Optimizing Predictive Models for Distance and Near Vision Outcomes in Presbyopic Surgery Using Machine Learning.

Purpose: To develop predictive models for postoperative visual acuity-both near and distance, monocular and binocular-and a composite outcome metric, distance-corrected visual gain (DCVG), in patients undergoing presbyopic corneal refractive surgery. Methods: A retrospective analysis was performed on 914 eyes from 457 patients treated with PresbyMAX who completed 6 months of follow-up. Machine learning algorithms, including Random Forest regression and classification models, were applied to predict corrected distance visual acuity (CDVA) and distance-corrected near visual acuity (DCNVA), both measured using the same distance correction. Feature importance was evaluated using Shapley additive explanations (SHAP) and partial dependence plots (PDPs). Results: Binocular models outperformed monocular in predictive accuracy, and CDVA was more predictable than DCNVA. A clinical trade-off was observed whereby improvements of up to three lines in DCNVA could be achieved without significant CDVA loss. The optimal planned addition ranged between +1.50 D and +2.25 D, balancing near and distance vision outcomes. Patients 45 to 56 years of age showed greater potential for near vision gain without compromising distance vision. The DCVG metric effectively quantified overall visual benefits, with only a minority of cases showing a net loss. Conclusions: Predictive modeling combined with the DCVG metric enables personalized surgical planning by identifying key factors influencing outcomes. This approach supports better management of the visual trade-offs inherent in presbyopic refractive surgery. Translational Relevance: Machine learning-based prediction models and the DCVG metric facilitate individualized clinical decision-making, improving patient counseling and optimizing the balance between near and distance vision in presbyopic treatment.