Risk Factors for Prolonged Stay in the Intensive Care Unit and on the Ward After Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Prolonged length of stay (LOS) after cardiac surgery has been associated with poor outcome and a considerable expenditure of health care resources. As our patient's demographics are changing, a continuing evaluation of the preoperative and intraoperative variables affecting LOS in the intensive care unit (ICU) and on the floor remains important. METHODS: This is a prospective study examining the determinants of prolonged LOS in 426 consecutive patients after cardiac surgery. Univariate and multivariate analyses were performed for an ICU stay > or =2 days and for a stay on the floor >7 days. Secondary outcome was the incidence of postoperative complications. RESULTS: Among all patients, 27.7% had a prolonged stay in the ICU. Univariate analysis revealed 13 perioperative variables that were significantly associated with prolonged stay. Independent predictors for extended ICU LOS included an ejection fraction <40% (RR 1.83; p = 0.04), high Parsonnet score (RR 2.23; p = 0.012), history of renal failure (RR 5.39; p = 0.001), and an emergency surgery (RR 2.43; p = 0.007). Furthermore, 30.5% of patients had an extended stay on the floor with female gender (RR 1.93; p = 0.009) and age (RR 2.55; p = 0.0001) being two independent risk factors. CONCLUSIONS: In this series of 426 consecutive patients, we have identified several perioperative risk factors associated with prolonged hospitalization that can help clinicians in their preoperative patient counseling, risk stratification, and selection. However, the most obvious use of these results is in allowing decision makers to implement specific strategies that would best allocate resources depending on the risk profile of cardiac patients.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".