Temporal trends and predictors of time to coronary angiography following non-ST-elevation acute coronary syndrome in the USA
Bibliographic record
Abstract
OBJECTIVE: This study aims to investigate the temporal trends in utilization of invasive coronary angiography (CA) at different time points and changing profiles of patients undergoing CA following non-ST-elevation acute coronary syndrome (NSTEACS). We also describe the association between time to CA and in-hospital clinical outcomes. PATIENTS AND METHODS: We queried the National Inpatient Sample to identify all admissions with a primary diagnosis of NSTEACS from 2004 to 2014. Patients were stratified into early (day 0, 1), intermediate (day 2) and late strategy (day≥3) according to time to CA. Multivariable logistic regression was used to investigate the association between time to CA and in-hospital mortality, major bleeding, stroke and Major Adverse Cardiac and Cerebrovascular Events. RESULTS: A total of 4 380 827 records were identified with a diagnosis of NSTEACS, out of which 57.5% received CA. The proportion of patients undergoing early CA increased from 65.6 to 72.6%, whereas late CA commensurately declined from 19.6 to 13.5%. Patients receiving early CA were younger (age: 64 vs. 70 years), more likely to be male (63.7 vs. 55.3%) and of Caucasian ethnic background (68.7 vs. 64.7%) compared with late CA group. Similarly, Women, weekend admissions and African Americans remain less likely to receive early CA. In-hospital mortality was lowest in the intermediate group (odds ratio=0.30, 95% confidence interval: 0.28-0.33). CONCLUSION: Use of early CA has increased in the management of NSTEACS; however, there remain significant disparities in utilization of an early invasive approach in women, African Americans, admission day and older patients in the USA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".