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/mm 3 ; 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/mm 3 , 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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".