Parameters of Hemodynamic Instability Measured by Transcranial Doppler in Patients Undergoing Carotid Endarterectomy and Carotid Angioplasty-Stent: Comparison Between Groups
Bibliographic record
Abstract
Background: Carotid atheromatosis causes 15-20% of ischemic stroke. Interventional treatment is sometimes limited by the risk of reperfusion, stroke and other complications. A better knowledge of hemodynamic changes along the procedures could help to establish the treatment in each patient. We aimed to gain insight into this employing transcranial Doppler (TCD) monitoring and determine conditions with a higher risk of complications. Methods: We prospectively recruited 53 patients with ≥ 70% cervical carotid stenosis or occlusion who underwent carotid angioplasty-stenting (CAS) (28 patients) or carotid endarterectomy (CEA) (25 patients). We performed TCD monitoring during the whole procedures. Results: Frequency of reperfusion syndrome (RS) was similar in both procedures, but other complications were more common (P < 0.001) in CEA. At baseline, hemodynamic reserve (HR) was correlated with asymmetry between mean flow velocity (MV) of middle cerebral arteries (MCAs) (r = -0.582; P = 0.003) and delayed systolic peak (r = -0.44; P = 0.02). Over 100% increase in MV after recanalization was associated with lower HR, but not to the RS. A more marked change in MV was associated with CAS procedure (P = 0.081), lack of orthodromic flow in ophthalmic artery (P = 0.013) and ≥ 95% carotid stenosis. Conclusions: Baseline asymmetry between MCA MVs and subsequent increase in the latter is related to parameters of hemodynamic instability, such as critical stenosis, endovascular procedure and loss of orthodromic flow in the OF. However, MV alone is not a good predictor of RS, despite of baseline MVs asymmetry related to impaired HR and peak systolic delay. RS is equally frequent in CEA and CAS techniques, while other complications are more common in the former. J Neurol Res. 2015;5(4-5):233-245 doi: http://dx.doi.org/10.14740/jnr351w
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".