Use of Vasoactive Medications after Cardiac Surgery in the United States
Bibliographic record
Abstract
Abstract Rationale Patients undergoing cardiac surgery often require vasopressor or inotropic (“vasoactive”) medications, but patterns of postoperative use are not well described. Objectives This study aimed to describe vasoactive medication administration throughout hospitalization for cardiac surgery, to identify patient- and hospital-level factors associated with postoperative use, and to quantify variation in treatment patterns among hospitals. Methods Retrospective study using the Premier Healthcare Database. The cohort included adult patients who underwent coronary artery bypass grafting or open valve repair or replacement (or in combination) from January 1, 2016, to June 30, 2018. Primary outcome was receipt of vasoactive medication(s) on the first postoperative day (POD1). We identified patient- and hospital-level factors associated with receipt of vasoactive medications using multilevel mixed-effects logistic regression modeling. We calculated adjusted median odds ratios to determine the extent to which receipt of vasoactive medications on POD1 was determined by each hospital, then calculated quotients of Akaike Information Criteria to compare the relative contributions of patient and hospital characteristics and individual hospitals with observed variation. Results Among 104,963 adults in 294 hospitals, 95,992 (92.2%) received vasoactive medication(s) during hospitalization; 30,851 (29.7%) received treatment on POD1, most commonly norepinephrine (n = 11,427, 37.0%). A median of 29.0% (range, 0.0–94.4%) of patients in each hospital received vasoactive drug(s) on POD1. After adjustment, hospital of admission was associated with twofold increased odds of receipt of any vasoactive medication on POD1 (adjusted median odds ratio, 2.07; 95% confidence interval, 1.93–2.21). Admitting hospital contributed more to observed variation in POD1 vasoactive medication use than patient or hospital characteristics (quotients of Akaike Information Criteria 0.58, 0.44, and <0.001, respectively). Conclusions Nearly all cardiac surgical patients receive vasoactive medications during hospitalization; however, only one-third receive treatment on POD1, with significant variability by institution. Further research is needed to understand the causes of variability across hospitals and whether these differences are associated with outcomes.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".