Novel Subclassification of Presbyopia Based on Lens Functional and Structural Features.

PURPOSE

The purpose of this study was to classify eyes with presbyopia using lens functional and structural features to aid in surgical decision making.

METHODS

This cross-sectional observational study included healthy volunteers or those solely affected by presbyopia. Lens function was assessed using dysfunction lens index (DLI), whereas lens density was evaluated with Pentacam (LD-P) and swept-source optical coherence tomography (LD-S). Principal component analysis (PCA) and k-means clustering classified participants into non-lens surgery (NLS) and lens surgery (LS) groups. A scoring system was developed from the optimal cutoff values for simplified diagnosis, with a website created for automatic classification.

RESULTS

Totally, 352 eyes of 176 patients were studied, with a mean age of 46.1 ± 6.2 years and a mean axial length of 24.42 ± 1.62 mm. Among them, 268 (76.1%) eyes were diagnosed with presbyopia. The top 5.2% (14/268) of presbyopia cases with the highest variability were excluded by PCA. Subsequently, 138 eyes (51.5%) were clustered into the NLS group, and 116 eyes (43.3%) into the LS group. The LS group presented lower DLI and higher lens density compared to the NLS group (all P < 0.001). Optimal cutoff values were determined as follows: DLI = 7.75, LD-P = 8.75, and LD-S = 60.05 pixel units. The scoring system demonstrated that combining DLI with LD-P achieved the highest diagnostic efficacy. A website (www.zhu-zhou-lf.com) was developed for automatic classification.

CONCLUSIONS

We proposed a novel classification of presbyopia using lens functional and structural features.

TRANSLATIONAL RELEVANCE

This classification, integrating DLI and lens density values, offers a diagnostic tool to guide surgical decisions in presbyopia.