The relationship between inotrope exposure, six-hour postoperative physiological variables, hospital mortality and renal dysfunction in patients undergoing cardiac surgery
Bibliographic record
Abstract
INTRODUCTION: Acute haemodynamic complications are common after cardiac surgery and optimal perioperative use of inotropic agents, typically guided by haemodynamic variables, remains controversial. The aim of this study was to examine the relationship of inotrope use to hospital mortality and renal dysfunction. MATERIAL AND METHODS: A retrospective cohort study of 1,326 cardiac surgery patients was carried out at two university-affiliated ICUs. Multivariable logistic regression analysis and propensity matching were performed to evaluate whether inotrope exposure was independently associated with mortality and renal dysfunction. RESULTS: Patients exposed to inotropes had a higher mortality rate than those not exposed. After adjusting for differences in Parsonnet score, left ventricular ejection fraction, perioperative intraaortic balloon pump use, bypass time, reoperation and cardiac index, inotrope exposure appeared to be independently associated with increased hospital mortality (adjusted odds ratio (OR) 2.3, 95% confidence interval (95% CI) 1.2 to 4.5) and renal dysfunction (adjusted OR 2.7, 95% CI 1.5 to 4.6). A propensity score-matched analysis similarly demonstrated that death and renal dysfunction were significantly more likely to occur in patients exposed to inotropes (P = 0.01). CONCLUSIONS: Postoperative inotrope exposure was independently associated with worse outcomes in this cohort study. Further research is needed to better elucidate the appropriate use of inotropes in cardiac surgery.
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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 | 0.000 | 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".