Predicting pre-myopia using screening data in clinics

Paper title: A nomogram for identifying premyopia and myopia candidates in Chinese children: focusing on those with cycloplegic spherical equivalent refraction ≤ + 0.75D

Authors: Wu J (1), Zhang C (2), Wang J (3)

1. Ophthalmology Department, The University of Hong Kong-Shenzhen Hospital, ShenZhen, Guangdong, China
2. Ophthalmology Department, The Shapingba Hospital, Chongqing University, Peoples Hospital of Shapingba District, Chonqing, China
3. Medical Technology Department, Chongqing Medical and Pharmaceutical College, Chonqing, China

Date: Published online August 28, 2025

Reference: Wu J, Zhang C, Wang J. A nomogram for identifying premyopia and myopia candidates in Chinese children: focusing on those with cycloplegic spherical equivalent refraction ≤ + 0.75D. BMC Ophthalmol. 2025 Aug 28;25(1):490.

[Link to open access paper]

There are several risk factors such as baseline refraction, age, parental myopia and lifestyle aspects which may contribute to a higher risk of developing myopia. Identifying children who may be pre-myopic is important for timely pro-active myopia management intervention. 

This study addresses the need for practical tools to identify children at risk of developing myopia before its onset. Given the limitations of cycloplegic refraction in real-world settings, the authors aimed to develop a predictive model using routine clinical data. Their goal was to create and validate a nomogram that enables early identification of pre-myopia and myopia intervention candidates - defined by a cycloplegic spherical equivalent refraction (CSER) of ≤+0.75D - using only primary screening measures.

Key points were as follows.

• The study included 1006 children aged 4–17 years from two Chinese medical centres.
• Independent predictors were uncorrected visual acuity (UCVA), non-cycloplegic spherical equivalent refraction (NCSER), axial length (AL), and the AL/ACRC ratio.
• These four parameters were incorporated into a nomogram that showed strong predictive performance, with area under the curve (AUC) values of 0.971 in the derivation set and 0.921 in the external validation set, where values closer to 1.0 indicate better diagnostic accuracy. 
• Decision curve analysis supported the model’s clinical utility across a wide range of threshold probabilities.
• The model was designed for use without cycloplegia, making it suitable for clinical and community screening programs.

This study presents a validated, clinically applicable nomogram that enables early identification of children who may benefit from myopia prevention or intervention strategies, even before a definitive myopia diagnosis. It is designed for use in typical clinical or community settings, where cycloplegic refraction may not always be feasible or practical due to time, resource, or safety constraints.

The nomogram uses four routinely available parameters - uncorrected visual acuity (UCVA), non-cycloplegic spherical equivalent refraction (NCSER), axial length (AL), and the axial length to corneal curvature (AL/ACRC) ratio - to generate a probability score. The nomogram assigns ‘points’ to each measure for a total risk score, which is then aligned with a probability of pre-myopia – see the image below, which is Figure 2 from the open access paper. This tool could help eyecare professionals stratify risk in children with cycloplegic SE ≤+0.75D, a group considered at high risk for myopia onset. The model’s strong predictive performance in both internal and external cohorts supports its reliability in diverse settings.

 In practice, the nomogram could aid decisions on when to pursue further cycloplegic refraction, initiate monitoring schedules, or consider early myopia control options. By integrating easily obtained biometric and refractive data into a structured tool, the model has the potential to offer a systematic approach to identify pre-myopic children, supporting earlier, more proactive clinical management. 

While this nomogram offers a practical screening tool for early risk assessment, further research is needed to refine its predictive capabilities. The model does not incorporate environmental or behavioural risk factors such as time spent outdoors, near work, or screen exposure, which are known to influence myopia development. Parental refractive history and genetic predisposition were also not included, despite their relevance to the development of childhood myopia. It also requires axial length and corneal curvature measurement, which may not be available to all practitioners either in practice or in a screening setting. 

The study’s cross-sectional and retrospective design limits conclusions about longitudinal predictive validity. Although external validation was conducted, long-term follow-up would help determine whether children identified by the model go on to develop myopia and benefit from early intervention. 

The model’s accuracy is currently restricted to populations within China, and it is unclear how well it would perform for other ethnic groups. Future research could adapt this tool for use in other populations. Including accommodation function or binocular vision status may further enhance predictions. Large-scale prospective studies will also be helpful to confirm whether integrating this nomogram into early myopia care leads to better long-term outcomes. 

Background: Primary refractive error screening parameters are commonly employed in clinical and community settings before cycloplegic assessment of myopia, however, their utility in identifying premyopia and myopia intervention candidates remains underexplored. This study aimed to develop a nomogram based on these routinely measured parameters to support clinical decision-making for premyopia and myopia prevention.

Methods: Pediatric patients (aged 4–17 years) from two medical centers in China were enrolled in this retrospective cohort study. A predictive model for the candidates of premyopia and myopia intervention was developed using logistic regression with multiple imputations. The model included the following primary screening parameters: age, gender, uncorrected visual acuity (UCVA), average corneal curvature (ACC), non-cycloplegic spherical equivalent refraction (NCSER), axial length (AL), and the axial length to average corneal radius of curvature (AL/ACRC) ratio. The efficacy of the model was assessed using the area under the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA). R was employed to conduct all statistical analyses.

Results: A total of 1006 participants (507 females, 499 boys) were enrolled, with 87.4% demonstrating cycloplegic spherical equivalent refraction (CSER) ≤ +0.75D. In multivariate logistic regression, UCVA, NCSER, AL, and AL/ACRC were identified as independent predictors. These predictors were incorporated into a nomogram to predict the candidates for premyopia and myopia intervention. The nomogram exhibited exceptional discrimination in the derivation set (AUC = 0.971, 95% CI: 0.957–0.984), whereas in the external validation set, the AUC was 0.921 (95% CI: 0.866–0.976) when a cutoff of 0.851 in derivation set was employed. Calibration was verified through the calibration curve and Hosmer-Lemeshow tests (P = 0.99 and P = 0.96, respectively), and the decision curve analysis demonstrated robust clinical utility for threshold probabilities of 0.10–1.00 in the derivation set and 0.20–1.00 in the external validation set.

Conclusion: The nomogram derived from the parameters of primary refractive error screening has the potential to preliminarily predict premyopia and myopia intervention candidates, thereby facilitating clinical decision-making in the context of premyopia and myopia prevention.

[Link to open access paper]