The aim of this study is to develop and validate a dry eye risk prediction model for adults with refractive error.

A total of 402 adults (402 eyes) with refractive error who visited the Second Affiliated Hospital of Harbin Medical University from June 2024 to May 2025 were enrolled as study subjects. Among them, there were 91 males (91 eyes) and 311 females (311 eyes) with a mean age of (51.8±14.5) years (ranging from 18 to 79 years). Demographic, lifestyle, and clinical data were collected through questionnaires and ophthalmic examinations. Using the random sorting function in Excel, all data were randomly divided into a training set (282 cases, 282 eyes) and a validation set (120 cases, 120 eyes) at a ratio of 7: 3. Variables with P<0.05 in univariate analysis were included to construct a logistic regression-based dry eye risk prediction model for adults with refractive error. Calibration curves were used to validate the goodness-of-fit of the model. Continuous variables that followed a normal distribution were expressed as ±s, with intergroup comparisons using independent sample t-tests; variables not following a normal distribution were expressed as median (Q1, Q3), with intergroup comparisons using rank sum tests. Categorical variables were expressed as frequency and percentage, with intergroup comparisons using chi-square tests, or Fisher′s exact test. Univariate logistic regression analysis was first performed for all categorical variables, and variables with P<0.05 were entered into the multivariate Logistic regression model. Based on this, a clinical prediction model was established and a nomogram was drawn. Decision curve analysis was used to evaluate the clinical applicability of the model.

The prevalence of dry eye was 42.2% in the training set and 44.2% in the validation set.Univariate logistic regression analysis was performed in the training set (282 cases, 282 eyes), and statistically significant variables were then entered into the multivariate Logistic regression model. Effective refractive correction was a strongly associated factor; the risk of dry eye in those with refractive correction was significantly lower than in those without correction (OR=0.172, 95%CI: 0.101 to 0.290, P<0.05). Myopia and presbyopia were both positively associated with dry eye (OR=1.644 and 2.00; 95%CI: 1.021 to 2.647 and 1.149 to 3.483; P<0.05). Consuming fewer than three types of nutrient-rich foods daily was associated with dry eye risk (OR=2.645, 95%CI: 1.058 to 6.612, P<0.05). The effective refractive correction, myopia, presbyopia, and consuming fewer than three types of nutrient-rich foods were entered into the multivariate logistic regression model. After adjusting for confounders, effective refractive correction, myopia, and presbyopia remained independent factors for dry eye (OR_adjusted=0.157, 1.715, and 1.928; 95%CI: 0.090 to 0.273, 1.003 to 2.931, and 1.039 to 3.575; P<0.05). The area under the curve of the model was 0.766 (95%CI: 0.711 to 0.821) in the training set and 0.781 (95%CI: 0.699 to 0.863) in the validation set. Calibration was good, and decision curve analysis showed a net clinical benefit across a wide range of threshold probabilities.

The nomogram-based dry eye risk prediction model constructed has a good predictive performance and clinical applicability. It reveals that refractive factors such as uncorrected refractive error, myopia, and presbyopia are core independent risk factors for dry eye in adults with refractive error. Timely and effective refractive correction plays a key role in dry eye prevention and helps promote individualized and early intervention in dry eye management.