Prediction of Prolonged ICU Stay in Cardiac Surgery Patients as a Useful Method to Identify Nutrition Risk in Cardiac Surgery Patients: A Post Hoc Analysis of a Prospective Observational Study
Bibliographic record
Abstract
BACKGROUND: Cardiovascular surgery patients with a prolonged intensive care unit (ICU) stay may benefit most from early nutrition support. Using established scoring systems for nutrition assessment and operative risk stratification, we aimed to develop a model to predict a prolonged ICU stay ≥5 days in order to identify patients who will benefit from early nutrition interventions. METHODS: This is a retrospective analysis of a prospective observational study of patients undergoing elective valvular, coronary artery bypass grafting, or combined cardiac surgery. The nutrition risk was assessed by well-established screening tools. Patients' preoperative EuroSCORE (European System for Cardiac Operative Risk Evaluation), primary disease, and intraoperative cardiopulmonary bypass (CPB) time were included as independent variables in a multivariate logistic regression analysis to predict a prolonged ICU stay (>4 days). RESULTS: The number of cardiac surgery patients included was 1193. Multivariate analysis revealed that for prediction of ICU stay >4 days, both Nutritional Risk Screening 2002 (area under the curve (AUC): 0.716, P = .020) and Mini Nutritional Assessment (MNA) score (AUC: 0.715, P = .037) were significant, whereas for prediction of ICU stay >5 days, only the MNA score showed significant results (AUC: 0.762, P = .011). CONCLUSION: Present data provide first evidence about the combined use of EuroSCORE, primary disease, CPB time, and nutrition risk screening tools for prediction of prolonged ICU stay in cardiac surgery patients. If prospectively evaluated in adequately designed studies, this model may help to identify patients with prolonged ICU stay to initiate early postoperative nutrition therapy and thus, facilitate an enhanced recovery.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".