CHANGES IN VASOACTIVE DRUG REQUIREMENTS AND MORTALITY IN CARDIAC INTENSIVE CARE UNIT PATIENTS
Bibliographic record
Abstract
ABSTRACT: Background: The Society for Cardiovascular Angiography and Intervention (SCAI) Shock Classification can define shock severity. We evaluated the vasoactive-inotropic score (VIS) combined with the SCAI Shock Classification for mortality risk stratification. Methods: This was a single-center retrospective cohort analysis including Mayo Clinic cardiac intensive care unit patients from 2007 to 2015. The peak VIS was calculated at 1 and 24 h after cardiac intensive care unit admission. In-hospital mortality was evaluated using multivariable logistic regression. Results: Of 9,916 included patients, vasoactive drugs were used in 875 (8.8%) within 1 h and 2,196 (22.1%) within 24 h. A total of 888 patients (9.0%) died during hospitalization. Patients who required vasoactive drugs within 1 h had higher in-hospital mortality (adjusted odds ratio [OR], 1.30; 95% confidence interval [CI], 1.03-1.65; P = 0.03) and in-hospital mortality rose with the VIS during the first 1 h (adjusted OR per 10 units, 1.22; 95% CI, 1.12-1.33; P < 0.001). The increase in VIS from 1 to 24 h was associated with higher in-hospital mortality (adjusted OR per 10 units, 1.16; 95% CI, 1.10-1.21; P < 0.001). These results were consistent in the 1,067 patients (10.9%) with cardiogenic shock. A gradient of in-hospital mortality was observed according to the VIS at 1 h and the increase in VIS from 1 to 24 h. Conclusions: Higher vasoactive drug requirements portend a higher risk of mortality, particularly a high VIS early after admission. The VIS provides incremental prognostic information beyond the SCAI Shock Classification, emphasizing the continuum of risk that exists across the spectrum of shock severity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".