Neuromonitoring during Endovascular Thoracoabdominal Aortic Aneurysm Repair: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Spinal cord ischemia (SCI) is a potentially devastating complication of thoracic endovascular aortic repair (TEVAR) and fenestrated-branched endovascular aortic repair (F-BEVAR). The aim of this systematic review was to evaluate the efficacy of neuromonitoring modalities to mitigate the risk of SCI during TEVAR and F-BEVAR procedures. METHODS: Following the PRISMA guidelines, we conducted a detailed literature search of databases including PubMed, MEDLINE via Ovid, Embase, Scopus, and Cochrane CENTRAL, from 1998 to the present. Inclusion criteria were original research articles examining neuromonitoring during TEVAR and F-BEVAR. The primary outcome was the incidence of SCI, while the secondary outcome included early mortality. The quality of studies was assessed using the Newcastle-Ottawa Scale. RESULTS: From 1,450 identified articles, 11 met inclusion criteria, encompassing data from 1,069 patients. Neuromonitoring modalities included motor-evoked potentials (MEPs), somatosensory evoked potentials (SSEPs), and near-infrared spectroscopy. The combination of MEPs and SSEPs was most commonly used, with 93% sensitivity and 96% specificity for detecting SCI risks. SCI incidence ranged from 3.8 to 17.3%, with permanent deficits occurring in 2.7-5.8% of cases. In-hospital mortality ranged from 0.4 to 8%. Risk factors for SCI were identified, including operation duration and extent of aortic coverage. CONCLUSIONS: Neuromonitoring with MEPs and SSEPs appears to be effective in detecting perioperative SCI risk during TEVAR and F-BEVAR. However, discrepancies between neuromonitoring changes and actual SCI outcomes suggest the need for cautious interpretation. While the incidence of SCI remains variable, identified risk factors may guide clinical decisions, particularly in high-risk procedures. Future research should focus on prospective studies and randomized controlled trials to validate these findings and improve SCI prevention strategies in TEVAR and F-BEVAR.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".