Zhaoqing Myopia Study

• Conditions: Myopia

• Registration Number: NCT04219228
• Lead Sponsor: Sun Yat-sen University

Brief Summary

Myopia is a common cause of vision loss, being particularly prevalent in children in East and Southeast Asia. The investigators will assess prevalence and incidence of myopia, identify digital biomarkers associated with myopia, and validate algorithms for the detection and/or predition of myopia and other ocular abnormalities in school-aged children in both urban and rural settings in Southern China.

Detailed Description

Myopia is a common cause of vision loss, being particularly prevalent in East and Southeast Asia. It is still not entirely clear whether and how visual experience in an urban environment with less outdoor exposure could have an impact on the development and progression of myopia. Zhaoqing has a relatively stable population of 4,084,600, which are representative of the Chinese population in term of demographic and socioeconomic characteristics.

Therefore, the investigators will conduct a longitudinal cohort study in both urban and rural settings to examine prevalence and incidence of myopia, identify digital biomarkers associated with myopia, and validate algorithms for the detection and/or predition of incidence and progression of myopia and other ocular abnormalities in school-aged children in Zhaoqing.

Study Timeline

• Study Start: 2019-12-14
• Study First Submitted: 2019-12-28
• Study First Submitted QC: 2020-01-03
• Study First Posted: 2020-01-06
• Status Verified: 2020-03
• Last Update Submitted: 2020-03-13
• Last Update Posted: 2020-03-17
• Primary Completion: 2023-02-02
• Study Completion: 2023-02-02

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 4000

Inclusion Criteria

• All first-grade students from 10 primary schools in urban counties, and from 10 primary schools in rural counties, Zhaoqing city.

Exclusion Criteria

• No.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Incident myopia	3 years	Incident myopia is defined as myopia detected during follow up among those without myopia at baseline. Myopia is defined as any eye's SER (sphere + 1/2 cylinder) of at least -0.5 diopters (D).

Secondary Outcome Measures

Name	Time	Method
Post-vision screening referral uptake	3 months	Any referral uptake will be confirmed by telephone follow-up.
Prevalence of myopia	baseline	Myopia is defined as any eye's SER (sphere + 1/2 cylinder) of at least -0.5 diopters (D).
Area under the receiver operating characteristic curve of the deep learning algorithm for the prediction of fast progressing myope	1 year	The investigators will estimate the area under the receiver operating characteristic curve of the deep learning algorithm for the prediction of fast progressing myope (a change in SER of 0.75D or more per year).
Area under the receiver operating characteristic curve of the diagnostic algorithm in identifying abnormal vision screening result	baseline	The investigators will estimate the area under the receiver operating characteristic curve of the diagnostic algorithm in identifying abnormal vision screening result (e.g., abnormal eye lid, abnormal cornea, and strabismus detected with mobile devices).
Change in axial length	1 year, 2 years, 3 years	Axial length will be measured with a non-contact optical device.
Sensitivity and specificity of the deep learning algorithm for the prediction of fast progressing myope	1 year	The investigators will estimate the sensitivity and specificity of the deep learning algorithm for the prediction of fast progressing myope (a change in SER of 0.75D or more per year). Cycloplegic spherical refraction changes measured by an auto-refractometer will be used as the indicator of myopia progression.
Sensitivity and specificity of the diagnostic algorithm in identifying abnormal vision screening result	baseline	The investigators will estimate the sensitivity and specificity of the diagnostic algorithm in identifying abnormal vision screening result (e.g., abnormal eye lid, abnormal cornea, and strabismus detected with mobile devices).
Area under the receiver operating characteristic curve of the deep learning algorithm for the prediction of incident myopia	1 year	The investigators will estimate the area under the receiver operating characteristic curve of the deep learning algorithm for the prediction of incident myopia.
Prevalence of amblyopia, strabismus and other ocular abnormalities	baseline	Cover-uncover tests will be performed to detect strabismus. Any ocular abnormalities, including corneal opacities, lens opacities, and retinal diseases will be recorded based on slit lamp, direct ophthalmoscopic and/or mobile phone video examination. Participants with an uncorrected visual acuity 6/7.5 or worse with undergo subjective refraction to identify amblyopia.
Sensitivity and specificity of the deep learning algorithm for the prediction of incident myopia	1 year	The investigators will estimate sensitivity and specificity of the deep learning algorithm for the prediction of incident myopia.

Trial Locations

Locations (2)

Zhognshan Ophthalmic Center, Sun Yat-sen University; 🇨🇳 Guangzhou, Guangdong, China

Schools; 🇨🇳 Zhaoqing, Guangdong, China