Analysis of influencing factors of dry eyes after cataract surgery and construction of a prediction model.

Abstract

Objective: To identify the influencing factors of dry eyes after cataract surgery and construct a prediction model to provide a reference for ophthalmologists in assessing the risk of postoperative dry eyes.

Methods: A retrospective study was conducted from January 2023 to April 2024, involving 219 patients (219 eyes) who underwent phacoemulsification with intraocular lens implantation at the Department of Ophthalmology, Ninth People's Hospital of Suzhou. Patients were divided into two groups based on the presence or absence of dry eyes at 2 weeks postoperatively. Data from both groups were analyzed to determine the influencing factors of dry eyes after cataract surgery. A nomogram prediction model was constructed using R software. The model's discrimination was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), and model calibration was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test and the Bootstrap method (self-sampling technique). Decision curve analysis was employed to evaluate the clinical utility of the model.

Results: Among the 219 cataract patients, 53 (24.20%) developed dry eyes during the 2-week follow-up period. Multivariate logistic regression analysis identified smoking (OR = 1.809, P = 0.037), diabetes mellitus (OR = 3.248, P = 0.002), elevated IL-6 (OR = 3.019, P = 0.016), a high Hospital Anxiety and Depression Scale (HADS) score (OR = 2.147, P = 0.029), and longer surgical incision length (OR = 2.995, P = 0.014) as significant risk factors for postoperative dry eye. The AUC of the nomogram model was 0.857 (95% CI: 0.803-0.913), and the H-L goodness-of-fit test showed no statistical significance (χ² = 4.472, P = 0.812), indicating good discrimination and calibration of the model. The average absolute error between predicted and actual probabilities after 1000 Bootstrap iterations was 0.021. Decision curve analysis demonstrated that the net benefit of the model was higher than the two extreme scenarios.

Conclusion: Postoperative dry eyes in cataract patients is associated with smoking, diabetes, elevated IL-6, high HADS scores, and longer incision lengths. The nomogram model demonstrates good predictive capability for assessing the risk of dry eyes after cataract surgery.