A multicenter, open-label, randomized, proof-of-concept phase II clinical trial to assess the efficacy and safety of icatibant in patients infected with SARS-CoV-2 (COVID-19) and admitted to hospital units without invasive mechanical ventilation: study protocol (ICAT-COVID)
Bibliographic record
Abstract
BACKGROUND: COVID-19 has quickly become a global pandemic with a substantial number of deaths and is a considerable burden for healthcare systems worldwide. Although most cases are paucisymptomatic and limited to the viral infection-related symptoms, some patients evolve to a second phase, with an impaired inflammatory response (cytokine storm) that may lead to acute respiratory distress syndrome and death. This is thought to be caused by increased bradykinin synthesis. METHODS: ICAT-COVID is a multicenter, randomized, open-label, proof-of-concept phase II clinical trial assessing the clinical efficacy and safety of adding icatibant to the standard of care in patients hospitalized with COVID-19 without invasive mechanical ventilation. Patients hospitalized with a confirmed COVID-19 pneumonia diagnosis (RT-PCR or antigen test ≤ 10 days prior to randomization, and radiographic evidence of pulmonary infiltrates), rated "4" or "5" on the WHO's clinical status scale, are eligible. Patients will be randomized on a 1:1 ratio to either standard of care-plus-icatibant (experimental group) or to standard of care alone (control group). The experimental group will receive 30 mg of icatibant subcutaneously 3 times a day for 3 days (for a total of 9 doses). The expected sample size is 120 patients (60 per group) from 2 sites in Spain. Primary outcomes are the efficacy and safety of Icatibant. The main efficacy outcome is the number of patients reaching grades "2" or "1" on the WHO scale within 10 days of starting treatment. Secondary outcomes include "long-term efficacy": number of patients discharged who do not present COVID-19-related relapse or comorbidity up until 28 days after discharge, and mortality. DISCUSSION: Icatibant, a bradykinin type 2 receptor antagonist with proven effectiveness and safety against hereditary angioedema attacks, may be beneficial for COVID-19 patients by inhibiting bradykinin's action on endothelial cells and by inhibiting the SARS-CoV-2 M protease. Our working hypothesis is that treatment with standard of care-plus-icatibant is effective and safe to treat patients infected with SARS-CoV-2 admitted to hospital for pneumonia without invasive mechanical ventilation. TRIAL REGISTRATION: EudraCT 2020-002166-13. CLINICALTRIALS: gov NCT04978051.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".