Coronavirus Disease 2019 and Hypertension: How Anti-hypertensive Drugs Affect COVID-19 Medications and Vice Versa
Bibliographic record
Abstract
BACKGROUND: As a medical problem, hypertension is one of the most common disorders in cardiovascular disease. High blood pressure has been identified as one of the most familiar risk factors for the ongoing COVID-19 pandemic. We planned to explore the possible interactions between anti-hypertensive agents and drugs targeting SARS-CoV-2 with broad investigations of these medications' mechanism of action and adverse effects. METHODS: Two co-authors searched the electronic databases (PubMed, Scopus, and Google Scholar) to collect papers relevant to the subject. The keywords searched were angiotensin-converting enzyme inhibitors (ACEI), angiotensin-II receptor blockers (ARBs), sympatholytic drugs (alpha-1 blockers, beta-blockers), vasodilators (calcium channel blockers, nitrates, and hydralazine), diuretics, chloroquine, hydroxychloroquine, lopinavir/ritonavir, remdesivir, favipiravir, interferons, azithromycin, anti-cytokine agents, glucocorticoids, anticoagulant agents, nitric oxide, and epoprostenol. RESULTS: QT prolongation, arrhythmia, hypokalemia, hypertriglyceridemia are the most dangerous adverse effects in the patients on COVID-19 medications and anti-hypertensive drugs. CONCLUSION: This review emphasized the importance of the potential interaction between drugs used against COVID-19 and anti-hypertensive agents. Therefore, caution must be exercised when these medications are being used simultaneously.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".