Evaluating corneal topographic patterns in keratoconic eyes and their association with keratoconus changes over time

评估圆锥角膜眼角膜地形图模式及其与圆锥角膜随时间变化的关系

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Abstract

AIM: To evaluate various corneal topographic patterns in keratoconus (KCN) and their association with disease progression. METHODS: A retrospective cohort study was conducted on 636 eyes of 335 KCN patients (396 males, 240 females) with a mean age of 30.95±7.95y. Participants underwent two ocular examinations, including corneal tomography using the Pentacam-HR. Topographic patterns were classified based on axial curvature, and KCN progression was defined as a change of ≥1.00 D in maximum keratometry (Kmax). Accordingly, evaluated eyes were categorized into progressive, regressive, and stable groups. RESULTS: The most common topographic pattern was asymmetric bowtie with inferior steepening (AB-IS, 27.4%) in both males (26.8%) and females (28.3%). In the regressive group, asymmetric bowtie with skewed radial axes (21.3%) was most frequent, while AB-IS (31.3%) and inferior steepening (IS, 25.9%) dominated the stable and progressive groups, respectively. Significant differences were observed in corneal parameters across patterns: the oval pattern exhibited the most negative spherical equivalent and the lowest corrected visual acuity, whereas the irregular pattern showed the highest Kmax values in the first examination (P<0.05). The asymmetric bowtie with superior steepening (AB-SS) pattern also exhibited the least curvature. In terms of changes in topographic parameters over time, variations were observed in certain parameters across different patterns. Notably, the irregular pattern exhibited the most significant changes in Kmax between the two examinations. Although the AB-SS tended to regressed, the AB-IS and IS patterns showed a tendency toward progression (P<0.05). Changes in most components of the KCN screening indices also varied significantly across patterns (P<0.05). CONCLUSION: Corneal topographic patterns in KCN exhibit distinct characteristics and progression rates. Understanding these patterns can aid in predicting disease progression and tailoring treatment strategies.

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