The use of renin–angiotensin–aldosterone system (RAAS) inhibitors is associated with a lower risk of mortality in hypertensive COVID‐19 patients: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Renin–angiotensin–aldosterone system (RAAS) inhibitors, including angiotensin‐converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) are one of the most prescribed antihypertensive medications. Previous studies showed RAAS inhibitors increase the expression of ACE2, a cellular receptor for severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), which provokes a concern that the use of ACEI and ARB in hypertensive individuals might lead to increased mortality and severity of coronavirus disease 2019 (COVID‐19). To further investigate the effects of ACEI/ARB on COVID‐19 patients, we systematically reviewed relevant studies that met predetermined inclusion criteria in search of PubMed, Embase, Cochrane Library databases, medRxiv, and bioRxiv. The search strategy included clinical data published through October 12, 2020. Twenty‐six studies involving 8104 hypertensive patients in ACEI/ARB‐treated group and 8203 hypertensive patients in non‐ACEI/ARB‐treated group were analyzed. Random‐effects meta‐analysis showed ACEI/ARB treatment was significantly associated with a lower risk of mortality in hypertensive COVID‐19 patients (odds ratio [OR] = 0.624, 95% confidence interval [CI] = 0.457–0.852, p = .003, I 2 = 74.3%). Meta‐regression analysis showed that age, gender, study site, Newcastle–Ottawa Scale scores, comorbidities of diabetes, coronary artery disease, chronic kidney disease, or cancer has no significant modulating effect of ACEI/ARB treatment on the mortality of hypertensive COVID‐19 patients (all p > .1). In addition, the ACEI/ARB treatment was associated with a lower risk of ventilatory support (OR = 0.682, 95% CI = 0.475–1.978, p = .037, I 2 = 0.0%). In conclusion, these results suggest that ACEI/ARB medications should not be discontinued for hypertensive patients in the context of COVID‐19 pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.315 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.020 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".