Mortality rates among hospitalized patients with COVID-19 treated with convalescent plasma A Systematic review and meta-analysis
Bibliographic record
Abstract
Abstract IMPORTANCE Many hospitalized patients with COVID-19 have been treated with convalescent plasma. However, it is uncertain whether this therapy lowers mortality and if so, if the mortality benefit is larger among specific subgroups, such as recipients of plasma with high antibody content and patients treated early in the disease course. OBJECTIVE To examine the association of COVID-19 convalescent plasma transfusion with mortality and the differences between subgroups in hospitalized patients with COVID-19. DATA SOURCES On October 26, 2022, a systematic search was performed for clinical studies of COVID-19 convalescent plasma in the literature. STUDY SELECTION Randomized clinical trials and matched cohort studies investigating COVID-19 convalescent plasma transfusion compared with standard of care treatment or placebo among hospitalized patients with confirmed COVID-19 were included. The electronic search yielded 3,841 unique records, of which 744 were considered for full-text screening. The selection process was performed independently by a panel of five reviewers. DATA EXTRACTION AND SYNTHESIS The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Data were extracted by 5 independent reviewers in duplicate and pooled using inverse-variance random-effects model. MAIN OUTCOMES AND MEASURES Prespecified end point was all-cause mortality during hospitalization. RESULTS Thirty-nine randomized clinical trials enrolling 21,529 participants and 70 matched cohort studies enrolling 50,160 participants were included in the systematic review. Separate meta-analyses demonstrated that transfusion of COVID-19 convalescent plasma was associated with a significant decrease in mortality compared with the control cohort for both randomized clinical trials (odds ratio (OR), 0.87 [95% CI, 0.76-1.00]) and matched cohort studies (OR, 0.77 [95% CI, 0.64-0.94]). Meta-analysis of subgroups revealed two important findings. First, treatment with convalescent plasma containing high antibody levels was associated with a decrease in mortality compared to convalescent plasma containing low antibody levels (OR, 0.85 [95% CI, 0.73 to 0.99]). Second, earlier treatment with COVID-19 convalescent plasma was associated with a significant decrease in mortality compared with the later treatment cohort (OR, 0.63 [95% CI, 0.48 to 0.82]). CONCLUSIONS AND RELEVANCE COVID-19 convalescent plasma use was associated with a 13% reduced risk in mortality, implying a mortality benefit for hospitalized patients with COVID-19, particularly those treated with convalescent plasma containing high antibody levels treated earlier in the disease course. Key Points Question What is the evidence regarding the potential mortality benefit associated with transfusion of convalescent plasma in hospitalized patients with COVID-19? Findings In this meta-analysis of 39 randomized clinical trials enrolling 21,529 participants and 70 matched cohort studies enrolling 50,160 participants, transfusion of convalescent plasma was associated with a 13% mortality benefit. Subgroup analyses revealed that patients treated with plasma containing higher levels of antibodies and patients treated earlier in the course of the disease had a greater mortality benefit associated with COVID-19 convalescent plasma transfusion. Meaning These findings suggest that transfusion of COVID-19 convalescent plasma is associated with a mortality benefit for hospitalized patients, particularly those treated earlier in the disease course.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".