Risk factors for severe outcomes of coronavirus disease 2019 through the waves of the pandemic: Comparing patients with and without solid organ transplantation
Bibliographic record
Abstract
BACKGROUND: While coronavirus disease 2019 (COVID-19) is no longer a public health emergency, certain patients remain at risk of severe outcomes. To better understand changing risk profiles, we studied the risk factors for patients with and without solid organ transplantation (SOT) through the various waves of the pandemic. METHODS: Using the National COVID Cohort Collaborative we studied a cohort of adult patients testing positive for COVID-19 between January 1, 2020, and May 2, 2022. We separated the data into waves of COVID-19 as defined by the Centers for Disease Control. In our primary outcome, we used multivariable survival analysis to look at various risk factors for hospitalization in those with and without SOT. RESULTS: A total of 3,570,032 patients were captured. We found an overall risk attenuation of adverse COVID-19-associated outcomes over time. In both non-SOT and SOT populations, diabetes, chronic kidney disease, and congestive heart failure were risk factors for hospitalization. For SOT specifically, longer time periods between transplant and COVID-19 were protective and age was a risk factor. Notably, asthma was not a risk factor for major adverse renal cardiovascular events, hospitalization, or mortality in either group. CONCLUSIONS: Our study provides a longitudinal view of the risks associated with adverse COVID-related outcomes amongst SOT and non-SOT patients, and how these risk factors evolved over time. Our work will help inform providers and policymakers to better target high-risk patients.
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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) | 0.000 | 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".