Coronary Artery Disease as an Independent Predictor of Cardiovascular Mortality in COVID-19 Patients
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19) is associated with increased risk of cardiovascular mortality. However, little is known about the combined effect of coronary artery disease (CAD) and COVID-19 on mortality. We aimed to investigate the incidence of cardiovascular and all-cause mortality in COVID-19 patients with CAD. Methods: This multicenter retrospective study identified 3,336 COVID-19 patients admitted between March and December 2020. Data points were manually reviewed in the patients' electronic health records. Multivariate logistic regression was used to assess whether CAD and its subtypes were associated with mortality. Results: This study shows that CAD was not an independent predictor of all-cause mortality (odds ratio (OR): 1.512, 95% confidence interval (CI): 0.1529 - 14.95, P = 0.723). However, there was a significant increase in cardiovascular mortality in patients with CAD compared to those without (OR: 6.89, 95% CI: 2.706 - 17.53, P < 0.001). There was no significant difference in all-cause mortality in patients with left main artery and left anterior descending artery disease (OR: 1.29, 95% CI: 0.80 - 2.08, P = 0.29). However, CAD patients with a history of interventions (e.g., coronary stenting or coronary artery bypass graft) showed increased mortality compared to those solely treated by medical management (OR: 1.93, 95% CI: 1.12 - 3.33, P = 0.017). Conclusions: CAD is associated with a higher incidence of cardiovascular mortality but not all-cause mortality in COVID-19 patients. Overall, this study will help clinicians identify characteristics of COVID-19 patients with increased risk of mortality in the setting of CAD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".