A223 FACTORS ASSOCIATED WITH EUS DIAGNOSTIC YIELD AND ADVERSE EVENT RISK: A LARGE RETROSPECTIVE SINGLE CENTRE STUDY
Bibliographic record
Abstract
Maximizing endoscopic ultrasound (EUS) diagnostic yield (DY) and safety is key to providing high quality care, yet the influence of patient and procedure factors on both these outcomes have only been examined using limited sample sizes. We aim to examine factors associated with EUS diagnostic yield and adverse event (AE) risk using a large cohort of patients who underwent a EUS procedure at our centre. A retrospective chart review of EUS procedures performed between 2009 and 2015 at The Ottawa Hospital was conducted. Data regarding patient demographics and details of the lesion, procedure, medications, and DY were collected. All patient ER visits or hospitalizations within 30 days of the EUS procedure were also collected and reviewed. Relation of the hospital encounter (definitely, possibly, and not) to the EUS procedure was established by consensus using pre-defined criteria. 1647 EUS procedures were examined. The median patient age was 64 (IQR, 53–73) years and 50% were female. Of all EUS cases, 105 (6.4%) presented to the ER or were hospitalized; however, only 58 (3.5%) were related to EUS. DY was 78% for EUS-FNA cases only. Rapid on-site evaluation impacted DY (73% vs. 80%, p = 0.02) but not AE risk (p = 0.53). Multivariate analysis of all cases demonstrated performing a FNA (OR 2.1, 95% CI 1.0–4.4) and having anesthesia-guided sedation (OR 3.9, 95% CI 1.8–8.5) was associated with an increased AE risk. DY was associated being a smoker (OR 0.7, 95% CI 0.5–0.9). Upon separating out EUS-FNA procedures only, we found on multivariate analysis that lesions > 3 cm (OR 2.4, 95% CI 1.1–5.3), using anesthesia-guided sedation (OR 3.9, 95% CI 1.3–12.0), and using a P2Y12 inhibitor (OR 7.9, 95% CI 2.5–25.1) was associated with an increased AE risk. Solid lesions (OR 2.0, 95% CI 1.1–3.6) and lesions > 3 cm (OR 1.9, 95% CI 1.2–3.2) were associated with an increased DY. Lesions > 3 cm should be subject to fewer FNA passes to decrease AE risk. EUS procedures on patients who expect to receive anesthesia-guided sedation should be reconsidered. Finally, patients receiving P2Y12 inhibitors should not be subject to FNA, unless their medication is stopped. Table 1. Factors associated with adverse events within 30 days of EUS procedure diagnostic and yield, multivariate analysis. None
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".