Myopia prediction: a systematic review

Authors: Han X, Liu C, Chen Y, He M.

Geographical coverage: Not reported   

Sector: Biomedical

Sub-sector: Treatment

Equity focus: None

Study population: Review of methods for calculating myopia risk, rather than individuals.

Review type: Other review

Quantitative synthesis method: Narrative synthesis

Qualitative synthesis method: Not applicable

Background: Myopia is a leading cause of visual impairment and has raised significant international concern in recent decades with rapidly increasing prevalence and incidence worldwide. Accurate prediction of future myopia risk could help identify high-risk children for early targeted intervention to delay myopia onset or slow myopia progression. Researchers have built and assessed various myopia prediction models based on different datasets, including baseline refraction or biometric data, lifestyle data, genetic data and data integration.

Objectives: To summarise all related work published in the past 30 years and provide a comprehensive review of myopia prediction methods, datasets and performance, which could serve as a useful reference and valuable guideline for future research.

Main findings:

Authors found age-specific SE is currently the strongest predictor for myopia prediction, while the additive effect of data including lifestyle, genetics and imaging data was inconclusive. Many challenges existed in this emerging field of myopia.

A total of 3,581 articles were identified by the authors in the initial search. After excluding duplicate papers and those that did not meet the inclusion criteria, 25 full-text articles were subsequently screened. After a full-text review, an additional six articles were excluded. This review was based on 17 core papers that utilised different data types for predicting future myopia risk.

• Prediction based on baseline refraction or biometric data – can be traced back to 1999. Studies reviewed suggest that baseline SE is the single best predictor for myopia onset, and preceding SE change is the best predictor for future myopia progression. In addition to statistical models, the centile curve is another more straightforward method for risk estimation on high myopia development. The challenge is how early and how accurately the prediction can be performed.
• Prediction based on lifestyle data – some studies show benefit of adding lifestyle factors to models, although others do not confirm its value. Limited effect could be due to the fact that many lifestyle factors are already reflected in baseline SE.
• Prediction based on genetic data – genes only appear to explain 10-20% of myopia risk. This currently appears to have limited value.
• Prediction based on integrated data – two studies suggest big potential for using real-world data collected in hospitals.

Authors emphasise that, while more research and better prediction models are always helpful and needed, the major task and challenge to fight the current myopia epidemic are successful implementation of currently available effective myopia prevention strategies (for example, increased outdoor time) and timely diagnosis and treatment for myopia individuals to minimise the risk of progression.

Authors note a need to generate integrated datasets and further develop methodologies for prediction.

Methodology:

Published studies were included if they were prospective observational studies conducted on humans and reported the use of a certain method to predict the future myopia risk, including myopia onset, myopia progression and specific spherical equivalence (SE). Only full-text studies published in English were included. Unpublished studies and meeting abstracts were not included due to uncertainty of methodological quality. Studies evaluating refraction prediction after treatments, including orthokeratology, atropine eyedrop and cataract surgery, were excluded.

Authors conducted a systematic search of all published articles related to myopia prediction model published between 1 January  1990, and 1 February  2021, by searching the online databases, including PubMed, EMBASE and Google Scholar. Titles of articles and full-texts were screened by two authors independently. No information was provided on the methods used to collect and analyse data of included studies.

Applicability/external validity: Applicability may be limited by the focus on English publications only.

Geographic focus: Authors do not consider how utility of prediction models might vary in different geographic contexts. At least one LMIC (China) was included in the review, however. The fact the review was limited to English material may have omitted prediction models developed in a non-English context.

Summary of quality assessment:

There were a number of limitations to the approaches used to identify, include and critically appraise studies. The search was limited to material published in English, and there is no evidence of reference sections of included articles being reviewed for further material. No assessment is made of the quality or risk of bias of included studies. In terms of the narrative analysis, there is no evidence that data was extracted by more than one author and, again, there is little consideration of how the quality of included studies may have influenced their reported results. For these reasons, we have low confidence in the findings of this review.