Abstract
To explore the correlation between tear LT-a, pterygium status, and dry eye indicators. We established a diagnostic model to evaluate active pterygium. A retrospective study was conducted between June 2021 and June 2023 on 172 patients, comprising 108 men and 64 women. The study analyzed LT-a and various ocular parameters in all participants. The data was collected using Excel software and analyzed using SPSS 25.0 statistical software and Medcalc. We made a nomogram diagnostic model to different diagnosed the state of pterygium. This study found that pterygium has progressive eye surface damage during the active state. There was no significant difference in dry eye indicators between the two groups. However, the concentration of LT-a in the active group was significantly lower than that in the inactive group (P < 0.001). We observed that increased pterygium grade corresponded to a worse ocular surface condition. In addition, LT-a was significantly positively correlated with disease duration, but negatively correlated with age, pterygium size, active pterygium state, and LLT value. The optimal intercept value for evaluating active pterygium in Lt-a was ≤ 0.49 dg/ml. We screened three variables for evaluating active pterygium through Single and Multiple regression analysis: LT-a grading, pterygium size, and congestion score. Finally, we made a reliable diagnostic nomogram model. Pterygium development triggers immune inflammation. Our model based on LT-a identifies active pterygium for personalized treatment options and new research directions.