The influence of ACE inhibitors and ARBs on hospital length of stay and survival in people with COVID-19
Bibliographic record
Abstract
OBJECTIVE: During the COVID-19 pandemic the continuation or cessation of angiotensin-converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARBs) has been contentious. Mechanisms have been proposed for both beneficial and detrimental effects. Recent studies have focused on mortality with no literature having examined length of hospital stay. The aim of this study was to determine the influence of ACEi and ARBs on COVID-19 mortality and length of hospital stay. METHODS: COPE (COVID-19 in Older People) is a multicenter observational study including adults of all ages admitted with either laboratory or clinically confirmed COVID-19. Routinely generated hospital data were collected. Primary outcome: mortality; secondary outcomes: Day-7 mortality and length of hospital stay. A mixed-effects multivariable Cox's proportional baseline hazards model and logistic equivalent were used. RESULTS: 1371 patients were included from eleven centres between 27th February to 25th April 2020. Median age was 74 years [IQR 61-83]. 28.6% of patients were taking an ACEi or ARB. There was no effect of ACEi or ARB on inpatient mortality (aHR = 0.85, 95%CI 0.65-1.11). For those prescribed an ACEi or ARB, hospital stay was significantly reduced (aHR = 1.25, 95%CI 1.02-1.54, p = 0.03) and in those with hypertension the effect was stronger (aHR = 1.39, 95%CI 1.09-1.77, p = 0.007). CONCLUSIONS: Patients and clinicians can be reassured that prescription of an ACEi or ARB at the time of COVID-19 diagnosis is not harmful. The benefit of prescription of an ACEi or ARB in reducing hospital stay is a new finding.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".