Risk factors for surgical site infection after intracranial electroencephalography monitoring for epilepsy in the pediatric population
Bibliographic record
Abstract
OBJECTIVE Intracranial electroencephalography (iEEG) monitoring is an important method of identifying the seizure focus in patients with medically refractory epilepsy. While previous studies have demonstrated low rates of surgical complications, reported rates of surgical site infection (SSI) are highly variable. To date, no studies have specifically evaluated the patient or operative risk factors contributing to SSI. The goals of this study were to examine the rate of SSI after iEEG monitoring for epilepsy workup in pediatric patients and to determine the variables that might contribute to the development of SSI. METHODS A retrospective analysis of hospital charts at the Hospital for Sick Children was performed for all patients who had undergone iEEG monitoring between 2000 and 2016. Univariate and multivariate analyses were performed to look for statistically significant variables in relation to SSI. RESULTS Among 199 patients eligible for analysis, 8 (4.0%) developed SSIs within a period ranging from 21 to 51 days postoperatively. Univariate analysis yielded 4 factors related to SSI: number of people present in the operating room on electrode insertion (p = 0.02), length of insertion surgery (p = 0.04), previous operation at the same surgical site (p = 0.04), and number of depth electrodes inserted (p = 0.01). Multivariate analysis revealed that both the number of people present during the implant operation (OR 0.08, 95% CI 0.01-0.70) and the number of depth electrodes inserted (OR 3.52, 95% CI 1.44-8.59) independently contributed to SSI. CONCLUSIONS This is the largest case series and the first comprehensive review of both patient and operative risk factors in the development of SSI from iEEG monitoring in a pediatric population. The authors' institution had a lower rate of infection than those in most other studies, which could be explained by their protocol of administering intravenous antibiotics perioperatively and post-implant removal antibiotics for 14 days. The authors found a correlation between SSI and the number of people present during the implant operation, as well as the number of depth electrodes; both may contribute to breaks in sterility.
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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 | 0.000 | 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".