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 COVID-19 in people with HIV (PWH) is critical to provide clinical guidance and risk reduction strategies. SETTING: Centers for AIDS Research Network of Integrated Clinic System, a US multisite clinical cohort of PWH in care. METHODS: We identified COVID-19 cases and severity (hospitalization, intensive care, and death) in a large, diverse HIV cohort during March 1, 2020-December 31, 2020. We determined predictors and relative risks of hospitalization among PWH with COVID-19, adjusted for disease risk scores. RESULTS: Of 16,056 PWH in care, 649 were diagnosed with COVID-19 between March and December 2020. Case fatality was 2%; 106 (16.3%) were hospitalized, and 12 died. PWH with current CD4 count <350 cells/mm 3 [aRR 2.68; 95% confidence interval (CI): 1.93 to 3.71; P < 0.001] or lowest recorded CD4 count <200 cells/mm 3 (aRR 1.67; 95% CI: 1.18 to 2.36; P < 0.005) had greater risks of hospitalization. HIV viral load and antiretroviral therapy status were not associated with hospitalization, although most of the PWH were suppressed (86%). Black PWH were 51% more likely to be hospitalized with COVID-19 compared with other racial/ethnic groups (aRR 1.51; 95% CI: 1.04 to 2.19; P = 0.03). Chronic kidney disease, chronic obstructive pulmonary disease, diabetes, hypertension, obesity, and increased cardiovascular and hepatic fibrosis risk scores were associated with higher hospitalization risk. PWH who were older, not on antiretroviral therapy, and with current CD4 count <350 cells/mm 3 , diabetes, and chronic kidney disease were overrepresented among PWH who required intubation or died. CONCLUSIONS: PWH with CD4 count <350 cells/mm 3 , and a history of CD4 count <200 cells/mm 3 , have a clear excess risk of severe COVID-19, accounting for comorbidities associated with severe outcomes. PWH with these risk factors should be prioritized for COVID-19 vaccination and 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".