Plaque Characteristics on CT Angiography Do Not Improve the Ability to Predict Hemodynamic Instability During and After Carotid Angioplasty and Stenting
Bibliographic record
Abstract
BACKGROUND: Hemodynamic instability is commonly seen during carotid angioplasty and stenting. Although prophylactic treatment with anticholinergics is beneficial, selected use in high-risk patients is desirable. This study examines whether plaque characteristics on computed tomography angiography in addition to demographic factors improve predictive capability. METHODS: We retrospectively collected information from 298 carotid angioplasty procedures between January 2013 and December 2018. Nine individuals were excluded due to a previous ipsilateral endarterectomy. Our primary outcome was a decrease of 20% or more in heart rate or blood pressure at angioplasty. Data were analyzed using χ2 tests and regression statistics. RESULTS: Of the 289 patients included for analysis, 57 had intraoperative instability and 26 had postoperative instability. Radiologist interpretation was found to have a risk ratio of 1.63 (95% confidence interval: 1.00-2.65) for intraoperative instability (P=0.080). Intraoperative instability was significantly associated with subsequent postoperative instability (P=0.005). Our regression model included previous endarterectomy and diabetes as predictive factors with a sensitivity of 11.3% and a specificity of 100.0%. Anticholinergic usage was associated with hypotension without coexisting bradycardia with a risk ratio of 2.36 (95% confidence interval: 1.06-5.26; P=0.047). CONCLUSIONS: Individuals without a previous contralateral endarterectomy and/or history of diabetes are at lower risk of hemodynamic instability. The addition of computed tomography angiographic variables does not improve this prediction. Future prospective, randomized work is required to improve our ability to identify and treat individuals at high risk of instability during carotid angioplasty and stenting.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".