Ion channel inhibition with amiodarone or verapamil in symptomatic hospitalized nonintensive-care COVID-19 patients: The ReCOVery-SIRIO randomized trial
Bibliographic record
Abstract
BACKGROUND: Ion channel inhibition may offer protection against coronavirus disease 2019 (COVID-19). Inflammation and reduced platelet count occur during COVID-19 but precise quantification of risk thresholds is unclear. The Recov ery-SIRIO study aimed to assess clinical effects of amiodarone and verapamil and to relate patient phenotypes to outcomes. METHODS: RECOVERY-SIRIO is a multicenter open-label 1:1:1 investigator-initiated randomized trial with blinded event adjudication. A sample of 804 symptomatic hospitalized nonintensive-care COVID-19 patients, follow-up for 28 days was initially planned. RESULTS: The trial was stopped when a total of 215 patients had been randomized to amiodarone (n = 71), verapamil (n = 72) or standard care alone (n = 72). At 15 days, the hazard ratio (hazard ratio [HR], 95% confidence interval [CI]) for clinical improvement was 0.77 (0.52-1.14) with amiodarone and 0.97 (0.81-1.17) with verapamil as compared to usual care. Clinically relevant associations were found between mortality or lack of clinical improvement and higher peak C-reactive protein (CRP) levels or nadir platelet count at 7, 10 and 15 days. Mortality rate increased by 73% every 5 mg/dL increment in peak CRP (HR 1.73, 95% CI 1.27-2.37) and was two-fold higher for every decrement of 100 units in nadir platelet count (HR 2.19, 95% CI 1.37-3.51). By cluster analysis, thresholds of 5 mg/dL for peak CRP and 187 × 103/mcL for nadir platelet count identified the phenogroup at greatest risk of dying. CONCLUSIONS: In this randomized trial, neither amiodarone nor verapamil were found to significantly accelerate short-term clinical improvement. Peak CRP and nadir platelet counts were associated with increased mortality both in isolation and by cluster analysis.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".