Construction and evaluation of an individualized nomogram prediction model for posterior vitreous detachment in patients with cataract surgery.

OBJECTIVE

To analyze factors linked to posterior vitreous detachment (PVD) after cataract surgery and develop a nomogram prediction model.

METHODS

A total of 480 cataract patients who underwent phacoemulsification from January 2022 to December 2024 were enrolled. They were divided into modeling (n = 240) and validation (n = 240) groups. Based on postoperative PVD status, the modeling group included 80 PVD and 160 non-PVD cases, while the validation group had 84 PVD and 156 non-PVD cases. Demographic and clinical data were analyzed. Multivariate logistic regression identified risk factors, and a nomogram was constructed. The model's performance was evaluated using ROC curves, calibration plots, and decision curve analysis (DCA).

RESULTS

No significant differences were found in gender, BMI, diabetes, hypertension, vitreous cavity depth, preoperative vitreous opacity, or lens nuclear hardness (P > 0.05). However, age, axial length, preoperative vitreous liquefaction, cumulative ultrasound energy (CUE) time, and operation time differed significantly (P < 0.05). Logistic regression confirmed these as independent predictors of PVD. ROC analysis indicated strong discriminative ability. Calibration curves showed good fit (modeling group: χ2 = 9.320, P = 0.316; validation group: χ2 = 6.282, P = 0.616). DCA revealed clinical net benefit when the risk threshold exceeded 0.02.

CONCLUSION

Older age, longer axial length, greater preoperative vitreous liquefaction, longer CUE time, and extended operation time independently predict PVD after cataract surgery. The nomogram based on these factors shows strong predictive accuracy and clinical utility.