Clinical outcomes after IL-6 blockade in patients with COVID-19 and HIV: a case series
Bibliographic record
Abstract
Abstract Background In hospitalized people with HIV (PWH) there is an increased risk of mortality from COVID-19 among hospitalized PWH as compared to HIV-negative individuals. Evidence suggests that tocilizumab—a humanized monoclonal interleukin (IL)-6 receptor inhibitor (IL-6ri) antibody—has a modest mortality benefit when combined with corticosteroids in select hospitalized COVID-19 patients who are severely ill. Data on clinical outcomes after tocilizumab use in PWH with severe COVID-19 are lacking. Case presentation We present a multinational case series of 18 PWH with COVID-19 who were treated with IL-6ri’s during the period from April to June 2020. Four patients received tocilizumab, six sarilumab, and eight received an undocumented IL-6ri. Of the 18 patients in the series, 4 (22%) had CD4 counts < 200 cells/mm3; 14 (82%) had a suppressed HIV viral load. Eight patients (44%), all admitted to ICU, were treated for secondary infection; 5 had a confirmed organism. Of the four patients with CD4 counts < 200 cells/mm3, three were treated for secondary infection, with 2 confirmed organisms. Overall outcomes were poor—12 patients (67%) were admitted to the ICU, 11 (61%) required mechanical ventilation, and 7 (39%) died. Conclusions In this case series of hospitalized PWH with COVID-19 and given IL-6ri prior to the common use of corticosteroids, there are reports of secondary or co-infection in severely ill patients. Comprehensive studies in PWH, particularly with CD4 counts < 200 cells, are warranted to assess infectious and other outcomes after IL-6ri use, particularly in the context of co-administered corticosteroids.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".