Abstract 12802: Impact of Atrial Fibrillation on Percutaneous Coronary Intervention With Orbital Atherectomy
Bibliographic record
Abstract
Introduction: Patients with atrial fibrillation risk various complications following cardiovascular procedures. As there is a lack of data on the disparities in outcomes in atrial fibrillation patients undergoing orbital atherectomy (followed by the percutaneous intervention (PCI)), we aim to bridge this knowledge gap via our study. Methods: We queried cases of PCI between 2016-2020 via the National Inpatient Sample. Patients with a code for orbital atherectomy (followed by PCI) were retained as per past studies. We compared patient characteristics between patients with and without atrial fibrillation, and the adjusted odds ratios (aOR) of various complications were studied via logistic regression analysis. Results: Our analysis found 20710 patients (ages 18 and more) who underwent orbital atherectomy between 2016-2020 in the United States, which included 5250(25.4%) of cases with atrial fibrillation. Compared to cases without atrial fibrillation, patients with atrial fibrillation undergoing orbital atherectomy showed higher odds of various complications such as acute ischemic stroke (AIS) (aOR 1.507, p<0.01), sepsis (aOR 1.389, p<0.01), pericardial effusion (aOR 1.931, p<0.01), major bleeding (aOR 1.423, p<0.01), cardiogenic shock (aOR 1.258, p<0.01), acute kidney injury (AKI) (aOR 1.438, p<0.01), and mortality (aOR 1.298, p<0.01). While fewer cases of coronary artery dissections were noted among patients with atrial fibrillation (2.0% vs. 2.3%), the results were not statistically significant (aOR 0.862, p=0.256). Conclusions: Atrial fibrillation predisposed patients undergoing orbital atherectomy to poorer outcomes, including AKI, AIS, sepsis, pericardial effusion, major bleeding, cardiogenic shock, and death. Physicians must therefore address this issue via proper screening for cardiac arrhythmias pre- and post-procedure and maintain a close follow-up in at-risk individuals.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".