Abstract 18546: Vasoactive Medication Variability in Patients With Acute Myocardial Infarction Complicated by Cardiogenic Shock
Bibliographic record
Abstract
Background: Patients with acute myocardial infarction complicated by cardiogenic shock (AMI-CS) often require vasoactive medications to maintain organ perfusion. However, there are limited data on the real-world use of vasoactive medications in this population. Methods: We used the Vizient® Clinical Data Base to identify patients aged ≥18 years who were admitted between 2015-2019 with AMI-CS and required mechanical ventilation. We report the proportion of patients receiving vasoactive medications by day as well as site-level variability for vasoactive medication strategies using the median odds ratio (MOR), which quantifies the odds that a patient receives a strategy at two randomly selected sites after adjusting for covariates. Results: We identified 10,758 patients from 160 centers admitted with AMI-CS. The median age was 65.8 years, 29.8% were female, 83.6% received vasoactive medications in the first 5 days, and 56.9% received temporary mechanical support. The most used vasoactive medication on day 1 was norepinephrine (48.2%), followed by epinephrine (31.5%), and dopamine (16.9%) ( Figure ). On day 1, 65.9% and 36.2% of patients receiving vasoactive medications were prescribed ≥2 or ≥3 different medications, respectively, including 111 different combinations. Among patients receiving inotropes (N=4,485), there was a nearly 3-fold difference in milrinone use across sites after accounting for differences in patient characteristics (MOR 2.89; 95% confidence interval [CI]: 2.39-3.38). For patients receiving either norepinephrine, dopamine, or epinephrine on day 1 (N=3,224), there was a nearly 2-fold difference in the use of norepinephrine as a first-line vasopressor across sites (MOR 1.84; 95% CI: 1.59-2.06). Conclusion: Norepinephrine is the most commonly used vasoactive medication in patients with AMI-CS. However, there is substantial site-level variability in vasoactive medication strategies, which require further study.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".