The Impact of Postoperative Intensive Care Unit Admission on Postoperative Hospital Length of Stay and Costs: A Prespecified Propensity-Matched Cohort Study
Bibliographic record
Abstract
BACKGROUND: In this prespecified cohort study, we investigated the influence of postoperative admission to the intensive care unit versus surgical ward on health care utilization among patients undergoing intermediate-risk surgery. METHODS: Of adult surgical patients who underwent general anesthesia without an absolute indication for postoperative intensive care unit admission, 3530 patients admitted postoperatively to an intensive care unit were matched to 3530 patients admitted postoperatively to a surgical ward using a propensity score based on 23 important preoperative and intraoperative predictor variables. Postoperative hospital length of stay and hospital costs were defined as primary and secondary end points, respectively. RESULTS: Among patients with low propensity for postoperative intensive care unit admission, initial triage to an intensive care unit was associated with increased postoperative length of stay (incidence rate ratio, 1.69 [95% CI, 1.59-1.79]; P < .001) and hospital costs (incidence rate ratio, 1.92 [95% CI, 1.81-2.03]; P < .001). By contrast, postoperative intensive care unit admission of patients with high propensity was associated with decreased postoperative length of stay (incidence rate ratio, 0.90 [95% CI, 0.85-0.95]; P < .001) and costs (incidence rate ratio, 0.92 [95% CI, 0.88-0.97]; P = .001). Decisions regarding postoperative intensive care unit resource utilization were influenced by individual preferences of anesthesiologists and surgeons. CONCLUSIONS: In patients with an unclear indication for postoperative critical care, intensive care unit admission may negatively impact postoperative hospital length of stay and costs. Postoperative discharge disposition varies substantially based on anesthesia and surgical provider preferences but should optimally be driven by an objective assessment of a patient's status at the end of 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".