Worldwide Prevalence of Hypertension in Patients with COVID-19: A Meta-analysis and Systematic Review
Bibliographic record
Abstract
Background: This systematic review and meta-analysis aimed to assess the ratio of patients with hypertension for whom coronavirus disease 2019 (COVID-19) has been diagnosed in the world. In addition, the effect of COVID-19 on the outcomes of hypertension was evaluated. Methods: To find articles published by July 2020, a comprehensive systematic search was carried out in five electronic databases, including PubMed, Scopus, Embase, and Web of Science. The meta-analysis entailed all relevant articles on the clinical and epidemiological features of patients with COVID-19. Two researchers independently reviewed the eligible post-selection studies, and finally, the discrepancies between the opinions of the two researchers were resolved by a third arbitrator. Two researchers independently examined the risk of bias using the Newcastle-Ottawa Scale. Results: The pooled prevalence of high blood pressure in patients hospitalized with COVID-19 worldwide was obtained as 31% (95% CI: 23 - 38%). The changes for hypertension prevalence in different studies included in the meta-analysis varied from 2 to 64%. Moreover, the results of subgroups analysis based on different countries demonstrated that the prevalence of hypertension in patients with COVID-19 in China and other countries was 29% (95% CI: 24 - 34%) and 32% (95% CI: 19 - 46%), respectively. Conclusions: The evidence revealed that a health condition that commonly accompanies and affects the outcomes of COVID-19 is hypertension. Therefore, COVID-19 patients with hypertension should be given priority and benefit from a preventive, therapeutic approach. Furthermore, essential training should be provided by health policymakers.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.045 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".