Development and validation of a risk prediction model for dry eye disease in myopic children

Introduction To identify independent risk factors for dry eye disease (DED) and to develop and validate a predictive model for DED among myopic schoolchildren aged 8–16 years in northern China. Methods A cross-sectional study was conducted among myopic children in Zhangjiakou, Hebei Province. The children underwent comprehensive ocular surface evaluations, including corneal fluorescein staining, tear film break-up time (FBUT), Schirmer I test, lipid layer thickness (LLT), and partial blink rate (PBR). DED was diagnosed using the 2022 Chinese Expert Consensus criteria. Behavioral and environmental risk factors were assessed via validated questionnaires. Logistic regression identified independent factors, and a nomogram was constructed and validated for individualized DED risk estimation. Results A total of 1,303 myopic children were included for analysis, and the prevalence of objectively diagnosed DED was 31.2%. Tear film instability, reduced LLT, and increased PBR were the predominant ocular surface abnormalities. The children were divided into training and validation sets according to the community. Among the 912 children in the training set, multivariate analysis identified orthokeratology (Ortho-K) lens use (OR = 4.74), daily screen time ≥ 4 h (OR = 4.21), near work ≥ 4 h (OR = 3.53), BMI ≥ 24 (OR = 3.20), and sleep duration 6 h (OR = 2.26) as independent risk factors (all p 0.05). The risk prediction nomogram demonstrated acceptable discriminative ability (AUC: 0.74 in the training set and 0.70 in the validation set). Conclusion Dry eye disease is common and under-recognized among myopic children in northern China, with risk closely linked to modifiable behavioral and lifestyle factors and Ortho-K lens use. The developed nomogram can facilitate early identification and targeted interventions for high-risk children.