Effect of RAAS Inhibitors in People with COVID-19: An Independent Participant Data Meta-Analysis
Bibliographic record
Abstract
Background: SARS-CoV-2 infection is mediated by angiotensin-converting enzyme 2 receptors. Trials in different settings were initiated to test whether renin-angiotensin-aldosterone system inhibitors (RAASi) improved clinical outcomes in people with COVID-19. Methods: We performed an individual participant data (IPD) meta-analysis of randomized controlled trials (RCTs) evaluating the effects of RAASi in people with COVID-19. The primary outcome of WHO Clinical Progression scale over 28 days was evaluated using a linear mixed model. All-cause mortality was evaluated with Cox proportional hazards. Results: Six trials evaluating losartan, telmisartan, and candesartan were included, with sample sizes ranging from 12 to 787. Trials were conducted in the USA, Australia, and India; four were completed as planned, and 5 were placebo controlled. The 1,130 participants had a median age of 50 years and 38% were female. The majority of the cohort was Asian (73%) and Caucasian (16%). The median BMI was 25 kg/m2, 29% had hypertension, 21% had diabetes, and 17% had a smoking history. The baseline mean WHO score was 3.6 in the RAASi arm and 3.5 in the control arm. Most participants resolved with a mean WHO score of 1.2 in the RAASi arm and 1.2 in the control arm at day 28, with no significant difference in WHO scale progression (Figure 1, p=0.29). RAASi did not affect overall mortality (26 deaths, event rate 0.04 and 18 deaths, event rate 0.03 in the RAASi and control arms respectively: HR 1.42 [0.77-2.64], p=0.261). Conclusion: This IPD meta-analysis presents the largest randomized report of the effects of commencing RAASi for acute COVID-19. There is no evidence supporting a benefit for RAASi in COVID-19 patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.063 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".