Study protocol for a multicentre, prospective cohort study of the association of angiotensin II type 1 receptor blockers on outcomes of coronavirus infection
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 epidemic grows and there are clinical trials of antivirals. There is an opportunity to complement these trials with investigation of angiotensin II type 1 receptor blockers (ARBs) because an ARB (losartan) was effective in murine influenza pneumonia. METHODS AND ANALYSIS: Our innovative design includes: ARBs; alignment with the WHO Ordinal Scale (primary endpoint) to align with other COVID-19 trials; joint longitudinal analysis; and predictive biomarkers (angiotensins I, 1-7, II and ACE1 and ACE2). Our hypothesis is: ARBs decrease the need for hospitalisation, severity (need for ventilation, vasopressors, extracorporeal membrane oxygenation or renal replacement therapy) or mortality of hospitalised COVID-19 infected adults. Our two-pronged multicentre pragmatic observational cohort study examines safety and effectiveness of ARBs in (1) hospitalised adult patients with COVID-19 and (2) out-patients already on or not on ARBs. The primary outcome will be evaluated by ordinal logistic regression and main secondary outcomes by both joint longitudinal modelling analyses. We will compare rates of hospitalisation of ARB-exposed versus not ARB-exposed patients. We will also determine whether continuing ARBs or not decreases the primary outcome. Based on published COVID-19 cohorts, assuming 15% of patients are ARB-exposed, a total sample size of 497 patients can detect a proportional OR of 0.5 (alpha=0.05, 80% power) comparing WHO scale of ARB-exposed versus non-ARB-exposed patients. ETHICS AND DISSEMINATION: This study has core institution approval (UBC Providence Healthcare Research Ethics Board) and site institution approvals (Health Research Ethics Board, University of Alberta; Comite d'etique de la recerche, CHU Sainte Justine (for McGill University and University of Sherbrook); Conjoint Health Research Ethics Board, University of Calgary; Queen's University Health Sciences & Affiliated Hospitals Research Ethics Board; Research Ethics Board, Sunnybrook Health Sciences Centre; Veritas Independent Research Board (for Humber River Hospital); Mount Sinai Hospital Research Ethics Board; Unity Health Toronto Research Ethics Board, St. Michael's Hospital). Results will be disseminated by peer-review publication and social media releases. TRIAL REGISTRATION NUMBER: NCT04510623.
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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 | 0.028 | 0.042 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.128 | 0.029 |
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".