Outcomes with mechanical circulatory support devices among patients with mechanical complications of acute myocardial infarction
Bibliographic record
Abstract
AIMS: The aim of this study is to examine the trends and outcomes of mechanical circulatory support (MCS) device use among patients with mechanical complications of acute myocardial infarction (AMI). METHODS AND RESULTS: Using data from the National Inpatient Sample (a large admirative database in the USA) years 2016-20, we identified AMI admissions (ST-elevation and non-ST-elevation myocardial infarction) with mechanical complications (ventricular septal defect, free wall rupture, or papillary muscle rupture). Among 4 450 219 AMI patients, 7025 (0.2%) had a mechanical complication of which 3115 patients (44.3%) received at least one MCS device. There was a rising trend in MCS use (39.3% in 2016 to 48.9% in 2020, Ptrend = 0.02), but there was no corresponding reduction in the incidence of in-hospital mortality (36.9% in 2016 vs. 43.4% in 2020, Ptrend = 0.75). There was no significant difference in in-hospital mortality between those who received MCS vs. those who did not (48.4 vs. 34.5%, respectively). CONCLUSION: In this large observational analysis of AMI hospitalizations, mechanical complications were rare and associated with very high in-hospital mortality. Although the use of MCS has increased, in-hospital mortality rates remain high even among patients who received MCS. Further investigations are needed to clarify the role of MCS devices among patients with mechanical complications of 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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".