Immune-Based Therapy for Hospitalized Patients With COVID-19 and Risk of Secondary Infections: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background Immune-based therapies are standard-of-care treatment for coronavirus disease 2019 (COVID-19) patients requiring hospitalization. However, safety concerns related to the potential risk of secondary infections may limit their use. Methods We searched OVID Medline, Ovid EMBASE, SCOPUS, Cochrane Library, clinicaltrials.gov, and PROSPERO in October 2020 and updated the search in November 2021. We included randomized controlled trials (RCTs). Pairs of reviewers screened abstracts and full studies and extracted data in an independent manner. We used RevMan to conduct a meta-analysis using random-effects models to calculate the pooled risk ratio (RR) and 95% CI for the incidence of infection. Statistical heterogeneity was determined using the I2 statistic. We assessed risk of bias for all studies and rated the certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation methodology. We conducted a meta-regression using the R package to meta-explore whether age, sex, and invasive mechanical ventilation modified risk of infection with immune-based therapies. The protocol is registered with PROSPERO (CRD42021229406). Results This was a meta-analysis of 37 RCTs including 32 621 participants (mean age, 60 years; 64% male). The use of immune-based therapy for COVID-19 conferred mild protection for the occurrence of secondary infections (711/15 721, 4.5%, vs 616/16 900, 3.6%; RR, 0.82; 95% CI, 0.71–0.95; P = .008; I2 = 28%). A subgroup analysis did not identify any subgroup effect by type of immune-based therapies (P = .85). A meta-regression revealed no impact of age, sex, or mechanical ventilation on the effect of immune-based therapies on risk of infection. Conclusions We identified moderate-certainty evidence that the use of immune-based therapies in COVID-19 requiring hospitalization does not increase the risk of secondary infections.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.051 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".