Association Between Intensive Care Unit Usage and Long‐Term Medication Adherence, Mortality, and Readmission Among Initially Stable Patients With Non–ST‐Segment–Elevation Myocardial Infarction
Bibliographic record
Abstract
Background Hospitals in the United States vary in their use of intensive care units (ICUs) for hemodynamically stable patients with non-ST-segment-elevation myocardial infarction (NSTEMI). The association between ICU use and long-term outcomes after NSTEMI is unknown. Methods and Results Using data from the National Cardiovascular Data Registry linked to Medicare claims, we identified 65 256 NSTEMI patients aged ≥ 65 years without cardiogenic shock or cardiac arrest on presentation between 2011 and 2014. We compared 1-year medication non-adherence, cardiovascular readmission, and mortality across hospitals by ICU use using multivariable regression models. Among 520 hospitals, 154 (29.6%) were high ICU users (>70% of stable NSTEMI patients admitted to ICU), 270 (51.9%) were intermediate (30%-70%), and 196 (37.7%) were low (<30%). Compared with low ICU usage hospitals, no differences were observed in the risks of 1-year medication non-adherence (adjusted odds ratio 1.08, 95% CI, 0.97-1.21), mortality (adjusted hazard ratio 1.06, 95% CI, 0.98-1.15), and cardiovascular readmission (adjusted hazard ratio 0.99, 95% CI, 0.95-1.04) at high usage hospitals. Patients hospitalized at intermediate ICU usage hospitals had lower rates of evidence-based therapy and diagnostic catheterization within 24 hours of hospital arrival, and higher risks of 1-year mortality (adjusted hazard ratio 1.07, 95% CI, 1.02-1.12) and medication non-adherence (adjusted odds ratio 1.09, 95% CI, 1.02-1.15) compared with low ICU usage hospitals. Conclusions Routine ICU use is unlikely to be beneficial for hemodynamically stable NSTEMI patients; medication adherence, long-term mortality, and cardiovascular readmission did not differ for high ICU usage hospitals compared with hospitals with low ICU usage rates.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".