Impact of Chronic Obstructive Pulmonary Disease Burden on Patients With Atrial Fibrillation: A Nationwide Study
Bibliographic record
Abstract
Background: Chronic obstructive pulmonary disease (COPD) and atrial fibrillation (Afib) are frequently comorbid, with COPD patients exhibiting a higher risk of Afib-related hospitalizations. This study investigated the relationship between COPD and Afib, focusing on 30-day readmission rates and outcomes. Methods: We conducted a retrospective cohort study using the Nationwide Readmissions Database (NRD) from 2016 to 2020. We included adult patients (≥ 18 years) with a primary diagnosis of Afib while excluding those with December discharges to ensure a complete 30-day follow-up. We compared patients with and without COPD, analyzing 30-day readmission rates, length of stay (LOS), hospital costs, in-hospital mortality, and associated factors using multivariable Cox and logistic regression models. Results: A total of 1,064,982 patients admitted with Afib were included, of which 873,070 had no COPD, and 191,912 had it. COPD patients were older (73.19 vs. 70.82 years), had a shorter LOS (coefficient = -0.05, P = 0.002, 95% confidence interval (CI): -0.08 to -0.02), and had a higher comorbidity burden (Elixhauser comorbidity index: 5.13 vs. 3.43, P < 0.0001). The 30-day readmission rate was significantly higher in the COPD group (16.0% vs. 9.0%, P < 0.001). Logistic regression revealed that COPD increased the odds of readmission (odds ratio: 1.35, 95% CI: 1.32 to 1.39, P < 0.001). Conclusion: COPD is a significant risk factor for 30-day readmission and in-hospital mortality among Afib patients, underscoring the need for integrated approaches targeting both diseases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".