Predictors, Complications, and Clinical Outcomes of Cerebrospinal Fluid Leak Post Endoscopic Endonasal Skull Base Surgery
Bibliographic record
Abstract
Background: Postoperative cerebrospinal fluid (CSF) leakage remains a significant complication following endoscopic endonasal skull base surgery (EES), leading to increased morbidity. This study aimed to identify factors and interventions predicting postoperative CSF leaks after EES for intradural skull base tumors and their clinical outcomes. Methods: We retrospectively reviewed data from 542 patients who underwent EES for intradural skull base pathology at the Ottawa Hospital between October 2001 and October 2023. Patient demographics, pre-operative, intraoperative (including reconstruction type), postoperative data, and patient outcomes were collected. Results: A total of 40 patients (7.4%) developed a postoperative CSF leak. The highest rate was in patients with suprasellar lesions (5.9%), followed by anterior cranial fossa lesions (1.1%). Significant predictors included a higher mean Body Mass Index (BMI) (30.4 vs. 26.1, p = 0.001). The use of a nasoseptal flap for reconstruction was associated with a significantly lower incidence of CSF leaks (p = 0.001). Tumor location, approach type, and dural sealants were not independent factors for the development of CSF leaks. Patients with CSF leaks had significantly longer lengths of stay (16.7 vs. 9.21 days, p < 0.001), higher 30-day readmission rates (p < 0.001), and increased postoperative sepsis (p = 0.021) and diabetes insipidus (p < 0.001). Conclusion: This retrospective study shows that higher preoperative BMI is associated with a significant risk of postoperative CSF leaks after EES. Conversely, using a pedicled vascularized flap reduces the risk. Postoperative CSF leaks are linked to increased morbidity, including diabetes insipidus and sepsis, prolonged hospitalization, and higher readmission rates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".