Unsupervised Clustering for POAG Phenotyping.

PURPOSE: To identify and characterize clinically meaningful phenotypic subtypes of POAG using unsupervised clustering of multimodal clinical data.

METHODS

This retrospective cohort study included 4274 eyes from 4274 patients aged ≥40 years with POAG. Twenty-one clinical features encompassing visual field indices and progression slopes, retinal nerve fiber layer (RNFL) thickness, optic nerve head parameters, and IOP level and variability were analyzed. Hierarchical clustering, K-means, and fuzzy C-means algorithms were applied, with stability assessed via internal validation metrics and visual field archetypal analysis.

RESULTS

Five reproducible POAG phenotypes were identified The. k-means and fuzzy C-means performed best (Calinski-Harabasz 731.76 and 731.43; Davies-Bouldin 1.59 and 1.60), with excellent pairwise agreement between them (κ = 0.97) compared with moderate agreement with hierarchical clustering (κ ≈ 0.55), and high overall stability (mean Adjusted Rand Index, 0.98 ± 0.02). Clusters 1 and 2 represented mild, stable phenotypes (mean deviation [MD] slopes of +0.14 and +0.10 dB/year), differing primarily in IOP burden. Cluster 3 showed structural-functional dissociation-significant RNFL thinning (71.40 ± 8.14 µm) despite limited functional progression (MD slope +0.08 dB/year). Cluster 4 exhibited the most aggressive course, with rapid functional decline (MD slope of -0.98 ± 0.58 dB/year), greatest IOP variability (3.88 ± 1.82 mm Hg), and advanced structural loss (RNFL 64.44 ± 10.05 µm). Cluster 5 demonstrated advanced baseline damage (MD of -15.28 ± 4.72 dB; RNFL, 62.87 ± 9.53 µm) with relative longitudinal stability. Visual field archetypes aligned with cluster-specific severity.

CONCLUSIONS

Multimodal clustering identifies distinct POAG phenotypes, supporting improved risk stratification and targeted management.