Influencing factors and nomogram prediction model for posterior capsular opacification after phacoemulsification combined with IOL implantation

Abstract:

AIM:To analyze the influencing factors for posterior capsular opacification(PCO)in patients after phacoemulsification combined with intraocular lens(IOL)implantation, and to construct a nomogram prediction model based on these factors.

METHODS:This was a retrospective cohort study conducted using convenience sampling. The model group comprised cataract patients who underwent phacoemulsification combined with IOL implantation at the hospital from January 2019 to March 2021. The external validation group included the same cohort of patients treated between April 2021 and March 2022. Patients were categorized into those with PCO and those without PCO based on the occurrence of PCO during follow-up. Clinical characteristics were compared between the PCO-positive and PCO-negative subgroups within the model group. Multivariate Logistic regression analysis was performed to identify factors influencing postoperative PCO in cataract patients, followed by the construction of a nomogram prediction model. The calibration curve was used to validate the model's performance in the model group, and the decision curve was employed to assess its clinical predictive efficacy.

RESULTS:The study model cohort included 256 patients(256 eyes), comprising 47 cases(47 eyes)with postoperative PCO and 209 cases(209 eyes)without PCO. Significant differences were observed in patient age, surgical history, comorbidities(diabetes, glaucoma, high myopia), operative duration, anesthesia method, and type of IOL material(all P<0.05). Multivariate Logistic regression analysis revealed that surgical history, age, hydrophilic IOL material, comorbid glaucoma, and operative duration were all influencing factors for postoperative PCO in cataract patients(all P<0.05). The external validation cohort comprised 112 cataract patients(112 eyes), including 22 cases(22 eyes)with postoperative PCO and 90 cases(90 eyes)without PCO. Statistical analyses showed no significant difference between the predicted and actual postoperative PCO probabilities(χ²_A=3.214, P_A=0.920; χ²_B=10.979, P_B=0.203), with C-index values of 0.904(0.855-0.952)and 0.908(0.846-0.970), respectively. Decision curve analysis demonstrated that the predictive model provided significant net benefit for clinical decision-making when the predicted risk threshold exceeded 0.04 in the model cohort and 0.02 in the external validation cohort. The ROC curve results demonstrated that the risk scores for PCO postoperatively in cataract patients from the training cohort and external validation cohort were 0.904(0.861-0.937)and 0.913(0.872-0.945), respectively, indicating that the predictive model exhibits good discriminative power.

CONCLUSION:Age, history of ocular surgery, comorbid glaucoma, operative duration, and hydrophilic IOL material are influencing factors for PCO after phacoemulsification combined with IOL implantation. The nomogram prediction model constructed based on these factors provides valuable guidance for the prevention and management of PCO in clinical practice.