Preview

Ambulatornaya khirurgiya = Ambulatory Surgery (Russia)

Advanced search

Risk factors associated with the emergence and progression of ocular ischemic syndrome in carotid artery stenosis

https://doi.org/10.21518/akh2025-009

Abstract

Introduction. The ocular ischemic syndrome (OIS) is a condition characterized by insufficient blood supply to the eye due to occlusion or hemodynamically significant stenosis of the internal carotid artery.

Aim. To study the risk factors for the occurrence and progression of OIS.

Materials and methods. The study included 91 patients divided into two groups: Group I (61 patients with OIS) and Group II (30 control patients). The study design was cross-sectional, allowing for the identification of potential risk factors. Data collection was performed using a standardized questionnaire and medical records, as well as duplex scanning of the brachiocephalic arteries.

Results. The results showed that the main risk factors increasing the likelihood of OIS are stenosis of the vertebral carotid artery (VCA) greater than 70% (AUC = 0.968; 95% CI: 0.925–1.000, p < 0.001) and occlusion of the VCA (AUC = 0.935; 95% CI: 0.864–1.000, p < 0.001). Other identified risk factors include nicotine dependence, arterial hypertension, dyslipidemia, obesity, and diabetes mellitus. The combination of risk factors for stenosis of the VCA greater than 70% includes nicotine dependence, arterial hypertension, dyslipidemia, and obesity, with a sensitivity of 88.6% and specificity of 91.4%. For VCA occlusion, key factors include arterial hypertension, obesity, and diabetes mellitus, with a sensitivity of 81.3% and specificity of 84.2%.

Discussion. Our study identified key factors such as carotid artery stenosis, arterial hypertension, and diabetes mellitus that contribute to the development of ocular ischemic syndrome, which is consistent with findings from other studies. The severity of carotid artery stenosis is an important predictor of eye ischemia, as it directly affects blood supply to the ocular artery.

Conclusions. The study allows for the identification of associations between risk factors and ocular ischemic syndrome, which may facilitate the development of prevention and treatment strategies, as well as the implementation of artificial intelligence for diagnosis. Key risk factors such as arterial hypertension, dyslipidemia, and obesity significantly increase the likelihood of developing OIS in cases of stenosis and occlusion of the carotid arteries.

About the Authors

P. E. Vakhrat’ian
Russian Scientific Center of Surgery named after Academician B.V. Petrovsky
Russian Federation

Pavel E. Vakhrat’ian, Dr. Sci. (Med.), Vascular Surgeon, Vascular Surgery Department,

2, Abrikosovsky Lane, 119991, Moscow



S. O. Popov
Russian Scientific Center of Surgery named after Academician B.V. Petrovsky
Russian Federation

Sergey O. Popov, Cand. Sci. (Med.), Cardiovascular Surgeon, Chief Physician,

2, Abrikosovsky Lane, 119991



A. V. Zabaeva
Sechenov First Moscow State Medical University (Sechenov University)
Russian Federation

Alina V. Zabaeva, Student of the Faculty of General Medicine,

8, Bldg. 2, Trubetskaya St., Moscow, 119991



K. R. Karpov
Sechenov First Moscow State Medical University (Sechenov University)
Russian Federation

Kamran R. Karpov, Student of the Faculty of General Medicine,

8, Bldg. 2, Trubetskaya St., Moscow, 119991



S. A. Knapp
Sechenov First Moscow State Medical University (Sechenov University)
Russian Federation

Semen A. Knapp, Student of the Faculty of General Medicine,

8, Bldg. 2, Trubetskaya St., Moscow, 119991



A. I. Gamanilova
Sechenov First Moscow State Medical University (Sechenov University)
Russian Federation

Anastasia I. Gamanilova, Student of the Faculty of General Medicine,

8, Bldg. 2, Trubetskaya St., Moscow, 119991



A. A. Fetaliev
Russian University of Medicine (ROSUNIMED)
Russian Federation

Abduljalil A. Fetaliev, Student of the Faculty of General Medicine,

4, Dolgorukovskaya St., Moscow, 127006



D. M. Shakhaev
Russian University of Medicine (ROSUNIMED)
Russian Federation

Dzhabrail M. Shakhaev, Student of the Faculty of General Medicine,

4, Dolgorukovskaya St., Moscow, 127006



Kh. A. Abasova
Russian University of Medicine (ROSUNIMED)
Russian Federation

Khalisat A. Abasova, Student of the Faculty of General Medicine, 

4, Dolgorukovskaya St., Moscow, 127006



P. M. Sotavov
Russian University of Medicine (ROSUNIMED)
Russian Federation

Pasha M. Sotavov, Student of the Faculty of General Medicine, 

4, Dolgorukovskaya St., Moscow, 127006



References

1. Wong TY, Klein R, Klein BE, Tielsch JM, Hubbard L, Nieto FJ. Retinal microvascular abnormalities and their relationship with hypertension, cardiovascular disease, and mortality. Surv Ophthalmol. 2001;46(1):59–80. https://doi.org/10.1016/s0039-6257(01)00234-x.

2. Cheung CY, Tay WT, Mitchell P, Wang JJ, Hsu W, Lee ML et al. Quantitative and qualitative retinal microvascular characteristics and blood pressure. J Hypertens. 2011;29(7):1380–1391. https://doi.org/10.1097/HJH.0b013e328347266c.

3. Sabanayagam C, Shankar A, Koh D, Chia KS, Saw SM, Lim SC et al. Retinal microvascular caliber and chronic kidney disease in an Asian population. Am J Epidemiol. 2009;169(5):625–632. https://doi.org/10.1093/aje/kwn367.

4. Zeng Y, Duan J, Ge G, Zhang M. Therapeutic management of ocular ischemia in takayasu’s arteritis: a case-based systematic review. Front Immunol. 2022;12:791278. https://doi.org/10.3389/fimmu.2021.791278.

5. Wang J, Cheng X, Meng Z, Wang Y. Impact of total cerebral small vessel disease score on ophthalmic artery morphologies and hemodynamics. J Transl Med. 2023;21(1):65. https://doi.org/10.1186/s12967-023-03908-y.

6. Yan Y, Zhang X, Yang Y, Han L, Wang H, Hu J. Analysis and curative effect of ocular ischemic diseases caused by carotid artery stenosis. Exp Ther Med. 2013;5(5):1310–1314. https://doi.org/10.3892/etm.2013.979.

7. Iorga ER, Costin D. Vascular emergencies in neuro-ophthalmology. Rom J Ophthalmol. 2020;64(4):323–332. https://doi.org/10.22336/rjo.2020.54.

8. Kim Y, Sung M, Park S. Clinical features of ocular ischemic syndrome and risk factors for neovascular glaucoma. Korean J Ophthalmol. 2017;31(4):343–350. https://doi.org/10.3341/kjo.2016.0067.

9. Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018;2(3):158–164. https://doi.org/10.1038/s41551-018-0195-0.

10. Vaghefi E, Yang S, Hill S, Humphrey G, Walker N, Squirrell D. Detection of smoking status from retinal images;A Convolutional Neural Network study. Sci Rep. 2019;9:7180. https://doi.org/10.1038/s41598-019-43670-0.

11. Leung H, Wang JJ, Rochtchina E, Wong TY, Klein R, Mitchell P. Impact of current and past blood pressure on retinal arteriolar diameter in an older population. J Hypertens. 2004;22(8):1543–1549. https://doi.org/10.1097/01.hjh.0000125455.28861.3f.

12. Ma F, Su J, Shang Q, Ma J, Zhang T, Wang X et al. Changes in Ocular Hemodynamics after Carotid Artery Angioplasty and Stenting (CAAS) in Patients with Different Severity of Ocular Ischemic Syndrome. Curr Eye Res. 2018;43(2):266–272. https://doi.org/10.1080/02713683.2017.1390771.

13. Nusinovici S, Rim TH, Yu M, Lee G, Tham YC, Cheung N et al. Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk. Age Ageing. 2022;51(4):afac065. https://doi.org/10.1093/ageing/afac065.

14. Kasimova MS, Makhkamova DK, Hamraeva GH. Peculiarities of the course of ocular ischemic syndrome in chronic cerebrovascular iscemia. Oftalmologiya. 2013;10(3):63–67. (In Russ.) https://doi.org/10.18008/1816-5095-2013-3-63-67.

15. Hayreh SS, Zimmerman MB. Ocular arterial occlusive disorders and carotid artery disease. Ophthalmol Retina. 2017;1(1):12–18. https://doi.org/10.1016/j.oret.2016.08.003.

16. de Havenon A, Meyer C, McNally JS, Alexander M, Chung L. Subclinical Cerebrovascular Disease: Epidemiology and Treatment. Curr Atheroscler Rep. 2019;21(10):39. https://doi.org/10.1007/s11883-019-0799-1.

17. Terelak-Borys B, Skonieczna K, Grabska-Liberek I. Ocular ischemic syndrome – a systematic review. Medical Science Monitor. 2012;18(8):138–144. https://doi.org/10.12659/msm.883260.

18. Suhail S, Tallarita T, Kanzafarova I, Lau J, Mansukhani S, Olatunji S et al. Ocular Ischemic Syndrome and the Role of Carotid Artery Revascularization. Ann Vasc Surg. 2024;105:165–176. https://doi.org/10.1016/j.avsg.2023.12.098.

19. Park S, Choi N, Yang B, Park K, Lee J, Jung S et al. Risk and risk periods for stroke and acute myocardial infarction in patients with central retinal artery occlusion. Ophthalmology. 2015;122(11):2336–2343.e2. https://doi.org/10.1016/j.ophtha.2015.07.018.

20. Zhang X, Hao X, Wang C, Xie L. Incidence and risk factors for ocular ischemic syndrome in patients with complete internal carotid artery occlusion. 25 October 2021, Preprint (Version 1). https://doi.org/10.21203/rs.3.rs-984715/v1.

21. Huang M, Han N, Wu Y, Gao Z, Zhang Z, Huang S. Identification of risk factors associated with developing ocular ischemia syndrome in patients with carotid artery occlusion. 14 June 2019, Preprint (Version 1). https://doi.org/10.21203/rs.2.10311/v1.

22. Zhang L, Yuan M, An Z, Zhao X, Wu H, Li H et al. Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China. PLoS ONE. 2020;15(5):e0233166. https://doi.org/10.1371/journal.pone.0233166.

23. Rim TH, Lee G, Kim Y, Tham YC, Lee CJ, Baik SJ et al. Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms. Lancet Digit Health. 2020;2(10):526–536. https://doi.org/10.1016/S2589-7500(20)30216-8.

24. Neroev VV, Zaytseva OV, Petrov SYu, Bragin AA. Artificial intelligence in ophthalmology: the present and the future. Rossiiskii Oftal’mologicheskii Zhurnal. 2024;17(2):135–141. (In Russ.) https://doi.org/10.21516/2072-0076-2024-17-2-135-141.

25. Grassmann F, Mengelkamp J, Brandl C, Harsch S, Zimmermann ME, Linkohr B et al. A Deep Learning Algorithm for Prediction of AgeRelated Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography. Ophthalmology. 2018;125(9):1410–1420. https://doi.org/10.1016/j.ophtha.2018.02.037.

26. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016;316(22):2402–2410. https://doi.org/10.1001/jama.2016.17216.

27. Xiangyu Chen, Yanwu Xu, Damon Wing Kee Wong, Tien Yin Wong, Jiang Liu. Glaucoma detection based on deep convolutional neural network. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:715–718. https://doi.org/10.1109/EMBC.2015.7318462.

28. Makhkamova DК. Etiopathogenesis of ocular ischemic syndrome. Vestnik Oftalmologii. 2017;133(2):120–124. (In Russ.) https://doi.org/ 10.17116/oftalma20171332120-124.

29. Frolov MA, Sakhovskaya NA, Frolov AM, Pryamikov AD. Feature of Ocular-Ischemic Syndrome in Patients with Cardiovascular Pathology. Literature Review. Oftalmologiya. 2020;17(2):188–194. (In Russ.) https://doi.org/10.18008/1816-5095-2020-2-188-194.

30. Kim YD, Noh KJ, Byun SJ, Lee S, Kim T, Sunwoo L et al. Effects of Hypertension, Diabetes, and Smoking on Age and Sex Prediction from Retinal Fundus Images. Sci Rep. 2020;10(1):4623. https://doi.org/10.1038/s41598-020-61519-9.


Review

For citations:


Vakhrat’ian P.E., Popov S.O., Zabaeva A.V., Karpov K.R., Knapp S.A., Gamanilova A.I., Fetaliev A.A., Shakhaev D.M., Abasova Kh.A., Sotavov P.M. Risk factors associated with the emergence and progression of ocular ischemic syndrome in carotid artery stenosis. Ambulatornaya khirurgiya = Ambulatory Surgery (Russia). 2025;22(1):227-236. (In Russ.) https://doi.org/10.21518/akh2025-009

Views: 575


ISSN 2712-8741 (Print)
ISSN 2782-2591 (Online)