Abstract 10482: Renin-Angiotensin-Aldosterone System Inhibition is Associated with Reduced In-Hospital Mortality in Critically Ill Covid-19 Patients with Pre-Existing Hypertension
Bibliographic record
Abstract
Background: The virus responsible for COVID-19 enters human cells by binding angiotensin-converting enzyme 2. The influence of renin-angiotensin-aldosterone system (RAAS) inhibitors, including angiotensin-converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARBs), remains uncertain. Aim: To examine the role of ACEi / ARB exposure on outcomes in COVID-19 patients with pre-existing hypertension (HTN) admitted to intensive care units (ICU). Methods: The COVID-19 Critical Care Consortium is a prospective, observational cohort study of patients requiring ICU admission for active COVID-19 spanning 354 participating sites in 54 countries. Patients >18 years old with pre-existing HTN requiring antihypertensive therapy were analysed. Length of stay and in-hospital mortality to 90 days post ICU admission were analysed as time-to-event outcomes by multistate survival analysis, and the influence of ACEi / ARB use on the hazards of death and discharge by multi-state Cox proportional hazard modelling and sensitivity analysis. Results: From December 1, 2019 through December 30, 2020, 663 eligible patients were registered. Of these, 480 patients had received ACEi and / or ARB therapy (median age 65 years, 67% male) in the 2 weeks before ICU admission, while 183 had not (66 years, 61% male). Average lengths of ICU and general ward stays were longer in the ACEi / ARB than non-ACEi / ARB group (20.8 days and 6.5 days vs. 15.5 and 6.0 days, respectively). ACEi / ARB use was associated with a decreased hazard of death (HR, 0.69, 95% CI, 0.54 — 0.88) that persisted after adjusting for propensity scores (0.67, 0.53 — 0.86). Cumulative probabilities (unadjusted for baseline characteristics) for death and discharge post ICU admission are depicted in the figure for ACEi/ARB (red) and non-ACEi / ARB (blue) patients. Conclusions: In 663 critically ill COVID-19 patients with pre-existing HTN, RAAS inhibition pre-ICU admission was linked to reduced in-hospital mortality.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".