Incidence, Outcomes, and Mortality of Reintubation After Anterior Cervical Fusion
Bibliographic record
Abstract
STUDY DESIGN: Retrospective database analysis. OBJECTIVE: To compare perioperative patient characteristics, hospital resource utilization, and early postoperative outcomes in patients requiring reintubation after anterior cervical fusion (ACF). SUMMARY OF BACKGROUND DATA: Airway compromise is a potential complication after anterior cervical surgery. Postsurgical soft-tissue edema or hematoma formation may be so severe that an unplanned reintubation may be required. The rate of reintubation after ACF and the effect on hospital outcomes remains unknown. METHODS: The Nationwide Inpatient Sample database was queried from 2002-2011. Patients undergoing elective ACF procedures for degenerative diagnoses were selected. Those who required an unplanned reintubation after ACF were identified. Patient demographics, comorbidities, length of stay, costs, number of levels fused, and mortality were analyzed. SPSS version 20 was used for statistical analysis and a P < 0.001 denoted statistical significance. RESULTS: A total of 262,425 patients underwent an elective ACF between 2002 and 2011 of which 1464 patients (5.6 per 1000 cases) required reintubation during their admission. The rate of reintubation was statistically greater for 3+-level fusions than the 1- to 2-level fusion cases. On average, patients requiring reintubation were older and had a greater number of comorbidities. These patients also incurred a significantly greater hospital stay and total hospital costs than unaffected patients. In addition, significant predictors for reintubation included 3+-level fusions, congestive heart failure, anemia, postoperative aspiration pneumonia, hematoma, thromboembolic events, and dysphagia. CONCLUSION: The reintubation rate after an elective ACF is 0.5%, and it increases to 1.6% after 3+-level fusions. Older patients with greater comorbidities are at an increased risk for reintubation. Given the greater LOS, costs and mortality associated with reintubation, it is imperative to identify patients at increased risk to help improve patient outcomes and decrease hospital resource utilization. LEVEL OF EVIDENCE: 4.
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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 | 0.002 | 0.003 |
| 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".