Classification of primary angle closure spectrum with hierarchical cluster analysis.

Purpose: To classify subjects with primary angle closure into clusters based on features from anterior segment optical coherence tomography (ASOCT) imaging and to explore how these clusters correspond to disease subtypes, including primary angle closure suspect (PACS), primary angle closure glaucoma(PACG), acute primary angle closure (APAC) and fellow eyes of APAC and reveal the factors that become more predominant in each subtype of angle closure.

Method: A cross-sectional study of 248 eyes of 198 subjects(88 PACS eyes, 53 PACG eyes, 54 APAC eyes and 53 fellow eyes of APAC) that underwent complete examination including gonioscopy, A-scan biometry, and ASOCT. An agglomerative hierarchical clustering method was used to classify eyes based on ASOCT parameters.

Results: Statistical clustering analysis produced three clusters among which the anterior segment parameters were significantly different. Cluster 1(43 eyes) had the smallest anterior chamber depth(ACD) and area, as well as the greatest lens vault (p<0.001 for all). Cluster 2(113 eyes) had the thickest iris at 2000 microns(p = 0.048), and largest iris area(p<0.001), and the deepest ACD (p<0.001). Cluster 3(92 eyes) was characterized by elements of both clusters 1 and 2 and a higher iris curvature(p<0.001). There was a statistically significant difference in the distribution of clusters among subtypes of angle closure eyes(p<0.001). Although the patterns of clusters were similar in PACS and PACG eyes, with the majority of the eyes classified into cluster 2(55%, and 62%, respectively), the highest proportion of APAC and fellow eyes were assigned to clusters 1(44%) and 3 (51%), respectively.

Conclusion: Hierarchical cluster analysis identified three clusters with different features. Predominant anatomical components are different among subtypes of primary angle closure.