July 2010
Volume 51, Issue 7
Free
Cornea  |   July 2010
Biometry of the Cornea and Anterior Chamber in Chinese Eyes: An Anterior Segment Optical Coherence Tomography Study
Author Affiliations & Notes
  • Leonard H. Yuen
    From the Singapore National Eye Centre (SNEC), Singapore;
  • Mingguang He
    the Zhongshan Ophthalmic Centre, Guangzhou, China;
  • Tin Aung
    From the Singapore National Eye Centre (SNEC), Singapore;
  • Hla M. Htoon
    the Singapore Eye Research Institute (SERI), Singapore; and
  • Donald T. Tan
    From the Singapore National Eye Centre (SNEC), Singapore;
    the Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
  • Jodhbir S. Mehta
    From the Singapore National Eye Centre (SNEC), Singapore;
  • Corresponding author: Jodhbir S. Mehta, Singapore National Eye Centre, 11 Third Hospital Avenue, Singapore 168751; jodmehta@gmail.com
Investigative Ophthalmology & Visual Science July 2010, Vol.51, 3433-3440. doi:10.1167/iovs.09-4307
  • Views
  • PDF
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Leonard H. Yuen, Mingguang He, Tin Aung, Hla M. Htoon, Donald T. Tan, Jodhbir S. Mehta; Biometry of the Cornea and Anterior Chamber in Chinese Eyes: An Anterior Segment Optical Coherence Tomography Study. Invest. Ophthalmol. Vis. Sci. 2010;51(7):3433-3440. doi: 10.1167/iovs.09-4307.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Purpose.: To investigate the normative data of corneal and anterior segment biometric parameters and their associations in Chinese adults, for use in preoperative assessment for corneal and anterior segment surgery.

Methods.: This cross-sectional, population-based study included 750 subjects aged ≥50 years. The subjects underwent an ophthalmic examination including imaging with anterior segment optical coherence tomography (AS-OCT). Dimensions were subsequently measured with the Zhongshan Assessment Program (ZAP). Also measured was posterior corneal arc length (PCAL), a novel parameter defined as the arc distance between scleral spurs on the posterior border of the cornea. Correlations with age, sex, height, weight, body mass index (BMI), refractive sphere and cylinder, and intraocular pressure were also measured.

Results.: The subjects' mean age was 63.3 ± 7.9 years and 349 (46.5%) were men. Corneal parameters (mean ± SD) included PCAL (12.924 ± 0.544 mm), anterior chamber depth (ACD; 2.684 ± 0.309 mm), central corneal thickness (562.39 ± 31.85 μm), anterior chamber curvature (7.35 ± 0.37 mm), and posterior corneal curvature (6.65 ± 0.34 mm). A moderate correlation was observed between PCAL and ACD (R = 0.55, P < 0.001) and a poor correlation between PCAL and age, height, weight, and BMI. Multivariate analysis showed a significant association between PCAL and ACD, ACC, PCC, and cylinder (P < 0.05).

Conclusions.: In this Chinese population, PCAL was found to correlate moderately with ACD. The data may be useful for corneal and anterior segment procedures such as Descemet's stripping automated endothelial keratoplasty (DSAEK).

Accurate qualitative and quantitative information on the morphology and anatomy of the cornea and anterior segment enables the surgeon to make informed preoperative decisions in anterior segment surgery. Qualitative information can affect surgical outcome, for example, in selecting the appropriate-sized phakic intraocular lens (pIOL) or posterior chamber intraocular lens (PCIOL), 1,2 the sizing of corneal implant rings (Intacs; Addition Technology, Des Plaines, IL) to correct keratoconus, the assessment of LASIK flap thickness after keratorefractive surgery, 3 and the optimization of graft sizes for Descemet's stripping automated endothelial keratoplasty (DSAEK) surgery. Such information provides enhanced safety in surgery and reduced postoperative complications. 
The development of the anterior segment optical coherence tomography (AS-OCT, Visante; Carl Zeiss Meditec, Dublin, CA) has allowed a fast, noncontact method of imaging the cornea and anterior chamber 46 for objective quantification and evaluation. 7 With a single anterior segment image, the cornea, both angles (in one meridian including the scleral spur 5,6 ), the anterior portion of the lens, and the iris surface are imaged. It has been shown to be highly reproducible with respect to repeatable image acquisition and thus has excellent interobserver and intraobserver variability. 8  
A novel algorithm software that uses AS-OCT images, the Zhongshan Assessment Program (ZAP), offers a simple, user-friendly quantification of anterior segment anatomy 9 with high inter- and intraobserver agreement and repeatability. 9,10 A novel parameter that is measured and evaluated in this study is the posterior corneal arc length (PCAL), defined as the arc distance of the posterior corneal border between scleral spurs. Imaging of the anterior corneal surface has been described extensively in the literature; however, accurate data on the shape of the posterior surface of the cornea are limited, 11 perhaps due to the complexities of imaging this surface. To obtain the images, acquisition must be performed through the anterior surface of the cornea, which can act as a magnifying lens and distort the perceived shape of the posterior cornea. 
This study serves to describe the corneal and anterior segment dimensions of an ethnic population in Singapore and to document for the first time PCAL data in this population, which may be a predictive parameter for anterior chamber surgery. The information from the study will form baseline normative data for the assessment of any surgical intervention in this population group. 
Methods
Study Population and Recruitment
The study was approved by the Institutional Review Board of the Singapore Eye Research Institute and was performed in accordance with the tenets of the Declaration of Helsinki. In this population-based, cross-sectional study, subjects older than 50 years who did not have any ophthalmic complaints were recruited from a government-run community polyclinic providing primary health care services. This polyclinic serves more than 10,000 people per month, mainly of lower to middle socioeconomic status, with a high proportion requiring chronic disease management. The subjects were identified by systematic sampling (every fifth patient registered at the polyclinic) and asked to participate in the study after they provided written informed consent. A detailed questionnaire including demographic, socioeconomic details, and medical and ocular history was administered. The exclusion criteria were a history of glaucoma, prior intraocular surgery or penetrating eye injury, and corneal disorders, such as corneal dystrophy, corneal opacity, or pterygium that would prevent anterior chamber depth (ACD) measurement. 
Imaging
Images were collected by using AS-OCT at the Singapore National Eye Centre after informed consent was obtained from all participants. The details of the AS-OCT imaging technology have been described previously. 9,12 Briefly, a 1.3-μm infrared light is used to obtain high-resolution, cross-sectional tomographic images of the anterior segment. 57 The image is horizontally composed of 256 A-scans in 16 mm with 1024 points per A-scan at 8 mm of depth. Each image has a maximum transverse and axial resolution of 60 and 18 μm, respectively. Scanning at 2000 axial scans per second, the machine needs approximately ⅛ second to scan an eye. Images were taken directly from the machine's output function as 816 × 636 pixel JPEG (lossless compression) files. All selected images were temporal–nasal (i.e., horizontal) scans, to maximize visibility of anatomic location and repeatability. 9,10 Consecutive images of only the right eye were used to ensure uniformity with other studies 9,13 and to reduce bias. 
Image Processing
All AS-OCT images were assessed by one ophthalmologist (LY) and were processed with inbuilt software that dewarps the images (adjusting for distortions arising from corneal optical properties). The scleral spurs, defined as the anatomic junction between the inner wall of the trabecular meshwork and the sclera, were identified (Fig. 1). 13 There is a prominent inner extension of the sclera at its thickest part, 9,13 and in this study, it was defined as a change in curvature of the inner surface of the angle wall, often appearing as an inward protrusion of the sclera. For each image the image file was opened, and the two scleral spurs were identified on an individual scan. The algorithm then calculated all parameters and the information was recorded (Fig. 1). 
Figure 1.
 
Appearance of the ZAP software using AS-OCT images: for each image, the image file is opened, and the two scleral spurs are identified and marked. Calculate OCT then measures all parameters including the PCAL (in millimeters), as shown by the arrow in the right image.
Figure 1.
 
Appearance of the ZAP software using AS-OCT images: for each image, the image file is opened, and the two scleral spurs are identified and marked. Calculate OCT then measures all parameters including the PCAL (in millimeters), as shown by the arrow in the right image.
The ZAP (Guangzhou, China) software automatically extracted the 300 × 600 8-bit gray scale (intensities from 0 to 255) image portion of the output file and performed noise and contrast conditioning. 9 A binary copy of the image was then produced in which pixels were either 1's (tissue) or 0's (open space), depending on whether they were brighter or darker than a calculated threshold. Algorithms defined the borders of the corneal epithelium and endothelium and the anterior surface of the iris. The algorithms used basic edge arguments (five consecutive 0's above and five consecutive 1's below indicated an anterior surface point) to describe the borders. The corneal border data were fitted with polynomial curves and a line-smoothing algorithm that was explicitly defined by the edge-finding algorithms and used the derivative data to repair steplike portions of the border. Anatomic distances are calculated for anterior segment and corneal parameters: ACD, central corneal thickness (CCT), anterior and posterior corneal curvatures (ACC and PCC), and PCAL. All measurements are in millimeters except for CCT, which is in micrometers. 
Statistical Methods
Parametric and nonparametric tests were used to compare continuous variables according to data distribution. Spearman's rho correlation, linear regression, and logistic regression analyses were used to assess factors relating to the PCAL. P < 0.01 was considered statistically significant (Stata; StataCorp LP, College Station, TX, and Excel; Microsoft, Redmond, WA). 
Results
A total of 750 consecutive eyes from Chinese Singaporeans were included in the study. The mean age of the patients was 63.3 ± 7.9 (range, 50–90.0) years; 401 (53.4%) were women. The mean ± SD PCAL was 12.92 ± 0.54 mm, ACD 2.68 ± 0.31 mm, CCT 562.39 ± 31.85 μm, ACC 7.36 ± 0.37 mm, and PCC 6.65 ± 0.34 mm. Table 1 summarizes the demographics of the patients' corneal parameters, stratified by age group and sex. 
Table 1.
 
Measured Corneal and Anterior Segment Parameters Stratified by Age and Sex
Table 1.
 
Measured Corneal and Anterior Segment Parameters Stratified by Age and Sex
Age ACD (mm) CCT (μm) ACC (mm) PCC (mm) PCAL (mm) n
All Persons
    Mean 2.680 562.39 7.36 6.67 12.92 750
    SD 0.310 31.85 0.37 0.34 0.54
Men
    All Ages
        Mean 2.72 563.25 7.39 6.70 12.99 349
        SD 0.30 32.97 0.40 0.36 0.54
    50–59 years
        Mean 2.78 569.09 7.44 6.72 13.06 102
        SD 0.32 31.79 0.39 0.33 0.53
    60–69 years
        Mean 2.74 567.18 7.36 6.69 12.96 144
        SD 0.29 30.59 0.39 0.37 0.57
    ≥70 years
        Mean 2.65 551.96 7.37 6.69 12.97 103
        SD 0.28 34.80 0.42 0.38 0.52
    P for trend P = 0.007 P = 0.000 P = 0.249 P = 0.731 P = 0.35
Women
    All Ages
        Mean 2.65 561.64 7.32 6.61 12.86 401
        SD 0.31 30.87 0.35 0.31 0.54
    50–59 years
        Mean 2.73 564.91 7.33 6.64 12.97 191
        SD 0.31 31.65 0.34 0.31 0.54
    60–69 years
        Mean 2.60 561.39 7.32 6.60 12.80 146
        SD 0.32 31.89 0.35 0.31 0.54
    ≥70 years
        Mean 2.53 552.44 7.33 6.58 12.70 64
        SD 0.23 23.92 0.39 0.31 0.46
    P for trend P = 0.000 P = 0.019 P = 0.96 P = 0.325 P = 0.000
Posterior Corneal Arc Length
There was poor (r < 0.3) to moderately strong correlation (r = 0.6–0.8), 14 between PCAL and the other corneal parameters: ACD (r = 0.55, r 2 = 0.31, P < 0.001), CCT (r = −0.071, r2 = 0.005, P = 0.051), ACC (r = 0.114, r 2 = 0.013, P = 0.002), and PCC (r = 0.307, r2 = 0.026, P < 0.001; Fig. 2). Overall, there was a poor correlation between PCAL and age (r = −0.095, r 2 = 0.009, P < 0.01), height (R = 0.199, R2 = 0.04, P < 0.001), weight (R = 0.151, R 2 = 0.023, P < 0.001), and BMI (R = 0.048, R2 = 0.002, P = 0.186; Fig. 3). PCAL also showed poor correlation with asphericity, cylinder, and IOP (Fig. 4). Tables 2 and 3 summarize the relationship of PCAL in quartiles with measured corneal and anterior segment parameters. 
Figure 2.
 
Scatterplots with best fit regression line and confidence intervals between PCAL and corneal parameters: ACD (r = 0.55, P < 0.001), CCT (r = −0.071, P = 0.051), ACC (r = 0.114, P = 0.002), and PCC (r = 0.307, P < 0.001).
Figure 2.
 
Scatterplots with best fit regression line and confidence intervals between PCAL and corneal parameters: ACD (r = 0.55, P < 0.001), CCT (r = −0.071, P = 0.051), ACC (r = 0.114, P = 0.002), and PCC (r = 0.307, P < 0.001).
Figure 3.
 
Scatterplots with best fit regression line with confidence intervals between PCAL against age (r = −0.095, P = 0.01), height (r = 0.199, P < 0.001), weight (r = 0151, P < 0.001), and BMI (r = 0.048, P = 0.186), showing no strong correlations between the parameters.
Figure 3.
 
Scatterplots with best fit regression line with confidence intervals between PCAL against age (r = −0.095, P = 0.01), height (r = 0.199, P < 0.001), weight (r = 0151, P < 0.001), and BMI (r = 0.048, P = 0.186), showing no strong correlations between the parameters.
Figure 4.
 
Scatterplots with best fit regression line with confidence intervals between PCAL against sphere (D) (r = −0.156, P < 0.001), cylinder (D) (r = −0.103, P = 0.006), and IOP (mm Hg) (r = −0.063, P = 0.002).
Figure 4.
 
Scatterplots with best fit regression line with confidence intervals between PCAL against sphere (D) (r = −0.156, P < 0.001), cylinder (D) (r = −0.103, P = 0.006), and IOP (mm Hg) (r = −0.063, P = 0.002).
Table 2.
 
Relationship of PCAL (in quartiles) with Measured Corneal and Anterior Segment Parameters
Table 2.
 
Relationship of PCAL (in quartiles) with Measured Corneal and Anterior Segment Parameters
PCAL (mm) n ACD (mm) CCT (μm) ACC (mm) PCC (mm)
1st Quartile (≤ 12.13)
    Mean 190 2.46 566.20 7.31 6.53
    SD 0.24 31.68 0.37 0.33
2nd Quartile (12.13–12.92)
    Mean 185 2.64 561.90 7.31 6.60
    SD 0.26 32.24 0.38 0.33
3rd Quartile (12.92–13.68)
    Mean 188 2.73 560.50 7.39 6.68
    SD 0.27 31.30 0.41 0.32
4th Quartile (≥ 13.68)
    Mean 187 2.92 560.90 7.40 6.80
    SD 0.28 32.13 0.33 0.31
P for trend 0.000 0.277 0.010 0.000
Table 3.
 
Spearman Correlation (r) with PCAL as the Independent Variable
Table 3.
 
Spearman Correlation (r) with PCAL as the Independent Variable
PCAL ACD CCT ACC PCC Age Height Weight BMI Sphere Cylinder IOP
r 1.000 0.550** −0.071 0.114* 0.307* −0.095* 0.199* 0.151* 0.048 −0.156* −0.103* −0.063*
(P) (<0.001) (0.051) (0.002) (<0.001) −(0.01) (<0.001) (<0.001) (0.186) (<0.001) (0.006) (0.002)
Univariate and Multivariate Analyses
Table 4 shows univariate and multivariate analyses for PCAL with measured ocular, systemic, and refractive parameters. In the univariate analysis, only CCT, BMI, and IOP were not significantly associated with PCAL. (BMI was used for the analysis instead of height and weight because of collinearity.) 
Table 4.
 
Univariate and Multivariate Analyses
Table 4.
 
Univariate and Multivariate Analyses
PCAL Univariate Multivariate
Coefficient P Coefficient P
ACD 0.564 <0.001 0.541 <0.001
ACC 0.123 0.001 −0.291 <0.001
PCC 0.315 <0.001 0.497 <0.001
CCT −0.066 0.073 −0.103 <0.001
Sex −0.118 0.001 −0.023 0.457
Age −0.089 0.015 0.001 0.973
BMI 0.066 0.071 0.028 0.337
Sphere −0.156 <0.001 0.018 0.556
Cylinder −0.103 0.006 −0.085 0.004
IOP −0.063 0.085 −0.021 0.467
A multivariate analysis with PCAL as the dependent parameter showed statistical significance in comparison with ACD, ACC, PCC, CCT, and cylinder. 
Sex.
Logistic regression analyses showed that all corneal parameters were significantly smaller in the women, except for CCT, which was not significantly different between the men and the women (OR = 0.998, P = 0.492; Table 5). 
Table 5.
 
Differences in Parameters According to Sex
Table 5.
 
Differences in Parameters According to Sex
Parameter/Sex Mean SD Max Min β OR P
ACD, mm
    Male 2.72 0.30 3.54 1.99 −0.782 0.457 0.001*
    Female 2.65 0.32 3.65 1.88
CCT, μm
    Male 563.25 32.97 659.2 474 −0.002 0.998 0.492
    Female 561.64 30.87 663.9 478
ACC, mm
    Male 7.39 0.40 8.61 5.36 −0.475 0.622 0.018†
    Female 7.32 0.35 8.51 6.19
PCC, mm
    Male 6.7 0.36 7.95 4.8 −0.788 0.455 0.001*
    Female 6.61 0.31 7.95 5.52
PCAL, mm
    Male 12.99 0.54 14.36 11.33 −0.440 0.644 0.001*
    Female 12.86 0.54 14.44 11.41
Age, Height, and Weight.
The mean age of the patients was 63.3 ± 7.9 (range, 50–90.0) years, and the mean height and weight of the subjects was 160.2 ± 7.9 cm and 61.3 ± 10.7 kg, respectively. In the measured corneal and anterior segment parameters, there was a decreasing trend with increasing age, and an increasing trend with increasing height and weight, which was somewhat expected although not always statistically significant (Table 6). 
Table 6.
 
Correlation of Age, Height, and Weight with Measured Corneal and Anterior Segment Parameters
Table 6.
 
Correlation of Age, Height, and Weight with Measured Corneal and Anterior Segment Parameters
Age Height Weight
β P β P β P
ACD −0.006 <0.001* 0.178 <0.001* 0.169 <0.001*
ACC −0.54 0.483 0.132 <0.001* 0.003 0.010
PCC −0.44 0.609 0.007 <0.001* 0.096 0.008
CCT −0.04 <0.001* 0.031 0.402 0.070 0.054
PCAL −0.089 0.015† 0.195 <0.001* 0.162 <0.001*
Discussion
The biometric analysis of the anterior segment of the eye has become increasingly important in the decision-making and management of corneal and anterior segment procedures, ranging from pre- and postoperative evaluation of AC IOLs and phakic intraocular lenses (pIOLs), 15 to potentially optimizing the size of grafts for endothelial keratoplasty procedures such as DSAEK, where the donor cornea, adherent to the posterior stroma, protrudes into the AC and may also crowd the angle. This study was conducted to document and present the findings of corneal characteristics in Chinese eyes from Singapore and to record associations and correlations of ocular and systemic parameters with a novel parameter, PCAL. 
From our study, the mean PCAL was 12.92 mm (95% CI, 12.88–12.96 mm), with a median of 12.91 mm. There was a moderate correlation with ACD (R = 0.55, R 2 = 0.3185, P < 0.001). Table 7 shows the relationship between ACD in quartiles and other corneal parameters. Univariate and multivariate analyses also showed a positive association between the two parameters. In our study, a shallow ACD was found to be associated with older age, hyperopic refractive error, female sex, shorter body stature, which confirms previous population-based studies on various ethnic populations (including Chinese eyes) and hospital-based investigations. 1619  
Table 7.
 
Relationship of ACD (in Quartiles) with Measured Corneal and Anterior Segment Parameters
Table 7.
 
Relationship of ACD (in Quartiles) with Measured Corneal and Anterior Segment Parameters
ACD (mm) n PCAL (mm) CCT (μm) ACC (mm) PCC (mm)
1st Quartile ≤2.28
    Mean 189 12.55 559.2 7.33 6.59
    SD 0.48 30.6 0.39 0.36
2nd Quartile 2.28–2.68
    Mean 191 12.79 561.2 7.36 6.66
    SD 0.45 32.5 0.34 0.33
3rd Quartile 2.68–3.17
    Mean 184 13.05 565.04 7.38 6.67
    SD 0.48 30.90 0.42 0.36
4th Quartile ≥3.17
    Mean 186 13.32 564.30 7.34 6.68
    SD 0.44 33.20 0.34 0.36
P for trend 0.000 0.251 0.533 0.034
The linear relationship between the two variables, PCAL and ACD, can be calculated as y = 10.257 + 0.9939x, where y = PCAL and x = ACD. This positive relationship is visualized in Figure 5; the arc's positive relationship with the vertical distance between the posterior cornea and the anterior lens capsule is self-explanatory. We postulate that the perpendicular height between the posterior cornea and a horizontal line from scleral spur to scleral spur would provide a stronger and more robust correlation in this instance, as the PCAL also uses the scleral spurs as landmarks (Fig. 5). As this measurement was not part of the study, we believe the best estimator of PCAL at present would be the linear equation as just stated. 
Figure 5.
 
The vertical distance (solid line) between the posterior cornea and the lens (ACD). The ACD varies with the vertical location of the lens. Vertical dashed line: the perpendicular height between the posterior cornea and an imaginary horizontal dashed line from scleral spur to scleral spur (circles). It is postulated that this vertical height remains constant in relation to PCAL, which also uses the scleral spurs as landmarks.
Figure 5.
 
The vertical distance (solid line) between the posterior cornea and the lens (ACD). The ACD varies with the vertical location of the lens. Vertical dashed line: the perpendicular height between the posterior cornea and an imaginary horizontal dashed line from scleral spur to scleral spur (circles). It is postulated that this vertical height remains constant in relation to PCAL, which also uses the scleral spurs as landmarks.
There was a significant difference in PCAL between the sexes, with the men exhibiting larger dimensions than the women, 12.99 ± 0.54 and 12.86 ± 0.54 mm, respectively (P = 0.001). This difference highlights the importance of customization of corneal surgery in men and women. Furthermore, there was a significant inverse correlation with age and in the univariate and multivariate analyses (P < 0.01). Hence, the preoperative management of a younger man would be significantly different from that of an elderly female patient. This highlights a further need to customize surgical decisions to achieve optimal outcomes. 
Currently, there is no optimal selection of donor graft size for DSAEK. This variable is left for the individual surgeon to decide, based purely empirically on a peripheral 1- to 2-mm clearance of the donor margins from the corneal limbus without taking into account vertical clearance of the donor margins from the iris and chamber angle. One of the major advantages of DSAEK is to be able to transplant a larger surface area of endothelial cells compared with standard penetrating keratoplasty (PK). A 9.0-mm graft, conventionally used in DSAEK, 2022 transfers 26% more surface area of healthy donor endothelial cells than does the standard 8.0-mm graft more commonly used in PK. 2325 However, because of the meniscal configuration of the donor graft after ALTK or manual dissection, the thickened peripheral portions of the graft are at increased risk of touching and adhering to the adjacent iris/drainage angle, especially in Chinese eyes with shallower anterior chambers, and the resultant peripheral anterior synechiae (PAS) may contribute to raised IOP after surgery and also increase the risk of allograft rejection, similar to PAS formation at the graft–host junction in PK. A solution may be to design an algorithm using PCAL dimensions, subtracting a constant to attain the maximum size graft that may be safely implanted without angle compromise with respect to both diameter and thickness of graft. 
The mean radius of the ACC in our study was 7.36 mm (95% CI, 7.33–7.38), with a median of 7.33 mm. There was a significant difference between the sexes in our study: The mean ± SD for the men was 7.39 ± 0.40 mm and for the women, 7.32 ± 0.35 mm (P = 0.018). Dubbleman et al. 11 also showed a statistically significant difference between the sexes in a predominantly Caucasian population. 
The mean radius of the PCC in our study was 6.65 mm (95% CI, 6.63–6.68), with a median of 6.67 mm, which is comparable to the range published in the literature. The radius of the schematic Gullstrand eye is 6.8 mm, 26 whereas in the schematic eye of Le Grand and El Hage 27 and Liou and Brennan, 28 it is 6.5 and 6.4 mm, respectively. 
Although the dimensions are both of the posterior cornea, there was only a fair correlation between PCC and PCAL (R = 0.307, R 2 = 0.094, P < 0.001). As corneal curvatures are measured for the central 3 mm of the cornea, this measurement only provides the tangential point at 3 mm and not the entire arc length. This result also suggests that the deviation from the mean PCAL is accounted for by the variation beyond the central 3 mm of the cornea to the scleral spurs. 
There was a difference between the sexes in our study for posterior corneal curvatures, the mean ± SD for the men was 6.70 ± 0.36 mm and for the women, 6.61 ± 0.31 mm (P = 0.001). Dubbleman et al. 11 also showed a statistically significant difference between the sexes, the men measuring 6.60 ± 0.03 mm and the women 6.456 ± 0.03 mm (P < 0.01). The mean age in their study was 39 ± 14 years (cf. the mean age in our study, 63.3 ± 7.9 years). As far as we know, our results show the first record of a difference between the sexes in the corneal curvatures of Chinese eyes. 
Dubbleman et al. 11 found that ACC and PCC were not age-dependent (P = 0.97 and 0.26, respectively). Our results concur that there was no correlation of ACC and PCC with age (P = 0.483 and 0.609, respectively). 
There was no correlation or statistically significant association between CCT and PCAL, suggesting parameter independence. There was, however, a slight decrease in corneal thickness with increasing age, which was not observed in a previous study. Dubbelman et al. 11 also showed that the men had a slightly greater (∼3 μm) CCTs (581 μm) than did the women (578 μm); in our study the mean CCT in the men was 563.25 ± 32.97 μm and in the women, 561.64 ± 30.87 μm, which not a statistically significant difference. 
There was a weak correlation between age and the corneal parameters, with a tendency toward a decrease in corneal dimensions with increasing age that was statistically significant with PCAL (P = 0.01), ACD (P < 0.001), and CCT (P < 0.001; Tables 2, 3, 8). Univariate regression also showed a significant inverse association between age and PCAL, ACD, and CCT (Table 4). Regression analyses showed a significantly positive association between height and corneal parameters, except for CCT (P = 0.402). A significantly positive association was also found between weight and corneal parameters, except for CCT (P = 0.054). 
Table 8.
 
Spearman Correlation (r) of Measured Corneal and Anterior Segment Parameters (Excluding PCAL) with Systemic and Refractive Parameters
Table 8.
 
Spearman Correlation (r) of Measured Corneal and Anterior Segment Parameters (Excluding PCAL) with Systemic and Refractive Parameters
ACD CCT ACC PCC Age Height Weight BMI Sphere Cylinder IOP
ACD 1.000 0.050 −0.017 0.081* −0.173† 0.168† 0.144† 0.075* −0.355† 0.011 −0.079*
(P) (0.1730) (0.640) (0.026) (<0.001) (<0.001) (<0.001) (0.039) (<0.001) (0.780) (0.031)
CCT 0.05 1.000 −0.025 −0.002 −0.145† 0.028 0.052 0.046 −0.146† 0.31 −0.10
(P) (0.173) (0.491) (0.963) (<0.001) (0.451) (0.152) (0.209) (<0.001) (0.415) (0.78)
ACC −0.017 −0.025 1.000 0.802† −0.036 0.179† 0.105† 0.026 0.188 −0.021 −0.015
(P) (0.64) (0.491) (<0.001) (0.33) (<0.001) (0.004) (0.474) (0.245) (0.571) (0.69)
PCC 0.081* −0.002 0.802† 1.000 −0.017 0.181† 0.101† 0.026 0.037 −0.071 −0.1
(P) (0.026) (0.963) (<0.001) (0.648) (<0.001) (0.006) (0.475) (0.323) (0.06) (0.791)
In addition to the aforementioned parameters, univariate analysis showed a significant association between PCAL and asphericity and cylinder (Table 4). There was no significant association with CCT, IOP, and BMI. A multivariate analysis, with PCAL as the dependent parameter, showed that only cylinder was significantly associated. Further analysis on the horizontality of the cylindrical axes compared with the horizontality of the AS-OCT scans showed no statistical significance. Cylinders were also analyzed at the 30° and 60° planes (with corresponding opposite axes) and also showed no significant difference (P > 0.05). 
General systemic disorders may affect ocular surface and corneal physiology; however, the anatomic measurements in our cohort seemed to remain the same, with or without disease. Subgroup analyses showed no difference between the eyes of patients with and without diabetes, those with and without ischemic heart disease, and those with and without hypertension (Table 9). 
Table 9.
 
Subgroup Analyses of Eyes with or without Systemic Diseases
Table 9.
 
Subgroup Analyses of Eyes with or without Systemic Diseases
Diabetes vs. No Diabetes Hypertension vs. No Hypertension Ischemic Heart Disease vs. No IHD Sphericity (Excluding High Refractive Errors)
PCAL 0.108 0.217 0.322 0.799
ACD 0.438 0.909 0.696 0.493
CCT 0.082 0.884 0.615 0.995
ACC 0.302 0.795 0.166 0.731
PCC 0.457 0.342 0.678 0.635
The inclusion of patients with higher myopia in this study is important for the normative database in Singapore, and in the Chinese diaspora, as myopia is prevalent. For completeness, we performed exclusion analysis, removing high myopes (> −5.0 D) and hyperopes (>5.0 D), and found that there was no significant difference between this group and the original 750 eyes (P > 0.05). When myopes and hyperopes were then divided into two groups, except for PCAL, all parameters between the two groups were similar and showed no significant difference (Table 10). The sample for refractive sphere followed a normal distribution. 
Table 10.
 
Subgroup Analyses between Myopes and Hyperopes
Table 10.
 
Subgroup Analyses between Myopes and Hyperopes
Myopes Hyperopes P
PCAL 13.035 12.835 <0.001
ACD 2.664 2.696 0.236
CCT 562.775 562.236 0.849
ACC 7.369 7.352 0.599
PCC 6.675 6.647 0.366
As a community-based epidemiologic study, our analyses targeted AS-OCT images collected from one ethnicity, the Chinese. Although not necessarily fully representative of all Chinese populations globally, our cohort did include native Singaporean Chinese, immigrant mainland Chinese, and expatriate Chinese (e.g., first- or second-generation Singaporean Chinese) who had immigrated from abroad to Singapore, and our sample size of 750 subjects was reasonably large, enabling statistical comparison of other parameters, using one eye of each subject, to reduce bias. 
Other potential limitations of this study include the inability to detect the scleral spur, which has previously been reported. 7 The visibility of the scleral spur was not detected in images where the internal surface of the sclera formed a smooth continuous line (with no inward protrusion of the sclera or change in its curvature) or in images with suboptimal quality. The study was also limited in that the measurements were restricted to horizontal nasal–temporal AS-OCT scans, as these have been shown to be the most consistent with respect to obtaining high-quality images for the ZAP program to analyze. 13 AS-OCT imaging has been shown to be restricted by eyelid anatomy for vertical scans. The mean age of the study's population (63.3 ± 7.9 years) is relatively older than that of Dubbelman et al. 11 (39 ± 14 years) and of Pinero et al. 15 (32.82 ± 7.91 years) and therefore comparisons with these patient populations may not be entirely valid. Furthermore, our population targeted those who were 50 years old and older; hence, our results are not generalizable to younger patients. It is reassuring, however, to find similar analytic outcomes of parameters between the sexes and associations with systemic measurements. 
This article serves to provide normal values of corneal dimensions and a novel parameter, PCAL, for Chinese eyes. We believe that these data will be clinically applicable for the surgical management of posterior corneal procedures (e.g., DSAEK) and in the assessment of patients requiring other forms of anterior segment or corneal surgery, including anterior chamber or iris-supported phakic IOL surgery, IOL implantation in cataract surgery, or refractive intrastromal corneal implantation. 
Footnotes
 Supported by National Research Foundation Translational and Clinical Research Programme Grant TCR R621/42/2008.
Footnotes
 Disclosure: L.H. Yuen, None; M. He, None; T. Aung, Carl Zeiss Meditec (F); H.M. Htoon, None; D.T. Tan, None; J.S. Mehta, None
References
Alio JL . Advances in phakic intraocular lenses: indications, efficacy, safety and new designs. Curr Opin Ophthalmol. 2004;15:350–357. [CrossRef] [PubMed]
Elgohary MA Chauhan DS Dowler JG . Optical coherence tomography of intraocular lens implants and their relationship to the posterior capsule: a pilot study comparing a hydrophobic acrylic to a plate haptic silicone type. Ophthalmic Res. 2006;38:116–124. [CrossRef] [PubMed]
Avila M Li Y Song JC Huang D . High speed optical coherence tomography for management after laser in situ keratomileusis. J Cataract Refract Surg. 2006;32:1836–1842. [CrossRef] [PubMed]
Nolan WP Aung T Machin D . Detection of narrow angles and established angle closure in Chinese residents of Singapore: potential screening tests. Am J Ophthalmol. 2006;141:896–901. [CrossRef] [PubMed]
Radhakrishnan S Goldsmith J Huang D . Comparison of optical coherence tomography and ultrasound biomicroscopy for detection of narrow anterior chamber angles. Arch Ophthalmol. 2005;123:1052–1059. [CrossRef]
Radhakrishnan S Huang D Smith SD . Optical coherence tomography imaging of the anterior chamber angle. Ophthalmol Clinic North Am. 2005;18:375–381, vi. [CrossRef]
Dorairaj S Liebmann J Ritch R . Quantitative evaluation of anterior segment parameters in the era of imaging. Trans Am Ophthalmol Soc. 2007;105:99–110. [PubMed]
Muler M Dahmen G Porksen E . Anterior chamber angle measurement with optical coherence tomography: intraobserver and interobserver variability. J Refract Cataract Surg. 2006;32:1803–1808. [CrossRef]
Console J Sakata L Aung T Friedman D He M . Quantitative analysis of anterior segment optical coherence tomography images: the Zhongshan Angle Assessment Program. Br J Ophthalmol. 2008;92:1612–1616. [CrossRef] [PubMed]
Chan JB Huang EH Yuen LH . Reproducibility of cornea measurements in anterior segment OCT images analyzed with the Zhongshan Assessment Program (ZAP): poster presented at European Society of Cataract and Refractive Surgery (ESCRS), Barcelona, Spain, 2009.
Dubbelman M Sicam V van der Heijde G . The shape of the anterior and posterior surface of the aging human cornea. Vision Res. 2006;46:993–1001. [CrossRef] [PubMed]
Su DH Friedman DS See JL . Degree of angle closure and extent of peripheral anterior synechiae: an anterior segment OCT study. Br J Ophthalmol. 2008;92:103–107. [CrossRef] [PubMed]
Sakata L Lavanya R Friedman D . Assessment of the scleral spur in anterior segment optical coherence tomography images. Arch Ophthalmol. 2008;126:181–185. [CrossRef] [PubMed]
Chan YH . Correlational Analysis. Singapore Med J. 2003;44(12):614–619. [PubMed]
Pinero D Plaza A Alio J . Anterior segment biometry with 2 imaging technologies: very-high-frequency ultrasound scanning versus optical coherence tomography. J Cataract Refract Surg. 2008;34:95–102. [CrossRef] [PubMed]
Xu L Cao WF Wang YX . Anterior chamber depth and chamber angle and their associations with ocular and general parameters: The Beijing Eye Study. Am J Ophthalmol. 2008;145:929–936. [CrossRef] [PubMed]
Congdon NG Youlin Q Quigley H . Biometry and primary angle glaucoma among Chinese, white, and black populations. Ophthalmology. 1997;104:1489–1495. [CrossRef] [PubMed]
Devereux JG Foster PJ Baasanhu J . Anterior chamber depth measurement as a screening tool for primary angle closure glaucoma in an East Asian population. Arch Ophthalmol. 2000;118:257–263. [CrossRef] [PubMed]
Nolan WP See JL Chew PT . Detection of primary angle closure using anterior segment optical coherence tomography in Asian eyes. Ophthalmology. 2007;114:33–39. [CrossRef] [PubMed]
O'Brien P Lake D Saw V . Endothelial keratoplasty: case selection in the learning curve. Cornea. 2008;27:1114–1118. [CrossRef] [PubMed]
Gorovoy MS . Descemet-stripping automated endothelial keratoplasty. Cornea. 2006;25:886–889. [CrossRef] [PubMed]
Price MO Price FWJr . Descemet's stripping with endothelial keratoplasty: comparative outcomes with microkeratome-dissected and manually dissected donor tissue. Ophthalmology. 2006;113:1936–1942. [CrossRef] [PubMed]
Thompson RWJr Price MO Bowers PJ . Long term graft survival after penetrating keratoplasty. Ophthalmology. 2003;110:1396–1402. [CrossRef] [PubMed]
Bertelmann E Pleyer U Reick P . Risk factors for endothelial cell loss post-keratoplasty. Acta Ophthalmol Scand. 2006;84:766–770. [CrossRef] [PubMed]
Tan DTH Janardhanan P Zhou H . Penetrating keratoplasty in Asian Eyes; the Singapore Corneal Transplant Study. Ophthalmology. 2008;115:975–982. [CrossRef] [PubMed]
Atchison DA Smith G . Optics of the Human Eye. Oxford, UK: Butterworth-Heinemann; 2000;34–35, 166–167, 251–256.
Le Grand Y El Hage SG . Physiological Optics. Berlin: Springer-Verlag; 1980:65–67.
Liou HL Brennan NA . Anatomically accurate, finite model eye for optical modelling. J Opt Soc Am Opt Image Sci. 1997;14:1684–1695. [CrossRef]
Figure 1.
 
Appearance of the ZAP software using AS-OCT images: for each image, the image file is opened, and the two scleral spurs are identified and marked. Calculate OCT then measures all parameters including the PCAL (in millimeters), as shown by the arrow in the right image.
Figure 1.
 
Appearance of the ZAP software using AS-OCT images: for each image, the image file is opened, and the two scleral spurs are identified and marked. Calculate OCT then measures all parameters including the PCAL (in millimeters), as shown by the arrow in the right image.
Figure 2.
 
Scatterplots with best fit regression line and confidence intervals between PCAL and corneal parameters: ACD (r = 0.55, P < 0.001), CCT (r = −0.071, P = 0.051), ACC (r = 0.114, P = 0.002), and PCC (r = 0.307, P < 0.001).
Figure 2.
 
Scatterplots with best fit regression line and confidence intervals between PCAL and corneal parameters: ACD (r = 0.55, P < 0.001), CCT (r = −0.071, P = 0.051), ACC (r = 0.114, P = 0.002), and PCC (r = 0.307, P < 0.001).
Figure 3.
 
Scatterplots with best fit regression line with confidence intervals between PCAL against age (r = −0.095, P = 0.01), height (r = 0.199, P < 0.001), weight (r = 0151, P < 0.001), and BMI (r = 0.048, P = 0.186), showing no strong correlations between the parameters.
Figure 3.
 
Scatterplots with best fit regression line with confidence intervals between PCAL against age (r = −0.095, P = 0.01), height (r = 0.199, P < 0.001), weight (r = 0151, P < 0.001), and BMI (r = 0.048, P = 0.186), showing no strong correlations between the parameters.
Figure 4.
 
Scatterplots with best fit regression line with confidence intervals between PCAL against sphere (D) (r = −0.156, P < 0.001), cylinder (D) (r = −0.103, P = 0.006), and IOP (mm Hg) (r = −0.063, P = 0.002).
Figure 4.
 
Scatterplots with best fit regression line with confidence intervals between PCAL against sphere (D) (r = −0.156, P < 0.001), cylinder (D) (r = −0.103, P = 0.006), and IOP (mm Hg) (r = −0.063, P = 0.002).
Figure 5.
 
The vertical distance (solid line) between the posterior cornea and the lens (ACD). The ACD varies with the vertical location of the lens. Vertical dashed line: the perpendicular height between the posterior cornea and an imaginary horizontal dashed line from scleral spur to scleral spur (circles). It is postulated that this vertical height remains constant in relation to PCAL, which also uses the scleral spurs as landmarks.
Figure 5.
 
The vertical distance (solid line) between the posterior cornea and the lens (ACD). The ACD varies with the vertical location of the lens. Vertical dashed line: the perpendicular height between the posterior cornea and an imaginary horizontal dashed line from scleral spur to scleral spur (circles). It is postulated that this vertical height remains constant in relation to PCAL, which also uses the scleral spurs as landmarks.
Table 1.
 
Measured Corneal and Anterior Segment Parameters Stratified by Age and Sex
Table 1.
 
Measured Corneal and Anterior Segment Parameters Stratified by Age and Sex
Age ACD (mm) CCT (μm) ACC (mm) PCC (mm) PCAL (mm) n
All Persons
    Mean 2.680 562.39 7.36 6.67 12.92 750
    SD 0.310 31.85 0.37 0.34 0.54
Men
    All Ages
        Mean 2.72 563.25 7.39 6.70 12.99 349
        SD 0.30 32.97 0.40 0.36 0.54
    50–59 years
        Mean 2.78 569.09 7.44 6.72 13.06 102
        SD 0.32 31.79 0.39 0.33 0.53
    60–69 years
        Mean 2.74 567.18 7.36 6.69 12.96 144
        SD 0.29 30.59 0.39 0.37 0.57
    ≥70 years
        Mean 2.65 551.96 7.37 6.69 12.97 103
        SD 0.28 34.80 0.42 0.38 0.52
    P for trend P = 0.007 P = 0.000 P = 0.249 P = 0.731 P = 0.35
Women
    All Ages
        Mean 2.65 561.64 7.32 6.61 12.86 401
        SD 0.31 30.87 0.35 0.31 0.54
    50–59 years
        Mean 2.73 564.91 7.33 6.64 12.97 191
        SD 0.31 31.65 0.34 0.31 0.54
    60–69 years
        Mean 2.60 561.39 7.32 6.60 12.80 146
        SD 0.32 31.89 0.35 0.31 0.54
    ≥70 years
        Mean 2.53 552.44 7.33 6.58 12.70 64
        SD 0.23 23.92 0.39 0.31 0.46
    P for trend P = 0.000 P = 0.019 P = 0.96 P = 0.325 P = 0.000
Table 2.
 
Relationship of PCAL (in quartiles) with Measured Corneal and Anterior Segment Parameters
Table 2.
 
Relationship of PCAL (in quartiles) with Measured Corneal and Anterior Segment Parameters
PCAL (mm) n ACD (mm) CCT (μm) ACC (mm) PCC (mm)
1st Quartile (≤ 12.13)
    Mean 190 2.46 566.20 7.31 6.53
    SD 0.24 31.68 0.37 0.33
2nd Quartile (12.13–12.92)
    Mean 185 2.64 561.90 7.31 6.60
    SD 0.26 32.24 0.38 0.33
3rd Quartile (12.92–13.68)
    Mean 188 2.73 560.50 7.39 6.68
    SD 0.27 31.30 0.41 0.32
4th Quartile (≥ 13.68)
    Mean 187 2.92 560.90 7.40 6.80
    SD 0.28 32.13 0.33 0.31
P for trend 0.000 0.277 0.010 0.000
Table 3.
 
Spearman Correlation (r) with PCAL as the Independent Variable
Table 3.
 
Spearman Correlation (r) with PCAL as the Independent Variable
PCAL ACD CCT ACC PCC Age Height Weight BMI Sphere Cylinder IOP
r 1.000 0.550** −0.071 0.114* 0.307* −0.095* 0.199* 0.151* 0.048 −0.156* −0.103* −0.063*
(P) (<0.001) (0.051) (0.002) (<0.001) −(0.01) (<0.001) (<0.001) (0.186) (<0.001) (0.006) (0.002)
Table 4.
 
Univariate and Multivariate Analyses
Table 4.
 
Univariate and Multivariate Analyses
PCAL Univariate Multivariate
Coefficient P Coefficient P
ACD 0.564 <0.001 0.541 <0.001
ACC 0.123 0.001 −0.291 <0.001
PCC 0.315 <0.001 0.497 <0.001
CCT −0.066 0.073 −0.103 <0.001
Sex −0.118 0.001 −0.023 0.457
Age −0.089 0.015 0.001 0.973
BMI 0.066 0.071 0.028 0.337
Sphere −0.156 <0.001 0.018 0.556
Cylinder −0.103 0.006 −0.085 0.004
IOP −0.063 0.085 −0.021 0.467
Table 5.
 
Differences in Parameters According to Sex
Table 5.
 
Differences in Parameters According to Sex
Parameter/Sex Mean SD Max Min β OR P
ACD, mm
    Male 2.72 0.30 3.54 1.99 −0.782 0.457 0.001*
    Female 2.65 0.32 3.65 1.88
CCT, μm
    Male 563.25 32.97 659.2 474 −0.002 0.998 0.492
    Female 561.64 30.87 663.9 478
ACC, mm
    Male 7.39 0.40 8.61 5.36 −0.475 0.622 0.018†
    Female 7.32 0.35 8.51 6.19
PCC, mm
    Male 6.7 0.36 7.95 4.8 −0.788 0.455 0.001*
    Female 6.61 0.31 7.95 5.52
PCAL, mm
    Male 12.99 0.54 14.36 11.33 −0.440 0.644 0.001*
    Female 12.86 0.54 14.44 11.41
Table 6.
 
Correlation of Age, Height, and Weight with Measured Corneal and Anterior Segment Parameters
Table 6.
 
Correlation of Age, Height, and Weight with Measured Corneal and Anterior Segment Parameters
Age Height Weight
β P β P β P
ACD −0.006 <0.001* 0.178 <0.001* 0.169 <0.001*
ACC −0.54 0.483 0.132 <0.001* 0.003 0.010
PCC −0.44 0.609 0.007 <0.001* 0.096 0.008
CCT −0.04 <0.001* 0.031 0.402 0.070 0.054
PCAL −0.089 0.015† 0.195 <0.001* 0.162 <0.001*
Table 7.
 
Relationship of ACD (in Quartiles) with Measured Corneal and Anterior Segment Parameters
Table 7.
 
Relationship of ACD (in Quartiles) with Measured Corneal and Anterior Segment Parameters
ACD (mm) n PCAL (mm) CCT (μm) ACC (mm) PCC (mm)
1st Quartile ≤2.28
    Mean 189 12.55 559.2 7.33 6.59
    SD 0.48 30.6 0.39 0.36
2nd Quartile 2.28–2.68
    Mean 191 12.79 561.2 7.36 6.66
    SD 0.45 32.5 0.34 0.33
3rd Quartile 2.68–3.17
    Mean 184 13.05 565.04 7.38 6.67
    SD 0.48 30.90 0.42 0.36
4th Quartile ≥3.17
    Mean 186 13.32 564.30 7.34 6.68
    SD 0.44 33.20 0.34 0.36
P for trend 0.000 0.251 0.533 0.034
Table 8.
 
Spearman Correlation (r) of Measured Corneal and Anterior Segment Parameters (Excluding PCAL) with Systemic and Refractive Parameters
Table 8.
 
Spearman Correlation (r) of Measured Corneal and Anterior Segment Parameters (Excluding PCAL) with Systemic and Refractive Parameters
ACD CCT ACC PCC Age Height Weight BMI Sphere Cylinder IOP
ACD 1.000 0.050 −0.017 0.081* −0.173† 0.168† 0.144† 0.075* −0.355† 0.011 −0.079*
(P) (0.1730) (0.640) (0.026) (<0.001) (<0.001) (<0.001) (0.039) (<0.001) (0.780) (0.031)
CCT 0.05 1.000 −0.025 −0.002 −0.145† 0.028 0.052 0.046 −0.146† 0.31 −0.10
(P) (0.173) (0.491) (0.963) (<0.001) (0.451) (0.152) (0.209) (<0.001) (0.415) (0.78)
ACC −0.017 −0.025 1.000 0.802† −0.036 0.179† 0.105† 0.026 0.188 −0.021 −0.015
(P) (0.64) (0.491) (<0.001) (0.33) (<0.001) (0.004) (0.474) (0.245) (0.571) (0.69)
PCC 0.081* −0.002 0.802† 1.000 −0.017 0.181† 0.101† 0.026 0.037 −0.071 −0.1
(P) (0.026) (0.963) (<0.001) (0.648) (<0.001) (0.006) (0.475) (0.323) (0.06) (0.791)
Table 9.
 
Subgroup Analyses of Eyes with or without Systemic Diseases
Table 9.
 
Subgroup Analyses of Eyes with or without Systemic Diseases
Diabetes vs. No Diabetes Hypertension vs. No Hypertension Ischemic Heart Disease vs. No IHD Sphericity (Excluding High Refractive Errors)
PCAL 0.108 0.217 0.322 0.799
ACD 0.438 0.909 0.696 0.493
CCT 0.082 0.884 0.615 0.995
ACC 0.302 0.795 0.166 0.731
PCC 0.457 0.342 0.678 0.635
Table 10.
 
Subgroup Analyses between Myopes and Hyperopes
Table 10.
 
Subgroup Analyses between Myopes and Hyperopes
Myopes Hyperopes P
PCAL 13.035 12.835 <0.001
ACD 2.664 2.696 0.236
CCT 562.775 562.236 0.849
ACC 7.369 7.352 0.599
PCC 6.675 6.647 0.366
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×