Predicting cerebral hyperperfusion syndrome after carotid artery procedures using deep learning
Official title: Prediction of Cerebral Hyperperfusion Syndrome After Carotid Revascularization Using Deep Learning
Run by State Institution "Republican Scientific and Practical Center" Cardiology, Belarus · NCT05800821 · Data updated 2026-11-12
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 |
|---|---|
| Ages | 30 Years to 80 Years |
| Sex | All |
| Enrollment | 500 (estimated) |
| Sponsor | State Institution "Republican Scientific and Practical Center" Cardiology, Belarus Academic / other |
| Locations | 1 site (Minsk) |
| Status | Recruiting |
| Started | May 3, 2023 |
| Main results expected | May 3, 2028 |
| Study ends | May 3, 2030 |
| Registry record updated | November 25, 2025 |
| Trial ID | NCT05800821 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates for this study are individuals aged 30 to 80 with symptomatic or asymptomatic significant carotid stenosis.
✕Probably not a fit
Patients who do not have carotid stenosis or are unwilling to participate in the study may not receive any benefit.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
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.
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 gone: 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.
Who can join: the full rules
Show full inclusion / exclusion 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 it's running
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State Institution "Republican Scientific and Practical Center "Cardiology"
Minsk, Belarus Recruiting
Study contacts
- Study coordinator: Ivan Maiseyenka
- Email: i_a_moiseenko@mail.ru
- Phone: +375333288850
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.