Quantification of Macular Perfusion in Healthy Children Using Optical Coherence Tomography Angiography

DOI: https://doi.org/10.21203/rs.3.rs-856190/v1

Abstract

Background: Variations in vessel density (VD) patterns of the macula in children of different ages are unknown.

Methods: In this cross-sectional study, subjects with normal eyes aged 3–18 years were enrolled. The macula was scanned by optical coherence tomography angiography (OCTA). Four age groups as under 7 years, 7-10 years, 11-14 years and more than 14 years of age were defined. The influences of age, gender, and body mass index on VD were analyzed.

Results: A total of 108 normal eyes from 54 participants with a mean age of 10.9 years were enrolled in the study. At the superficial and deep retina capillary plexus (SCP and DCP) and choriocapillaris (CC), the mean VD of the fovea was 20.10%, 36.99%, and 75.67%, respectively; at parafovea, these measurements were 53.12%, 55.81%, and 69.76%, respectively; and at perifovea, these measures were 51.38%, 52.46%, and 73.47%, respectively. The median foveal avascular zone (FAZ) was 0.28 mm2. No significant differences were found between the superior-hemi and inferior-hemi VD of the studied plexuses. The VD at CC parafovea and perifovea differed significantly between groups. Neither age nor sex affected the VD of macular retina or CC. FAZ areas were different between genders.

Conclusions: No difference between eyes and genders in the retina and CC VD of macular area was noted. FAZ area was larger in male. While this study is not a longitudinal study but may give us a hint for the macular vascular development during puberty and clinical applications of OCTA in children. 

Introduction

Optical coherence tomography angiography (OCTA) is a relatively recent diagnostic modality that provides a rapid, noninvasive, high-resolution measurement of retinal and choroidal vascular layers and blood flow without the use of dye injection [1–3]. The ability to distinguish the retinal superficial (SCP) and deep (DCP) capillary plexus and analyze the various retinal vascular layers separately is a major advantage of OCTA over fluorescein angiography (FA) [4, 5].

It is necessary to consider the properties of vascular perfusion density in healthy children's eyes in order to identify pathological OCTA findings in pediatric retinal vascular disorders such as Coats' disease, hereditary exudative vitreoretinopathy, and others. Understanding the physiological changes that arise through infancy and adolescence, as well as gender discrepancies, is crucial in evaluating the pathophysiology of certain childhood retinal disorders. It is mysterious what physiological changes occur in the macula during the first decades of life. The aim of this research was to assess the structural and perfusion of macular region in observable 6×6 mm area at macular region. Despite its potential utility, vascular density (VD) data in normal children's eyes is limited, and clinical applications in children are also sparse [1, 6, 7]. Few research attempted to present children's normative data [6, 8, 9]. Thus, the objective of this research was to report macular microvascular measures in healthy Iranian pediatric individuals using OCTA analysis, as well as to assess the factors that may affect this quantitative data.

Methods

The research was conducted from April 2018 to May 2020 on healthy children who came to Farabi Eye Hospital for routine eye exams as well as students from two Tehran schools who were invited to participate in the programme. Institutional review board approval was obtained from Farabi Hospital Review Board-Tehran University of Medical Sciences (Tehran-Iran) for this cross-sectional study. It followed the tenets of the Declaration of Helsinki. A written informed consent was obtained from the participants. Fifteen eligible children or their parents did not provide informed consent and were thus excluded. In our research, we were able to achieve a sufficient signal-to-noise ratio and experienced no loss of long fixation in the majority of the children due to the fixation target and verbal incentive. Despite several scans, 5 younger children (11%) refused to comply. Because of the Corona days quarantine limitations, several of our female volunteers did not show up at the hospital.

The study included consecutive healthy children with a best-corrected visual acuity (BCVA) of 20/20, refractive error between − 3 and + 1 diopter and a spherical equivalent (SE) of -0.5 to + 0.5, and intraocular pressure (IOP) of less than 21 mmHg. Exclusion criteria were any condition precluding accurate retinal imaging, patient gesture, frequent blinking, and the history of prior migraine or any systemic, neurological, and ocular disease or syndromic condition, any media opacity, ophthalmic surgery or laser history, amblyopia, and any positive past medical, surgical and drug history and/or any trauma.

Patients were classified into four groups: those under the age of seven, those aged seven to ten, those aged eleven to fourteen, and those aged fourteen to eighteen. Full ophthalmologic examination including clinical history, past medical history, best-corrected visual acuity (BCVA, decimal scale), anterior segment biomicroscopy using a slit lamp, Goldmann applanation tonometry (or tonopen), and dilated pupil (1% tropicamide) ophthalmoscopy were performed.

Examinations and imaging were carried out on the same day in the morning working hours (8 up to 2 PM). OCTA images were obtained using the split-spectrum amplitude-decorrelation angiography algorithm (SSADA) on the AngioVue OCT-A system version 2018,0,0,18 (Optovue RTVue XR Avanti, California, USA). This device uses an 840 nm wavelength laser to capture 70,000 A-scans per second; 304 A-scans made up a B-scan, while 304 vertical (Y-FAST) and horizontal (X-FAST) lines were sampled in the scanning area to obtain a 3D data cube and eliminating any motion artifact.

Automated segmentation of SCP and DCP and choriocapillay (CC) was performed using the integral software algorithm which sets the inner margin of SCP at 3 µm below the internal limiting membrane (ILM) of the retina and the outer boundary at 15 µm beneath the inner plexiform layer (IPL), with the DCP at 15 µm beneath the IPL to 71 µm under the IPL. CC was determined as the area between 15 to 45 µm below the Bruch’s membrane.

VD was calculated by the software as the relative density of the flow (percentage) of the total studied area on the binary reconstructed images.[10] The fovea position is automatically determined by OCTA. The foveal region was outlined as a central circle with a diameter of 120 pixels (1.0 mm) and the parafoveal area was outlined as a 91-pixel wide ring around the foveal region (1.0 mm width).

For statistical analysis, the SCP, DCP and CC of foveal, parafoveal, perifoveal and whole image (WI) and superior (sup) and inferior (inf) half (hemi) data of parafoveal and perifoveal areas at macula on 3×3-mm scan and 6×6-mm volumetric scans, on the basis of the Early Treatment Diabetic Retinopathy Study (ETDRS) grid, were composed. The software automatically generated the VD, thickness, and area of the foveal avascular zone (FAZ) used in this analysis. FAZ area (in millimeters squared) measured in the 3×3-mm scan. Central macular thickness (CMT) was computed by an automated algorithm available in the machine.

Data from both eyes of all subjects was considered for analysis. Two ophthalmologists (FG and VH) performed the evaluation of the OCTA images. In OCTA-based quantitative tests, image quality is reported to be a possible confounder [[8, 11]]. As a result, all results in our study were adjusted for SSI, which was found to have a strong correlation with SCP, DCP, and CC density.

Statistical Analysis

All quantitative variables were reported as mean with standard deviation after evaluating the normality of distribution with the Kolmogorov- Smirnov test and histogram. The majority of continuous variables did not have a typical Gaussian distribution, and they have all been reported as median with the range. All statistical analyses were performed by statistical software (SPSS software Version 21; SPSS, Inc., Chicago, IL, USA). Kruskal-Wallis test and one-way analysis of variance (ANOVA) were performed for nonparametric and parametric comparison, respectively. Mann Whitney U test and post-hoc analysis (dunnett’s test) were used to compare the values between groups in nonparametric and parametric variables accordingly. In this study, collinearity for different variables was checked. P-values less than 0.05 were considered statistically significant.

Results

A total of 108 eyes of 54 individuals were analyzed in this study. Mean age of all subjects was 10.9 ± 3.9 years (range: 3–18 years). Seventy two percent (78 eyes) of participants were male with a mean age of 10.7 ± 4.0 years, and 28.8% (30 eyes) were female with a mean age of 11.4 ± 3.6 years old. Mean weight was 39.2 ± 15.3 kilograms and mean height was 137.8 ± 21.7 centimeters. Among the four studied groups, 26 eyes (24.1%) were in less than 7 years age group, 26 eyes (24.1%) in the 7–10 years age group, 35 eyes (32.4%) in the 11–14 years group, and 21 eyes (19.4%) were in the older than 14 years age group (Table 1).

Table 1

Demographic characteristics of normal children in different age groups.

Groups

Total

(108 eyes)

(M ± SD)

< 7

(26 eyes)

(M ± SD)

7–10

(26 eyes)

(M ± SD)

11–14

(35 eyes)

(M ± SD)

> 14

(21 eyes)

(M ± SD)

P-value

(%) OD

54 (50)

13 (24.1)

13 (24.1)

18 (33.3)

10 (18.5)

0.995

Sex-male (%)

78 (72.2)

20 (25.6)

20 (25.6)

25 (32.1)

13 (16.7)

0.637

BCVA (LogMAR)

0

0

0

0

0

-

Weight

39.25 ± 15.37

23.92 ± 5.23

31.61 ± 4.39

44.76 ± 6.82

62.00 ± 14.66

< 0.001

Height

137.80 ± 21.75

109.42 ± 10.38

133.38 ± 3.80

149.35 ± 10.22

163.38 ± 16.21

< 0.001
**BCVA: Best corrected visual acuity

 

CMT

CMT was 252.34 ± 24.64 µm and being mildly thicker in the male in comparison with the female (253.27 ± 30.11 vs. 250.61 ± 28.82; P = 0.528, Pearson Chi-square). The mean CMT in each group was 250.15 ± 28.17, 247.27 ± 23.44, 250.88 ± 18.00 and 271.44 ± 44.92 µm in the aging groups, consequently (P = 0.004, ANOVA).

VD

At the macular region, the median whole picture (WI) VD of SCP, DCP, and CC was 50.094.74, 50.636.75, and 70.75 7.21 percent, respectively. In the foveal region, VD of DCP was significantly higher than that in SCP (36.99 ± 8.82 vs. 20.10 ± 7.32%, P < 0.001), comparable with in the parafoveal area (54.52 ± 7.39 vs. 51.66 ± 6.88 %; P = 0.005) and perifovea area (51.70 ± 6.98 vs. 50.91 ± 4.61 %; P = 0.078 borderline).

VD of SCP and DCP of the foveal, parafoveal and perifoveal area were similar in males and females and right and left eyes (P < 0.05), and no difference in superior and inferior hemifield was observed at parafoveal and perifoveal areas of macula (P > 0.05).

In a post hoc analysis of variance (ANOVA) test and nonparametric tests (Kruskal-Wallis test), in SCP and DCP, the WI and especially inf-hemi of WI showed slight decrease after age 14. In evaluation the subsegmental changes, more changes in the parafoveal and perifoveal VD at inferior half were shown after age 14 (Table 2,3).

Table 2

Vascular density in superficial capillary plexus (SCP) of normal children in different age groups.

Groups

VD/

Reference

group

(median, %)

Total

(108 eyes)

< 7

(26 eyes)

7–10

(26 eyes)

11–14

(35 eyes)

> 14

(21 eyes)

P-value

Kruskal –Wallis test

WI

< 7

7–10

11–14

50.66

(21.04–56.74)

51.83

(21.04–56.74)

51.76

(44.09–55.29)

--*

51.17

(46.58–55.68)

--*

--*

49.53

(35.81–55.01)

-*

0.035*

0.008*

0.085

Wi-sup-hemi

< 7

7–10

11–14

51.01

(25.43–57.60)

51.67

(25.43–56.85)

50.77

(45.27–55.09)

--*

50.99

(46.38–55.60)

--*

--*

49.68

(32.97–57.60)

--*

< 0.001*

0.049

0.156

Wi-inf-hemi

< 7

7–10

11–14

50.79

(12.29–56.63)

48.99

(12.29–56.63)

50.79

(43.01–55.49)

--*

51.20

(39.01–56.37)

--*

--*

48.47

(39.01–54.03)

--*

0.013*

0.002*

0.034

Fovea

< 7

7–10

11–14

20.10

(± 7.32)

20.34

(± 8.29)

20.20

(± 7.20)

--*

20.31

(± 6.20)

--*

--*

18.26

(± 8.02)

--*

--*

--*

**0.863

Para-fovea

< 7

7–10

11–14

53.12

(5.82–59.68)

51.34

(5.82–59.68)

51.36

(26.58–57.91)

--*

53.88

(44.72–58.61)

--*

--*

51.14

(28.06–59.01)

--*

--*

0.014*

0.089

Para-sup- hemi

< 7

7–10

11–14

53.79

(10.76–65.85)

51.83

(25.43–65.85)

50.77

(45.27–55.09)

--*

50.99

(46.38–55.60)

--*

--*

49.68

(32.97–56.05)

--*

--*

0.047*

0.274

Para-inf- hemi

< 7

7–10

11–14

52.79

(0.89–58.97)

50.77

(0.89–50.39)

53.68

(10.95–58.66)

--*

53.15

(44.61–58.73)

--*

--*

50.41

(32. 79–58.97)

--*

--*

0.005*

0.060

Perifovea

< 7

7–10

11–14

51.38

(22.09–57.07)

52.17

(22.09–57.07)

51.56

(47.57–56.02)

--*

51.54

(46.74–56.18)

--*

--*

56.19

(41.03–55.39)

--*

0.024*

0.025*

0.171

Peri-sup- hemi

< 7

7–10

11–14

51.72

(30.82–58.25)

52.43

(30.82–58.25)

51.73

(46.77–55.34)

--*

51.38

(45.85–56.78)

--*

--*

50.43

(40.11–56.79)

--*

0.056*

--*

0.154

Peri-inf-hemi

< 7

7–10

11–14

51.49 (13.32–57.33)

52.14

(13.32–56.54)

51.34

(47.34–56.70)

--*

52.09

(46.33– 57.33)

--*

--*

49.67

(40.58–53.99)

--*

0.010*

0.011*

0.131

FAZ

0.28 (0.04–4.20)

0.27 (0.05–1.10)

0.28 (0.10–5.05)

0.29 (0.09–0.40)

0.28 (0.04–4.20)

0.228
* Mann-Whitney test
**Kruskal-Wallis Test


Table 3

Vascular density in deep capillary plexus (DCP) of normal children in different age groups.

Groups

VD/

Reference

group

(median, %)

Total

(108 eyes)

< 7

(26 eyes)

7–10

(26 eyes)

11–14

(35 eyes)

> 14

(21 eyes)

P-value

Kruskal –Wallis test

WI

< 7

7–10

11–14

51.15

(16.36–63.32)

50.23

(16.36–63.32)

51.41

(43.53–61.12)

--*

52.93

(42.62–61.51)

--*

--*

49.14

(28.51–58.65)

--*

0.054*

0.018*

0.300

WI-sup-hemi

< 7

7–10

11–14

50.91

(22.81–62.88)

50.66

(23.50–62.88)

51.07

(42.97–61.87)

--*

52.29

(42.58–61.36)

--*

--*

50.44

(22.81–60.14)

--*

--*

0.028*

0.379

WI-inf-hemi

< 7

7–10

11–14

51.38

(8.93–63.32)

50.33

(8.93–63.32)

51.83

(42.22–60.40)

--*

52.70

(42.66–61.65)

--*

--*

50.04

(39.95–57.20)

--*

0.036*

0.018*

0.186

Fovea

< 7

7–10

11–14

36.99

(± 8.82)

35.86

(± 10.73)

38.12

(± 10.81)

--*

37.79

(± 6.51)

--*

--*

34.84

(± 6.39)

--*

--*

--*

**0.290

Para-fovea

< 7

7–10

11–14

55.81

(6.75–64.83)

55.97

(6.75–64.83)

54.62

(33.28–62.90)

--*

56.34

(48.67–63.47)

--*

--*

51.14

(28.06–59.01)

--*

--*

0.035*

0.404

Para-sup-hemi

< 7

7–10

11–14

54.72

(7.01)

54.29

(9.66)

54.98

(5.20)

--*

56.00

(4.21)

--*

--*

52.49

(9.10)

--*

--*

--*

**0.519

Para-inf-hemi

< 7

7–10

11–14

55.35

(0.89–58.97)

50.77

(0.89–50.39)

53.68

(10.95–58.66)

--*

53.15

(44.61–58.73)

--*

--*

50.41

(32.79–58.97)

--*

--*

0.027*

0.420

Perifovea

< 7

7–10

11–14

52.46

(16.88–65.79)

51.37

(16.88–65.79)

52.93

(44.34–61.67)

--*

53.01

(44.56– 62.58)

--*

--*

50.62

(31.76–59.31)

--*

0.028*

0.007*

0.169

Peri-sup-hemi

< 7

7–10

11–14

51.64

(23.81– 67.10)

51.42

(23.86–67.10)

52.29

(42.51–61.80)

--*

52.11

(44.10–61.95)

--*

--*

50.61

(23.81–60.82)

--*

--*

0.026*

0.336

Peri-inf-hemi

< 7

7–10

11–14

51.73

(7.20)

49.82

(11.10)

53.35

(4.29)

--*

53.50

(4.75)

--*

--*

49.45

(6.19)

--*

0.008*

0.004*

0.060
* Mann-Whitney test
**Kruskal-Wallis Test

 

The most compacted VD of macular area was found at CC especially in the foveal area. At CC, the foveal VD decreased continually after age 11 (Mann-Whitney Test- Table 4). The increase in the VD of CC occurs more in perifoveal area after 7 years up to 14 years of age and after age 14 it tended to be decreased slightly. At the parafoveal area, VD of CC tended to increase after age 11 years and then after 14 years, a slight decrease in VD was observed. We found no association between age on each retinal capillary plexuses and CC after adjusting for gender and BMI.

Table 4

Choriocapillaris (CC) vascular density of normal children in different age groups.

Groups

VD/

Reference

group

(median, %)

Total

(108 eyes)

< 7

(26 eyes)

7–10

(26 eyes)

11–14

(35 eyes)

> 14

(21 eyes)

P-value

Kruskal –Wallis test

WI

< 7

7–10

11–14

70.75

(22.76–78.90)

69.00

(22.76–74.80)

73.40

(63.68–77.41)

0.019*

73.43

(67.26–78.90)

0.001*

--*

72.89

(62.68–76.76)

--*

--*

0.012*

0.001

WI-sup-hemi

< 7

7–10

11–14

73.01

(40.48–79.62)

70.07

(40.48–76.05)

73.44

(64.22–77.80)

< 0.17*

74.20

(66.73–79.62)

< 0.001*

--*

73.29

(63.72–78.25)

--*

--*

0.019*

0.001

WI-inf-hemi

< 7

7–10

11–14

72.40

(0.92–78.36)

68.28

(0.92–74.91)

73.72

(62.78–77.36)

0.027*

73.03

(64.82–78.20)

0.003*

--*

72.72

(56.39–75.36)

--*

--*

0.010*

0.001

Fovea

< 7

7–10

11–14

75.67

(21.48–83.42)

76.02

(21.48–80.80)

77.43

(39.89–83.42)

-*

75.49

(66.76–80.23)

--*

--*

73.95

(31.93–78.89)

--*

0.014*

0.004*

**0.417

Para-fovea

< 7

7–10

11–14

69.76

(9.61–80.95)

68.18

(9.61–76.43)

70.46

(57.64–80.22)

--*

72.69

(61.20–80.95)

0.017*

--*

69.43

(58.49–78.39)

--*

--*

0.005*

0.013

Para-sup-hemi

< 7

7–10

11–14

70.23

(18.11-80-51)

69.50

(18.11–76.25)

69.99

(58.47–79.53)

--*

72.76

(60.24–80.51)

0.037*

--*

69.18

(56.53–77.59)

--*

--*

0.019*

0.042

Para-inf-hemi

< 7

7–10

11–14

70.21

(1.10–81.38)

67.50

(1.10–76.61)

70.73

(46.22–80.90)

--*

71.87

(62.16–81.38)

0.011*

--*

69.50

(51.96–79.19)

--*

--*

0.007*

0.013

Perifovea

< 7

7–10

11–14

73.47

(22.92–80.03)

69.38

(22.92–75.31)

73.76

(63.22–77.87)

0.017*

74.07

(67.06– 80.03)

0.001*

--*

73.72

(63.63–78.11)

--*

--*

0.048*

0.001

Peri-sup-hemi

< 7

7–10

11–14

73.13

(44.91–78.94)

70.88

(44.91–76.61)

73.42

(63.97–77.93)

0.012*

74.79

(44.10–61.95)

< 0.001*

--*

73.77

(65.70–78.94)

--*

--*

0.027*

0.001

Peri-inf-hemi

< 7

7–10

11–14

73.28

(0.81–79.04)

68.86

(0.81–75.66)

74.29

(62.47–77.80)

< 0.025*

74.10

(65.33–79.04)

0.003*

--*

74.12

(57.65–77.29)

--*

--*

0.048*

0.002
* Mann-Whitney test
**Kruskal-Wallis Test


FAZ

In all participants the mean FAZ was 0.38 ± 0.60 mm2 being significantly higher in the female in comparison with the male (0.37 ± 0.71 vs. 0.32 ± 0.17, P = 0.013). The analysis showed no change in FAZ in the aging groups (P = 0.228, Mann Whitney) (Table 2). No inter-eye correlation was observed in the FAZ (P = 0.719, Spearman correlation). The results showed that there is no significant correlation between age and BMI with FAZ area (P > 0.05). There was no statistically significant correlation between FAZ and CMT (P = 0.274).

Correlations

In our sample, there was no significant association between right and left eye for any of the study parameters, as predicted (P > 0.05). In addition, we performed the entire analysis considering one eye per subject, and the results were similar. For example, OCTA parameters’ correlation coefficients for bilateral eyes were less than 0.3 for FAZ (r = 0.25, P = 0.719), VD of foveal SCP (r = -0.07, P = 0.84), foveal DCP (r = -0.22, P = 0.619) and foveal CC (r = -0.04, P = 0.669).

Correlations were evaluated after adjusting for the age, gender and Avg-SSI, and BMI and taking collinearity into account in multiple linear regression in these pediatric groups. The following results have been obtained:

CMT There was no correlation between CMT and any other parameter.

SCP-Fovea was significantly correlated with DCP-fovea (B=0.839, CI: 0.592-0.838, P<0.001).

DCP-Fovea was related with fovea FAZ (B=-0.181, CI: -3.980- -0.918, P=0.002), CMT (B=0.105, CI: 0.001- 0.066, P=0.046), SCP-fovea (B=0.634, CI: 0.612-0.876, P<0.001), DCP-perifovea (B=0.137, CI: 0.019-0.376, P=0.030), WI-CC (B=6.835, CI: 1.945-28.161, P=0.025), CC-WI-sup-hemi (B=-3.306, CI: -13.964- -0.929, P=0.026), and CC-WI-inf-hemi (B=-3.958, CI: -14.633- -0.914, P=0.027).

CC fovea was associated with age (B=-0.261, CI: 0.946- -0.349, P<0.001), DCP-fovea (B=-0.133, CI: 0.009- 0.267, P=0.036), SCP-parafovea (B=-0.181, CI: -3.980- -0.918, P=0.008), CC-WI-inf-hemi (B= 2.743, CI: 1.033-4.710, P=0.001), and CC-parafovea (B= 0.395, CI: 0.002-0.939, P=0.049).

FAZ was correlated with SCP-fovea (B=-0.424, CI: -0.052- -0.022, P<0.001), CC-WI (B= -1.841, CI: -0.486- -0.115, P=0.002), and CC-perifovea (B= 1.727, CI: 0.100-0.460, P=0.003).

Discussion

In groups of healthy pediatric subjects, we collected quantitative data on macular microvascular structure changes. FAZ and VD of SCP and DCP are unaffected by age or contralaterality. FAZ was greater in boys and could be impacted adversely by SCP-fovea and CC perfusion.

DCP-Fovea was found to be influenced by SCP-Fovea (and vice versa), SCP-perifovea, FAZ, and VD of CC. The fovea has the highest mean of VD at CC, and the age groups have more VD in the inferior hemifield of CC at the macular region. The only variables that were correlated with VD of foveal CC were WI-inf-hemi-CC and SCP-parafovea. Retinal and choroidal vascular changes may occur before structural changes. Many aspects of the microscopic and physiologic characteristics of the retina and choroid are now well understood in normal subjects thanks to OCTA. OCTA was used to explore pathophysiologic features of certain diseases [[12–16]]. Most previous studies using OCTA, however, mainly have evaluated normal adult patients [1, 4, 5, 9].

FAZ was reported to be 0.26–0.47 mm using different kinds of OCTA machines at 3 × 3-mm enface AngioScans in the kids between mean age of 8–12 years in different capillary layers. Hsu et al. recorded an FAZ of 0.35 ± 0.17 mm2 in their 89 eyes (mean age, 8.5 ± 5.3 years-Heidelberg OCTA instrument and MATLAB program by unknow size of evaluated field) [7]. Comparingly, Yilmaz et al reported the SCP-FAZ area to be 0.28 ± 0.09 mm2 and DCP-FAZ to be 0.38 ± 0.09 in 15 eyes from 15 normal children (a mean age of 8.6 ± 2.2 years- Nidek's RS-3000 in 3 × 3-mm) [17]. İçel et al stated SCP-FAZ as 0.3 ± 0.09 mm2 in 146 children eyes (the mean age of 11.27 ± 3 years- Nidek RS-3000 AngioScan in 3×3-mm) [18]. In our study, the mean FAZ area was 0.38 ± 0.60 mm2, considering a single layer measurement on 3×3-mm enface image (automatedly derived data from software).

The observed differences may be attributed to ethnic differences, sample size, different OCTA machine use, and different capillary layer FAZ measurements. While the estimation techniques were not equivalent, we could conclude that the FAZ areas of healthy children and adults are comparable. The FAZ area of adults has been found to be from 0.24–0.46 mm and more in DCP than SCP [4, 19–21].

The effect of aging on the size and shape of the FAZ is a hotly debated subject. OCTA has demonstrated in many studies that the scale and form of the FAZ in healthy adult populations increases with age [6, 22, 23]. Since our and other studies only included children, the aging impact on FAZ could not be detected in this age range [7, 19, 24].

The impact of sex on the FAZ area remains inconclusive. Some research results indicated that adult females had higher FAZ than males, which can also be seen in children [8, 19, 23, 25, 25, 26]. Other studies reported no significant difference between the sexes [18, 25, 26]. In our study, accounting our smaller number of female subjects, the median of FAZ was 0.24 mm (range 0.104–5.05) in male and 0.33 mm (range 0.035–1.083) in female. Population-based studies are needed to determine the true effect of sex on FAZ area in the future.

Unlike previous studies [6–8, 18, 26], our research found no correlation between FAZ and CMT. The difference may be attributed to unadjusted results to certain major confounders in previous studies.

Our multivariate analysis showed that FAZ was only correlated with VD of fovea-SCP and CC, especially perifoveal CC. Any 0.4 percent reduction in SCP-fovea and 1.81 percent reduction in CC-WI VD result in a 1mm increase in FAZ. Zhang et al disclosed the impact of sex, foveal retinal thickness, parafoveal retinal thickness, axial length, and SSI on FAZ in their cohort [8].

Using the Optovue Angioretina program, several researchers have tested the SCP and DCP in healthy adult subjects. In line with our results, some of these studies revealed no variations in VD between genders [7] whereas increased SCP-VD in males was reported in other studies [6].

In our study, the foveal region CC had the most VD compared to the other parts' VD in SCP, DCP, and CC. At the foveal part of CC, the VD increased up to age 10 and from 11 to 18 years a decreasing trend started. The parafoveal area of CC and mostly perifoveal area had increasing order up to 13 years of age and then mild decrease in each part was noted (P < 0.05). Similar to our results, Zhang et al showed that in the parafoveal region, the CC had the densest vascular flow compared to DCP and SCP (P < 0.001) [[8]]. Other studies have shown that as the FAZ area increases, the VD of SCP, DCP, and CMT decreases dramatically during aging [18].

Our study used 6×6-mm enface images for measurements to evaluate the macular region as a whole, while their study used 3×3-mm enface images, so the quantities are not equivalent. In a pediatric study, they found that neither retinal perfusion nor VD were affected by age [[6]]. They reported that CC perfusion density not retinal VD inversely is correlated with age, even after accounting for potentially confounding factors such as refractive error and race, but not adjusting for sex and the BMI of the children [6].

Iafe et al [22] analyzed adult macular VD in 70 subjects (mean 48 ± 20 years of age, range 9–88 years), Wang et al [27] in 105 Chinese healthy participants (age: 35.9 ± 13.8 years, range 17–64 years), Coscas et al [20] in 70 subjects (age: 48 ± 18 years, range 20–79 years), and Ghassemi et al [5] in 112 healthy cases (age: 36.39 ± 11.31 years, range 12–67 years). VD of SCP and DCP decreased with age in these and other similar studies on healthy adult eyes [28, 29]. However, only the CC-fovea was affected by age in our pediatric series, and aging changes could be observed in children aged 11 to 18 years. Our study was confined to pediatric patients, and hence we were unable to determine whether VD continues to decline throughout young adulthood or stabilizes.

Although we did not note any gender differences in VD of retina and CC at macular area, some significant differences were reported in the SCP between sexes in healthy children in other study [[25]]. On the other hand in adults, Coscas et al uncovered a higher retinal VD in females than in males > 60 years of age and attributed this difference to a slower vascular aging in females [20]. Other studies stated no gender-based variance in VD of retina in macular area [5].

The primary limitation of our study was the cross-sectional nature of the analysis and relatively small samples in age groups. Furthermore, we did not perform axial length measurements in our study that may have weakened our analysis. Although, we limited our cases to those with a small amount of refractive error and a spherical equivalent between − 0.5 and + 0.5 diopter, in which it does not seem to affect significantly the image magnification. The effect of ethnic variation on VD is not evaluated in this study. The flow projection artifact of the large vessels of the SCP onto deeper retinal layers may have been a concern and may have influenced the quantitative analysis of VD values in the DCP.

Conclusion

Using OCTA as a noninvasive and accurate method, we created a valid normative database of choroidal and retinal microvasculature in healthy Iranian children. VD is similar in both sexes and eyes in this study data analysis and foveal VD was shown to be stable over time in children and not correlated with BMI. Interestingly, only the CC was affected by age in our pediatric series, and it tend to be decreased at 14 to 18 years. This database could be useful to evaluate foveal and choroidal development and pathophysiologic characteristics. Future research, including longitudinal follow-up of children after birth using hand-held SD-OCTA devices, would provide more detail about the pattern in VD of retina CC and FAZ during normal childhood development. Computing the VD is a feasible value for quantitative evaluation of macular perfusion status with potential clinical applications.

Abbreviations

BMI: Body mass index,

CC: Choriocapillaris,

CMT: Central macular thickness,

DCP: Deep capillary plexus,

FAZ: Foveal avascular zone,

OCT: Optical coherence tomography,

SCP: Superficial capillary plexus,

VD: Vascular density,

WI: Whole image

Declarations

References

  1. de Carlo TE, Bonini Filho MA, Chin AT, et al (2015) Spectral-Domain Optical Coherence Tomography Angiography of Choroidal Neovascularization. Ophthalmology 122:1228–1238. https://doi.org/10.1016/j.ophtha.2015.01.029
  2. Jia Y, Bailey ST, Wilson DJ, et al (2014) Quantitative Optical Coherence Tomography Angiography of Choroidal Neovascularization in Age-Related Macular Degeneration. Ophthalmology 121:1435–1444. https://doi.org/10.1016/j.ophtha.2014.01.034
  3. Spaide RF, Klancnik JM, Cooney MJ (2015) Retinal vascular layers imaged by fluorescein angiography and optical coherence tomography angiography. JAMA Ophthalmol 133:45–50. https://doi.org/10.1001/jamaophthalmol.2014.3616
  4. Shahlaee A, Samara WA, Hsu J, et al (2016) In Vivo Assessment of Macular Vascular Density in Healthy Human Eyes Using Optical Coherence Tomography Angiography. Am J Ophthalmol 165:39–46. https://doi.org/10.1016/j.ajo.2016.02.018
  5. Ghassemi F, Fadakar K, Bazvand F, et al (2017) The Quantitative Measurements of Vascular Density and Flow Areas of Macula Using Optical Coherence Tomography Angiography in Normal Volunteers. Ophthalmic Surg Lasers Imaging Retina 48:478–486. https://doi.org/10.3928/23258160-20170601-06
  6. Borrelli E, Lonngi M, Balasubramanian S, et al (2019) MACULAR MICROVASCULAR NETWORKS IN HEALTHY PEDIATRIC SUBJECTS. Retina Phila Pa 39:1216–1224. https://doi.org/10.1097/IAE.0000000000002123
  7. Hsu ST, Ngo HT, Stinnett SS, et al (2019) Assessment of Macular Microvasculature in Healthy Eyes of Infants and Children Using OCT Angiography. Ophthalmology 126:1703–1711. https://doi.org/10.1016/j.ophtha.2019.06.028
  8. Zhang Y, Zhang B, Fan M, et al (2020) The vascular densities of the macula and optic disc in normal eyes from children by optical coherence tomography angiography. Graefes Arch Clin Exp Ophthalmol 258:437–444. https://doi.org/10.1007/s00417-019-04466-0
  9. Park SH, Cho H, Hwang SJ, et al (2020) Changes in the Retinal Microvasculature Measured Using Optical Coherence Tomography Angiography According to Age. J Clin Med 9:883. https://doi.org/10.3390/jcm9030883
  10. Nesper PL, Roberts PK, Onishi AC, et al (2017) Quantifying Microvascular Abnormalities With Increasing Severity of Diabetic Retinopathy Using Optical Coherence Tomography Angiography. Invest Ophthalmol Vis Sci 58:BIO307–BIO315. https://doi.org/10.1167/iovs.17-21787
  11. Rao HL, Pradhan ZS, Weinreb RN, et al (2017) Determinants of Peripapillary and Macular Vessel Densities Measured by Optical Coherence Tomography Angiography in Normal Eyes. J Glaucoma 26:491–497. https://doi.org/10.1097/IJG.0000000000000655
  12. Ghassemi F, Fadakar K, Berijani S, et al (2021) Quantitative assessment of vascular density in diabetic retinopathy subtypes with optical coherence tomography angiography. BMC Ophthalmol 21:82. https://doi.org/10.1186/s12886-021-01831-8
  13. Dimitrova G, Chihara E, Takahashi H, et al (2017) Quantitative Retinal Optical Coherence Tomography Angiography in Patients With Diabetes Without Diabetic Retinopathy. Investig Opthalmology Vis Sci 58:190. https://doi.org/10.1167/iovs.16-20531
  14. Agemy SA, Scripsema NK, Shah CM, et al (2015) RETINAL VASCULAR PERFUSION DENSITY MAPPING USING OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IN NORMALS AND DIABETIC RETINOPATHY PATIENTS. Retina 35:2353–2363. https://doi.org/10.1097/IAE.0000000000000862
  15. Chwiejczak K, Biswas S, Tsamis E, et al (2021) Macular fibrosis in Coats’ disease demonstrated by Optical Coherence Tomography Angiography - new data toward pathophysiology. Eur J Ophthalmol 1120672120986366. https://doi.org/10.1177/1120672120986366
  16. Hsu ST, Finn AP, Chen X, et al (2019) Macular Microvascular Findings in Familial Exudative Vitreoretinopathy on Optical Coherence Tomography Angiography. Ophthalmic Surg Lasers Imaging Retina 50:322–329. https://doi.org/10.3928/23258160-20190503-11
  17. Yilmaz I, Ocak OB, Yilmaz BS, et al (2017) Comparison of quantitative measurement of foveal avascular zone and macular vessel density in eyes of children with amblyopia and healthy controls: an optical coherence tomography angiography study. J AAPOS Off Publ Am Assoc Pediatr Ophthalmol Strabismus 21:224–228. https://doi.org/10.1016/j.jaapos.2017.05.002
  18. İçel E, Yılmaz H, Uçak T, et al (2020) Evaluation of the Optic Disc and Macula in Healthy Children Using Optical Coherence Tomography Angiography. Turk J Ophthalmol 50:228–233. https://doi.org/10.4274/tjo.galenos.2020.85282
  19. Ghassemi F, Mirshahi R, Bazvand F, et al (2017) The quantitative measurements of foveal avascular zone using optical coherence tomography angiography in normal volunteers. J Curr Ophthalmol 29:293–299. https://doi.org/10.1016/j.joco.2017.06.004
  20. Coscas F, Sellam A, Glacet-Bernard A, et al (2016) Normative Data for Vascular Density in Superficial and Deep Capillary Plexuses of Healthy Adults Assessed by Optical Coherence Tomography Angiography. Investig Opthalmology Vis Sci 57:OCT211. https://doi.org/10.1167/iovs.15-18793
  21. Kuehlewein L, Tepelus TC, An L, et al (2015) Noninvasive Visualization and Analysis of the Human Parafoveal Capillary Network Using Swept Source OCT Optical Microangiography. Invest Ophthalmol Vis Sci 56:3984–3988. https://doi.org/10.1167/iovs.15-16510
  22. Iafe NA, Phasukkijwatana N, Chen X, Sarraf D (2016) Retinal Capillary Density and Foveal Avascular Zone Area Are Age-Dependent: Quantitative Analysis Using Optical Coherence Tomography Angiography. Invest Ophthalmol Vis Sci 57:5780–5787. https://doi.org/10.1167/iovs.16-20045
  23. Tan CS, Lim LW, Cheong KX, et al (2016) Measurement of Foveal Avascular Zone Dimensions and its Reliability in Healthy Eyes Using Optical Coherence Tomography Angiography. Am J Ophthalmol 165:201–202. https://doi.org/10.1016/j.ajo.2016.02.035
  24. Mirhajianmoghadam H, Jnawali A, Musial G, et al (2020) In vivo assessment of foveal geometry and cone photoreceptor density and spacing in children. Sci Rep 10:8942. https://doi.org/10.1038/s41598-020-65645-2
  25. Niestrata-Ortiz M, Fichna P, Stankiewicz W, Stopa M (2019) Sex-Related Variations of Retinal and Choroidal Thickness and Foveal Avascular Zone in Healthy and Diabetic Children Assessed by Optical Coherence Tomography Imaging. Ophthalmol J Int Ophtalmol Int J Ophthalmol Z Augenheilkd 241:173–178. https://doi.org/10.1159/000495622
  26. Samara WA, Say EAT, Khoo CTL, et al (2015) CORRELATION OF FOVEAL AVASCULAR ZONE SIZE WITH FOVEAL MORPHOLOGY IN NORMAL EYES USING OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY. Retina Phila Pa 35:2188–2195. https://doi.org/10.1097/IAE.0000000000000847
  27. Wang Q, Chan S, Yang JY, et al (2016) Vascular Density in Retina and Choriocapillaris as Measured by Optical Coherence Tomography Angiography. Am J Ophthalmol 168:95–109. https://doi.org/10.1016/j.ajo.2016.05.005
  28. Arrigo A, Aragona E, Capone L, et al (2018) Advanced Optical Coherence Tomography Angiography Analysis of Age-related Macular Degeneration Complicated by Onset of Unilateral Choroidal Neovascularization. Am J Ophthalmol 195:233–242. https://doi.org/10.1016/j.ajo.2018.08.001
  29. Yoshikawa Y, Shoji T, Kanno J, et al (2018) Optic disc vessel density in nonglaucomatous and glaucomatous eyes: an enhanced-depth imaging optical coherence tomography angiography study. Clin Ophthalmol Auckl NZ 12:1113–1119. https://doi.org/10.2147/OPTH.S167222