Clinical trial NCT06294496: Study of Carotid Artery Stenosis Through the Integration of Multimodal Imaging and Computational Fluid Dynamics
Official title: Perioperative Risk and Clinical Efficacy Study of Cervical Artery Stenosis Patients Through the Integration of Multimodal Imaging and Computational Fluid Dynamics
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Carotid Stenosis
Keywords: Carotid Stenosis
Multimodal Imaging
Computational Fluid Dynamics
Lead sponsor: Sichuan Provincial People's Hospital
Sponsor class: OTHER
First posted: 2024-03-05
Enrollment: 40
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adults aged between 18 and 80 years old

Brief summary: Ischemic stroke affects 2.5 to 3 million people annually in China, ranking as the leading cause of death and disability. Cervical artery stenosis is a significant contributor to this problem, with about 50% of patients experiencing cognitive impairment due to reduced cerebral blood flow. Two main surgical approaches, carotid endarterectomy (CEA) and carotid artery stenting (CAS), are used to treat severe cervical artery stenosis, but their effects on various factors remain unclear.

This project collects multimodal imaging data, including CT perfusion and angiography, to create 3D models of cervical artery stenosis. Computational fluid dynamics and AI analysis are used to assess hemodynamics. By monitoring blood flow, oxygen levels, and evaluating postoperative outcomes, the goal is to tailor surgical approaches for better patient outcomes and improved quality of life.

Detailed description: In China, the annual incidence of ischemic stroke is estimated to be between 2.5 to 3 million cases, making it the leading cause of death and disability among the population. Among these cases, cervical artery stenosis is a significant independent risk factor for ischemic stroke. Approximately 50% of patients with cervical artery stenosis are prone to develop vascular-related cognitive impairment due to cerebral hypoperfusion, severely affecting human health and quality of life.

There are currently two main surgical approaches for treating severe cervical artery stenosis: carotid endarterectomy (CEA) and carotid artery stenting (CAS). The effects of these two surgical methods on preoperative and postoperative intracranial and extracranial hemodynamic changes, the mechanisms underlying perioperative complications, the establishment of collateral circulation, and long-term prognosis remain unclear. Therefore, researching perioperative risk assessment and clinical efficacy of different surgical approaches is of great significance for patient outcomes.

This project aims to collect multimodal imaging data from patients with cervical artery stenosis, including brain CT perfusion imaging and CT angiography. Using artificial intelligence algorithms, three-dimensional models of cervical artery stenosis will be reconstructed, and computational fluid dynamics will be employed to automatically or semi-automatically analyze the hemodynamic characteristics of patients' carotid arteries. By monitoring cerebral blood flow velocity, local cerebral oxygen metabolism, and assessing postoperative stroke, ischemia-reperfusion injury, and collateral circulation both intracranially and extracranially, precise evaluations will be conducted.

Based on individual patient characteristics, the surgical approach can be optimized to prevent cerebral ischemia-reperfusion injury, improve clinical prognosis, and enhance the quality of life for patients.

Eligibility criteria: Inclusion Criteria:

1) Clinical diagnosis of carotid stenosis.

Exclusion Criteria:

1. Vascular stenosis with vascular malformations.
2. Aneurysm.
3. Severe heart, liver, or kidney disease.

Primary outcomes:
measure: Perioperative cardio-cerebrovascular adverse events
timeFrame: 2 weeks after surgery
Secondary outcomes:
measure: Compute fluid dynamics parameters
timeFrame: 1 day before the surgery, 3 days after the surgery
measure: Clinical outcome
timeFrame: 6 months after surgery
Other outcomes:
Locations:
city: Chengdu
country: China
facility: Sichuan Provincial People's Hospital
state: Sichuan
Study officials:
affiliation: Sichuan Provincial People's Hospital
name: Chunling Li, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Pessin MS, Hinton RC, Davis KR, Duncan GW, Roberson GH, Ackerman RH, Mohr JP. Mechanisms of acute carotid stroke. Ann Neurol. 1979 Sep;6(3):245-52. doi: 10.1002/ana.410060311.
citation: Wu S, Wu B, Liu M, Chen Z, Wang W, Anderson CS, Sandercock P, Wang Y, Huang Y, Cui L, Pu C, Jia J, Zhang T, Liu X, Zhang S, Xie P, Fan D, Ji X, Wong KL, Wang L; China Stroke Study Collaboration. Stroke in China: advances and challenges in epidemiology, prevention, and management. Lancet Neurol. 2019 Apr;18(4):394-405. doi: 10.1016/S1474-4422(18)30500-3.
citation: Pandian JD, Gall SL, Kate MP, Silva GS, Akinyemi RO, Ovbiagele BI, Lavados PM, Gandhi DBC, Thrift AG. Prevention of stroke: a global perspective. Lancet. 2018 Oct 6;392(10154):1269-1278. doi: 10.1016/S0140-6736(18)31269-8.
citation: Lanzino G, Tallarita T, Rabinstein AA. Internal carotid artery stenosis: natural history and management. Semin Neurol. 2010 Nov;30(5):518-27. doi: 10.1055/s-0030-1268864. Epub 2011 Jan 4.
citation: Aristova M, Vali A, Ansari SA, Shaibani A, Alden TD, Hurley MC, Jahromi BS, Potts MB, Markl M, Schnell S. Standardized Evaluation of Cerebral Arteriovenous Malformations Using Flow Distribution Network Graphs and Dual-venc 4D Flow MRI. J Magn Reson Imaging. 2019 Dec;50(6):1718-1730. doi: 10.1002/jmri.26784. Epub 2019 May 9.
citation: Gonzales NR, Demaerschalk BM, Voeks JH, Tom M, Howard G, Sheffet AJ, Garcia L, Clair DG, Barr J, Orlow S, Brott TG; CREST Investigators. Complication rates and center enrollment volume in the carotid revascularization endarterectomy versus stenting trial. Stroke. 2014 Nov;45(11):3320-4. doi: 10.1161/STROKEAHA.114.006228. Epub 2014 Sep 25.
citation: Fukuda S, Shimogonya Y, Yonemoto N, Fukuda M, Watanabe A, Fujiwara K, Enomoto R, Hasegawa K, Yasoda A, Tsukahara T; NHO Carotid CFD Study Group. Hemodynamic Risk Factors for the Development of Carotid Stenosis in Patients with Unilateral Carotid Stenosis. World Neurosurg. 2022 Apr;160:e353-e371. doi: 10.1016/j.wneu.2022.01.019. Epub 2022 Jan 11.
citation: Pavlin-Premrl D, Boopathy SR, Nemes A, Mohammadzadeh M, Monajemi S, Ko BS, Campbell BCV. Computational Fluid Dynamics in Intracranial Atherosclerosis - Lessons from Cardiology: A Review of CFD in Intracranial Atherosclerosis. J Stroke Cerebrovasc Dis. 2021 Oct;30(10):106009. doi: 10.1016/j.jstrokecerebrovasdis.2021.106009. Epub 2021 Jul 31.