized for COVID-19 may have worse clinical outcomes compared to immunocompetent patients. Meanwhile, patients with HIV had similar outcomes to immunocompetent patients.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.003 | 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".