Mathematical projection model of visual loss due to fuchs corneal dystrophy

Shin Hatou, Shigeto Shimmura, Jun Shimazaki, Tomohiko Usui, Shiro Amano, Hideaki Yokogawa, Akira Kobayashi, Xiaodong Zheng, Atsushi Shiraishi, Yuichi Ohashi, Tsutomu Inatomi, Kazuo Tsubota

研究成果: Article査読

13 被引用数 (Scopus)

抄録

Purpose. To devise a mathematical disease classification model for eyes with primary guttata cornea, on the bases of endothelial loss trajectory and probability of advanced disease. Methods. A series of 1971 patients (3281 eyes), some with and some without guttata corneas, undergoing specular microscopy were retrospectively reviewed. The eyes were classified into four stages; stage 0, without guttae; 1, guttata cornea without edema; 2, mild Fuchs' corneal dystrophy (FCD); and 3, severe FCD, according to clinical records, and patient age and corneal endothelial cell density (ECD) were plotted. Nonparametric density smoothing was used to create a contour map, and a best-fit curve for ECD loss was calculated. The relation between ECD decrease rate and the stages were evaluated. Results. Endothelial decrease rate in stage 0 was 0.44%/year, which was compatible with that of normal eyes reported in previous studies. Decrease rates of stages 1, 2, and 3 were 0.81%, 2.65%, and 3.08%/ year, respectively. The age-ECD loss curves of 1.40%/year (ECO 1.4) and 2.00%/year (ECO 2.0) further divided stage 1 into three subgroups; stage 1a, asymptomatic guttata cornea; 1b, borderline guttata cornea; and 1c, pre-FCD. The ECO 2.0 cutoff line differentiated eyes with FCD from those without edema with a sensitivity and specificity of >90%. Stage 1c eyes were below ECO 2.0 and had a decrease rate as high as FCD. Conclusions. This mathematical model can be used to predict the prognosis of patients with primary guttata cornea.

本文言語English
ページ(範囲)7888-7893
ページ数6
ジャーナルInvestigative Ophthalmology and Visual Science
52
11
DOI
出版ステータスPublished - 2011 10

ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems
  • Cellular and Molecular Neuroscience

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