Nirmatrelvir/Ritonavir Regimen for Mild/Moderately Severe COVID-19: A Rapid Review With Meta-Analysis and Trial Sequential Analysis
Bibliographic record
Abstract
BACKGROUND The efficacy, effectiveness, and safety of the approved nirmatrelvir/ritonavir regimen for treatment of laboratory-confirmed mild/moderately severe COVID-19 remains unclear. METHODS We systematically identified randomized controlled trials (RCTs) and real-world studies (RWS; observational studies) of the efficacy/effectiveness and/or safety of the approved nirmatrelvir/ritonavir regimen for COVID-19. We pooled appropriate data (adjusted estimates for RWS) using an inverse variance, random-effects model. We calculated statistical heterogeneity using the I2 statistic. Results are presented as relative risk (RR) with associated 95% CI. We further assessed risk of bias/study quality and conducted trial sequential analysis of the evidence from RCTs. RESULTS We included 4 RCTs (4,070 persons) and 16 RWS (1,925,047 persons) of adults (aged ≥18 years). One and 3 RCTs were of low and unclear risk of bias, respectively. The RWS were of good quality. Nirmatrelvir/ritonavir significantly decreased COVID-19 hospitalization compared with placebo/no treatment (RR = 0.17; 95% CI, 0.10-0.31; I2 = 77.2%; 2 RCTs, 3,542 persons), but there was no significant difference for decrease of worsening severity (RR = 0.82; 95% CI, 0.66-1.01; I2 = 47.5%; 3 RCTs, 1,824 persons), viral clearance (RR = 1.19; 95% CI, 0.93-1.51; I2 = 82%; 2 RCTs, 528 persons), adverse events (RR = 1.41; 95% CI, 0.92-2.14; I2 = 70.6%; 4 RCTs, 4,070 persons), serious adverse events (RR = 0.82; 95% CI, 0.41-1.62; I2 = 0%; 3 RCTs, 3,806 persons), and all-cause mortality (RR = 0.27; 95% CI, 0.04-1.70; I2 = 49.9%; 3 RCTs, 3,806 persons), although trial sequential analysis suggested that the current total sample sizes for these outcomes were not large enough for conclusions to be drawn. Real-world studies also showed significantly decreased COVID-19 hospitalization (RR = 0.48; 95% CI, 0.37-0.60; I2 = 95.0%; 11 RWS, 1,421,398 persons) and all-cause mortality (RR = 0.24; 95% CI, 0.14-0.34; I2 = 65%; 7 RWS, 286,131 persons) for nirmatrelvir/ritonavir compared with no treatment. CONCLUSIONS Nirmatrelvir/ritonavir appears to be promising for preventing hospitalization and potentially decreasing all-cause mortality for persons with mild/moderately severe COVID-19, but the evidence is weak. More studies are needed.
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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 | 0.030 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.044 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".