Predicting cerebral hyperperfusion syndrome after carotid artery procedures using deep learning

Prediction of Cerebral Hyperperfusion Syndrome After Carotid Revascularization Using Deep Learning

This study is testing if deep learning can help predict which patients are at risk of developing cerebral hyperperfusion syndrome after carotid artery procedures.

Quick facts

• Study type: Observational
• Enrollment: 500 (estimated)
• Ages: 30 Years to 80 Years
• Sex: All
• Sponsor: State Institution "Republican Scientific and Practical Center" Cardiology, Belarus (Academic / other)
• Locations: 1 site (Minsk)
• Trial ID: NCT05800821 on ClinicalTrials.gov

What this trial studies

This observational study aims to predict the occurrence of cerebral hyperperfusion syndrome (CHS) following carotid revascularization procedures, such as carotid endarterectomy and stenting. It utilizes deep learning algorithms to analyze patient data and identify risk factors associated with increased perfusion rates that may lead to CHS. The study focuses on patients with significant carotid stenosis and aims to improve understanding and awareness of CHS among healthcare providers. By identifying at-risk patients, the study seeks to enhance patient outcomes and reduce complications associated with carotid revascularization.

Who should consider this trial

Good fit: Ideal candidates for this study are individuals aged 30 to 80 with symptomatic or asymptomatic significant carotid stenosis.

Not a fit: Patients who do not have carotid stenosis or are unwilling to participate in the study may not receive any benefit.

Why it matters

Potential benefit: If successful, this study could lead to better prediction and management of cerebral hyperperfusion syndrome, ultimately improving patient safety and outcomes after carotid procedures.

How similar studies have performed: While there is limited knowledge about CHS, the use of deep learning in predicting medical outcomes is a growing field, and similar approaches have shown promise in other areas of medicine.

Eligibility criteria

Inclusion Criteria:

1. Age between 30 and 80 years.
2. Occlusive-stenotic lesion of the carotid arteries with indications for carotid revascularization.

Exclusion Criteria:

1. Systemic vasculitis.
2. Cerebral vessel aneurysms.
3. Arteriovenous malformation of the brain.
4. Primary brain tumor (including metastatic lesions).
5. Epilepsy.
6. History of traumatic brain injury.
7. Demyelinating diseases of the central nervous system.
8. History of neuroinfection.
9. Atrial fibrillation.
10. Frequent supraventricular or ventricular extrasystoles.
11. Chronic heart failure with left ventricular ejection fraction less than 40%.
12. Chronic kidney disease with estimated glomerular filtration rate less than 45 mL/min/1.73 m².
13. Presence of an implanted cardioverter-defibrillator or pacemaker.
14. Presence of contraindications to the medical use of iodine-containing radiographic contrast agents.
15. Patient's unwillingness to continue participating in the study.
16. Absence of a temporal acoustic window for transcranial Doppler ultrasonography.

Where this trial is running

Minsk

• State Institution "Republican Scientific and Practical Center "Cardiology" — Minsk, Belarus (Recruiting)

Study contacts

• Study coordinator: Ivan Maiseyenka

Conditions Carotid Artery Diseases; Carotid Atherosclerosis; Carotid Artery Stenosis; Cerebral Hyperperfusion Syndrome; Carotid Stenosis; Stroke; Deep Learning