Anticoagulation Use as an Independent Predictor of Mortality and Major Adverse Cardiovascular Events in Hospitalized COVID-19 Patients: A Multicenter Retrospective Analysis
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19) is associated with increased incidence of cardiac arrhythmias and thrombotic events. The adverse cardiovascular outcomes related to ambulatory anticoagulation (AC) therapy in COVID-19 patients are unknown. The goal of this study was to identify the effects of AC use in hospitalized COVID-19 patients. Methods: This is a multicenter, retrospective study that identified 2,801 hospitalized COVID-19 polymerase chain reaction (PCR)-positive patients admitted between March 2020 and July 2021. Of these, 375 (13.4%) were ambulatory AC users. Data were collected from the electronic health records of hospitalized patients. Mortality included in-hospital death and hospice referral. Major adverse cardiovascular events (MACEs) included acute heart failure (HF), myocardial infarction (MI), myocarditis, pulmonary embolism (PE), deep venous thrombosis (DVT), pericardial effusion, pericarditis, stroke, shock, and cardiac tamponade. A Chi-square test was used to analyze categorical variables, and multivariate logistic regression analysis was performed to account for comorbidities. Results: AC non-users exhibited a higher incidence of mortality than AC users (13.9% vs. 7.7%, P = 0.001). However, MACE incidence was higher in AC users than AC non-users (44.8% vs. 26.8%, P < 0.001). The higher MACE incidence was driven by higher rates of acute HF (8.3% vs. 2.5%, P < 0.001), MI (26.9% vs. 18.2%, P < 0.001), PE/DVT (16.3% vs. 2.7%, P < 0.001), pericardial effusion (1.6% vs. 0.5%, P = 0.025), and stroke (2.9% vs. 1.2%, P = 0.018). After multivariate logistic regression, MACE incidence remained higher (odds ratio (OR) = 1.61, 95% confidence interval (CI): 1.27 - 2.05, P < 0.001) and all-cause mortality rate lower (OR = 0.34, 95% CI: 0.23 - 0.52, P < 0.001) in AC users. Conclusions: Ambulatory AC use is associated with increased MACEs but decreased all-cause mortality in patients hospitalized with COVID-19. This study will help physicians identify patients at risk of cardiovascular mortality and direct management based on the identified risk.
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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 | 0.007 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".