Trends in the Inpatient Burden of Coronary Artery Disease in Granulomatosis With Polyangiitis: A Study of a Large National Dataset
Bibliographic record
Abstract
OBJECTIVE: Cardiovascular (CV) diseases are serious comorbidities in patients with granulomatosis with polyangiitis (GPA). In a sample of patients hospitalized for GPA, we sought to examine trends in the burden of coronary artery disease (CAD) and its 2 serious manifestations, acute myocardial infarction (AMI) and heart failure (HF). METHODS: We used the National Inpatient Sample to conduct a retrospective cross-sectional analysis. Our sample consisted of hospitalizations for GPA between 2005 and 2014. We examined trends in the proportion of CAD, AMI, and HF in all hospitalizations with GPA compared to those without GPA. We used logistic regression adjusted for potential confounders and included interaction terms. RESULTS: Among a total of 103,453 GPA hospitalizations, 20,351 (19.7%) hospitalizations had a concurrent diagnosis of CAD. GPA with CAD was associated with overall lower burden of traditional CV risk factors compared to non-GPA with CAD, with the exception of chronic kidney disease (57% vs 21%). Over the 10-year study period, there were rising trends in the inpatient burden of CAD (16.6% in 2005 to 22.7% in 2014) and CAD with HF (4.3% in 2005 to 9.9% in 2014), but not AMI (1.2% in 2005 to 1.1% in 2014), in GPA hospitalizations compared to non-GPA controls. CONCLUSION: In this national sample of GPA hospitalizations, we found that the burden of CAD and CAD with HF was on the rise over the 10-year period compared to non-GPA; however, it was not the case for AMI.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".