Abstract:AIM: To construct an assessment model for the risk of myopia in preschool children and evaluate its clinical value.
METHODS: A retrospective analysis was conducted on the data of preschool children who established refractive profiles in our hospital from January 2020 to December 2024. The eye with lower refractive error was selected. The right eye was chosen in cases where the refractive error of both eyes was identical. The data were divided into a training set(n=385)and a validation set(n=165)in a 7∶3 ratio. The data of the children were collected in detail, and the children's spherical equivalent(SE)was used as the dependent variable. The multiple linear regression model was used to screen the indicators that affected the biological indicators related to myopia in preschool children.Subsequently, the children in the training set were divided into a myopia group and a normal vision group based on whether myopia occurred(SE≤-0.50 D can be diagnosed as myopia according to refractive power assessment). Subsequently, the influencing factors selected by the multiple linear regression model were included as predictive variables, and a risk assessment equation was constructed using Logistic regression. The discrimination, calibration and clinical application value of the assessment model were verified through the receiver operating characteristic(ROC)curve, Hosmer-Lemeshow test and decision curve.
RESULTS: This study included 550 preschool children(550 eyes). The training set comprised 385 children(385 eyes; 190 boys and 195 girls), and the validation set comprised 165 children(165 eyes; 82 boys and 83 girls). Among the children in the training set, the myopia group included 34 children(34 eyes; 16 boys and 18 girls), and the non-myopic group included 351 children(351 eyes; 174 boys and 177 girls). Multiple linear regression analysis showed that the ratio of axial length(AL)to corneal curvature radius(CR), subfoveal choroidal thickness(SFCT), anterior chamber depth(ACD), age, parental myopia history, and outdoor activity time were risk factors for myopia in preschool children(all P<0.05). Based on the above risk factors, a Logistic regression risk assessment model was constructed, and the evaluation model Logit(P)was obtained as 0.894×AL/CR-1.312×SFCT+1.182×ACD+2.063×age+0.763×parental myopia history-5.868. The effectiveness of the myopia risk assessment model for preschool children based on the training set was evaluated. The results showed that the area under the ROC curve(AUC)was 0.939(95%CI: 0.912-0.965), the H-L test was χ2=1.487, P=0.993, and the decision curve indicated that within the threshold range of 0.03-0.81 and 0.89-0.97, the net return rate was greater than 0. This suggested that the model had good discrimination, calibration, and clinical application value in evaluating the myopia risk and actual risk of preschool children. Internal validation was conducted in an independent validation set, and the results showed that the AUC of the ROC curve was 0.967(95%CI: 0.919-1.000), the H-L test was χ2=3.818, P=0.873, and the decision curve indicated that within the threshold range of 0.01-0.99, the net return rate was greater than 0, further suggesting that the model assessment had good consistency with the actual risk.
CONCLUSION: Age, parental history of myopia, AL/CR, SFCT, and ACD are risk factors for myopia in preschool children. The Logistic regression risk assessment model constructed based on these factors has high evaluation value and can effectively evaluate the probability of myopia occurrence in individuals, providing reference for individualized prevention of myopia risk in preschool children.
