Updated Living Systematic Review and Meta-analysis of Controlled Trials of Mesenchymal Stromal Cells to Treat COVID-19: A Framework for Accelerated Synthesis of Trial Evidence for Rapid Approval—FASTER Approval
Bibliographic record
Abstract
BACKGROUND: Mesenchymal stromal cells (MSCs) may reduce mortality in patients with COVID-19; however, early evidence is based on few studies with marked interstudy heterogeneity. The second iteration of our living systematic review and meta-analysis evaluates a framework needed for synthesizing evidence from high-quality studies to accelerate consideration for approval. METHODS: A systematic search of the literature was conducted on November 15, 2021, to identify all English-language, full-text, and controlled clinical studies examining MSCs to treat COVID-19 (PROSPERO: CRD42021225431). FINDINGS: Eleven studies were identified (403 patients with severe and/or critical COVID-19, including 207 given MSCs and 196 controls). All 11 studies reported mortality and were pooled through random-effects meta-analysis. MSCs decreased relative risk of death at study endpoint (RR: 0.50 [95% CI, 0.34-0.75]) and RR of death at 28 days after treatment (0.19 [95% CI], 0.05-0.78) compared to controls. MSCs also decreased length of hospital stay (mean difference (MD: -3.97 days [95% CI, -6.09 to -1.85], n = 5 studies) and increased oxygenation levels at study endpoint compared to controls (MD: 105.62 mmHg O2 [95% CI, 73.9-137.3,], n = 3 studies). Only 2 of 11 studies reported on all International Society for Cellular Therapy (ISCT) criteria for MSC characterization. Included randomized controlled trials were found to have some concerns (n = 2) to low (n = 4) risk of bias (RoB), while all non-randomized studies were found to have moderate (n = 5) RoB. INTERPRETATION: Our updated living systematic review concludes that MSCs can likely reduce mortality in patients with severe or critical COVID-19. A master protocol based on our Faster Approval framework appears necessary to facilitate the more accelerated accumulation of high-quality evidence that would reduce RoB, improve consistency in product characterization, and standardize outcome reporting.
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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 | 0.036 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.049 | 0.010 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads agree on what is shown here.
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".