Discussion
We analyzed the refractive errors and ocular biometric changes over time
in a Turkish population at the onset age ranges of myopia over time.
Regarding onset ages of cohort, there were significant differences
between onset biometric parameters and age. Accordingly, the increased
onset age was caused to significant increase ACD (p = 0.002) and CCT (p
= 0.044) whilst it was caused to significant decrease in LT (p = 0.011).
Regarding consecutive follow-ups, there were significant differences in
AL (p = 0.045), ACD (p <0.001) and LT (p = 0.026). In younger
onset age, the progression rate of AL and ACD were higher than older
onset age whereas, the regression rate LT was lower. Interestingly,
these significant differences among biometric measurements and final age
were lost at final visits (p>0.05). It was noted that only
myopia prevalence was increased in observation time periods. Regarding
the Cox regression results, myopic SE was significantly correlated by
age (HR: 0.73, 95% CI: 0.57-0.92, β = -0.32, p = 0.009), AL (HR: 4.55,
95% CI:2.87-7.24, β= 1.52, p<0.001), and
Kmean (HR: 2.04, 95% CI:1.55-2.67, β = 0.71, p
< 0.001). In the cohort study, factors correlating the average
SE at the end of three years were evaluated by logistic regression. The
onset data of SE (β=0.916, p<0.001), AL (β = -0.451, p
< 0.001), ACD (β = 0.430, p = 0.005), and Kmean (β = -0.172,
p<0.001) were found to be significantly associated with the
mean SE at the final data and therefore, the null hypothesis was
rejected.
As an output of the statistical analyses of this study, an equation was
proposed using the logistic regression model for calculating the
estimated SE following three years. The proposed equation or calculator
uses the onset SE, ACD, AL, and Kmean parameters as
input to calculate estimated SE following three years. Regarding the
proposed equation, the output is positively (increasingly) impacted by
the onset SE and ACD, while it is negatively (decreasingly) impacted by
the onset AL and Kmean.
Physiologically, biometric parameters such as AL, ACD, LT, and corneal
power could affect the steadily refractive condition of the
eye.12 Correspondingly, the onset of myopia or its
progression could be seen during the enlargement of AL which is not able
to be tolerated naturally.4-6 More specifically, 5-15
aged children are considered as the onset age of
myopia.13,14 It has been reported that observing the
changeover of optic biometric values on school-age children up to
adolescence is an ideal method for interpreting the initiation of
myopia.9 Besides, it has been reported that the
increasing onset age of young patients could be one of the significant
variables correlating clinical outcomes in a previous
report.13 Hence, the proposed equation in this study
should be considered as specific for the children between the ages of
7-12. When it is employed to younger ages or older ages than the cohort,
the progression cannot estimate via the proposed calculator due to
unique developmental mechanisms.15,16
In a previous report, it has been reported that potential myopia
calculators could be beneficial tools to reflect the average potential
outcome.10 In addition, it has been reported that such
tool built-on the data should be collected from cases examined for
between 2 and 5 years.10 In agreement with the
previous report, the underlying data of the proposed calculator were
collected from cases examined for 3 years. For the first time, the
logistic regression was employed to propose an SE calculator in 7-12
aged children. Regarding the proposed calculator, associated biometric
variables were identified as the onset values of SE, ACD, AL, and
Kmean. The input parameters consisted of the department
database records collected from registered instruments (Topcon RM-A7000B
and Lenstar LS900). Hence, data variability might be possible when the
proposed calculator is validated with different optical biometry
instruments due to the incompatibility phenomenon. The rationale of this
phenomenon, the low in agreement status in interchangeably used biometry
instruments has been also reported in a previous
study.17
The environmental factors regarding the onset of myopia in children are
the outcomes of close-up physical activities.18,19However, this retrospective study cannot standardize individual
environmental factors. The retrospective design of this study was
considered a limitation of the study. As the nature of a retrospective
study, selection bias could occur unintentionally among the patients.
The present study described the calculation of estimated refractive
error after 3 years using baseline biometric values in primary school
children. Only patients with consecutive measurements for three years
were included. Consequently, we found that the 3-year estimation of the
refractive error correlated regularly with initial parameters in 7-12
aged children. Regarding the proposed logistic model, it could be used
for patients with developing or progressing myopia as scheduling
follow-ups and treatment planning purposes. Also, in patients with an
estimation of fewer refractive error, the follow-ups could be made less
often those. With this motivation, further prospective studies are
needed to validate the proposed logistic regression equation in
follow-up patients.
In this cohort study, data from Eskisehir-city and surroundings were
analyzed therefore, the outcomes might not reflect the general Turkish
population or other nations. However, the primary purpose of this study
was not to conduct a demographic study.
It has been reported normative values for the axial length can be used
to monitor eye growth in European children at both 6 and 9 years of age
(N = 6934) in a previous study.20 Similarly, Sanz Diez
et al.21 have reported a clinical model for the
prediction of myopia development based on the creation of percentile
curves of axial length in school-aged children from Wuhan in central
China (N=12,554). Very recently, Truckenbrod et al.22have reported correlation curves for axial length by spherical
equivalent, age, or gender in German children between 2-18 years of age
(N=1965). In addition, the authors have concluded that the percentile
curves of axial length can be used as a predictive measure for the
likelihood of developing as well as the progression of
myopia.22 In agreement with the previous reports, the
axial length was one of the biometric variables in our generated model
in the present study. Sanz Diez et al .21 have
used the successive measurements of 226 children to verify the
predictive power of the axial length growth percentile curves. In
agreement with the previous report, data validation was performed on the
dataset of included children (N=197, 100%) in this study. The equation
or the calculation model was firstly generated in this study. Though
less sample size was used relatively from the previous reports, the
model was validated with our data set.
Within the limitation of this cohort study, we can conclude as follows:
- The proposed equation with this study can provide a foresight about
refractive errors three years after in school-age children aged 7-12
years based on identified optical biometric measurements. Knowing about
the level of refractive errors might act as guidance to assist for this
process management and creating a prevention strategy.
- This study assessed the onset of myopia and its progression correlated
by optical biometric measurements in 7-12 aged children. This
information and the model may as a reference for future comparative
studies to control myopia progression.