Abstract 13656: Outcomes of Pre-existing Cardiovascular Disease Amongst COVID-19 Patients
Bibliographic record
Abstract
Introduction: COVID-19 has multiorgan involvement and it is believed that outcomes are poor amongst patients with hypertension (HTN) and pre-existing cardiovascular disorders (CVD). Hypothesis: The objective of this meta-analysis is to evaluate outcomes [mortality and invasive mechanical ventilation (IMV) utilization] of COVID-19 in patients with pre-existing HTN and CVD. Methods: English full-text-observational studies having data on epidemiological characteristics of patients with COVID-19 were identified searching PubMed using MeSH-terms from December 1, 2019, to April 30, 2020. Studies having CVD or HTN as one of the pre-existing comorbidities and described outcomes including IMV and mortality were selected with a consensus of three reviewers. 29 studies met these criteria. Following MOOSE protocol, data on patients’ characteristics especially age and history of CVD, HTN, IMV, and mortality were pooled using a random-effects model. The pooled prevalence of CVD and HTN were calculated. Meta-regression was performed and correlation coefficient (r) and odds ratio (OR) were estimated to evaluate the effects of pre-existing CVD and HTN on outcomes of COVID-19 patients. Results: Out of 29 studies with COVID-19 epidemiology data, 21, 17, 18 and 19 studies have details on mortality, IMV, HTN, and pre-existing CVD, respectively. Pooled prevalence of HTN was 28.2% [95%CI:22.1%-35.1%; p<0.001; 4858/11626 patients; Heterogeneity (I 2 ):97.8%] and pre-existing CVD was 12.2% [8.9%-16.6%; p<0.001; 2044/11664 patients; I 2 :96.8%]. In age-adjusted meta-regression analysis, IMV was significantly higher among COVID-19 patients with pre-existing CVD [r:0.28; OR:1.3 (1.1-1.6); I 2 :89.7%; p=0.0028] without significant association with HTN [r:0.01; OR:1.0 (0.9-1.1); I 2 :95.9%; p=0.8161]. HTN [r:0.001; OR:1.0 (0.9-1.1); I 2 :96%; p=0.9685] and pre-existing CVD [r:-0.01; OR:0.9 (0.9-1.1); I 2 :96.3%; p=0.8772] had no significant association with mortality amongst COVID-19 patients. Conclusion: In the age-adjusted analysis, though we identified pre-existing CVD as a risk factor for higher utilization of IMV, pre-existing CVD and HTN had no independent role in increasing 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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.018 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".