Factors associated with severity of COVID-19 disease in a multicenter cohort of people with HIV in the United States, March-December 2020
Bibliographic record
Abstract
BACKGROUND: Understanding the spectrum of SARS-CoV-2 infection and COVID-19 disease in people with HIV (PWH) is critical to provide clinical guidance and implement risk-reduction strategies. OBJECTIVE: To characterize COVID-19 in PWH in the United States and identify predictors of disease severity. DESIGN: Observational cohort study. SETTING: Geographically diverse clinical sites in the CFAR Network of Integrated Clinical Systems (CNICS). PARTICIPANTS: Adults receiving HIV care through December 31, 2020. MEASUREMENTS: COVID-19 cases and severity (hospitalization, intensive care, death). RESULTS: (aRR 2.68; 95%CI 1.93-3.71; P<.001) or lowest recorded CD4 count <200 (aRR 1.67; 95%CI 1.18-2.36; P<.005) had greater risk of hospitalization. HIV viral load suppression and antiretroviral therapy (ART) status were not associated with hospitalization, although the majority of PWH were suppressed (86%). Black PWH were 51% more likely to be hospitalized with COVID-19 compared to other racial/ethnic groups (aRR 1.51; 95%CI 1.04-2.19, P=.03). Chronic kidney disease (CKD), chronic obstructive pulmonary disease, diabetes, hypertension, obesity, and increased cardiovascular and hepatic fibrosis risk scores were associated with higher risk of hospitalization. PWH who were older, not on ART, with current CD4 <350, diabetes, and CKD were overrepresented amongst PWH who required intubation or died. LIMITATIONS: Unable to compare directly to persons without HIV; underestimate of total COVID-19 cases. CONCLUSIONS: , low CD4/CD8 ratio, and history of CD4 <200, have a clear excess risk of severe COVID-19, after accounting for comorbidities also associated with severe outcomes. PWH with these risk factors should be prioritized for COVID-19 vaccination, early treatment, and monitored closely for worsening illness.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".