Retinal vessel multifractals predict pial collateral status in patients with acute ischemic stroke

• Published in: PloS one Vol. 17; no. 5; p. e0267837
• Main Authors: Khan, Adnan, De Boever, Patrick, Gerrits, Nele, Akhtar, Naveed, Saqqur, Maher, Ponirakis, Georgios, Gad, Hoda, Petropoulos, Ioannis N, Shuaib, Ashfaq, Faber, James E, Kamran, Saadat, Malik, Rayaz A
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.05.2022; Public Library of Science (PLoS)
• Subjects: Algorithms; Angiography; Biology and Life Sciences; Blood flow; Blood pressure; Blood vessels; Brain Ischemia - diagnostic imaging; Care and treatment; Cerebral Angiography - methods; Cerebral blood flow; Cholesterol; Collateral Circulation - physiology; Computed tomography; Computer and Information Sciences; CT imaging; Evaluation; Fractal geometry; Fractals; High density lipoprotein; Humans; Infarction, Middle Cerebral Artery; Ischemia; Ischemic Stroke; Low density lipoprotein; Machine learning; Medicine and Health Sciences; Occlusion; Physical Sciences; Research and Analysis Methods; Retina; Retinal Vessels - diagnostic imaging; Retrospective Studies; Stroke; Stroke (Disease); Stroke - diagnostic imaging; Support vector machines; Tomography; Tortuosity; Triglycerides; Veins & arteries; Belgium
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0267837

Pial collateral blood flow is a major determinant of the outcomes of acute ischemic stroke. This study was undertaken to determine whether retinal vessel metrics can predict the pial collateral status and stroke outcomes in patients. Thirty-five patients with acute stroke secondary to middle cerebral artery (MCA) occlusion underwent grading of their pial collateral status from computed tomography angiography and retinal vessel analysis from retinal fundus images. The NIHSS (14.7 ± 5.5 vs 10.1 ± 5.8, p = 0.026) and mRS (2.9 ± 1.6 vs 1.9 ± 1.3, p = 0.048) scores were higher at admission in patients with poor compared to good pial collaterals. Retinal vessel multifractals: D0 (1.673±0.028vs1.652±0.025, p = 0.028), D1 (1.609±0.027vs1.590±0.025, p = 0.044) and f(α)max (1.674±0.027vs1.652±0.024, p = 0.019) were higher in patients with poor compared to good pial collaterals. Furthermore, support vector machine learning achieved a fair sensitivity (0.743) and specificity (0.707) for differentiating patients with poor from good pial collaterals. Age (p = 0.702), BMI (p = 0.422), total cholesterol (p = 0.842), triglycerides (p = 0.673), LDL (p = 0.952), HDL (p = 0.366), systolic blood pressure (p = 0.727), HbA1c (p = 0.261) and standard retinal metrics including CRAE (p = 0.084), CRVE (p = 0.946), AVR (p = 0.148), tortuosity index (p = 0.790), monofractal Df (p = 0.576), lacunarity (p = 0.531), curve asymmetry (p = 0.679) and singularity length (p = 0.937) did not differ between patients with poor compared to good pial collaterals. This is the first translational study to show increased retinal vessel multifractal dimensions in patients with acute ischemic stroke and poor pial collaterals. A retinal vessel classifier was developed to differentiate between patients with poor and good pial collaterals and may allow rapid non-invasive identification of patients with poor pial collaterals.