Management and outcomes of acute myocardial infarction in patients with preexisting heart failure: an analysis of 2 million patients from the national inpatient sample
Bibliographic record
Abstract
Background Inpatient management and outcomes of patients presenting with acute myocardial infarction (AMI) with a history of heart failure (HF) have not been well characterized.Methods Hospitalizations for AMI from the Nationwide Inpatient Sample (2015–2018) were categorized according to a preexisting diagnosis of HF with preserved ejection fraction (HFpEF), reduced ejection fraction (HFrEF), or absence of HF. Utilization of invasive management and in-hospital outcomes were analyzed.Results Among 2,434,639 hospitalizations with an AMI, 19.8% had a history of HFrEF and 11.9% had a history of HFpEF. Coronary angiography and PCI respectively were performed significantly less among patients with HF (36.6% and 17.4% in HFpEF, 51.1% and 24.6% in HFrEF, and 64.4% and 42.3% among patients without HF, all p < 0.0001). Mortality was more common among patients with HFrEF (10.3%) and HFpEF (8.3%) when compared to patients without a history of HF (6.4%), p < 0.0001. Conclusion HF is a common preexisting comorbidity among patients presenting with AMI and is associated with lower utilization of invasive procedures and higher complications including mortality, particularly among those with HFrEF.
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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 | 0.000 |
| 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 | 0.000 |
| 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".