High Myopia Normative Database of Peripapillary Retinal Nerve Fiber Layer Thickness to Detect Myopic Glaucoma in a Chinese Population

中文摘要

這篇研究的目的是開發和驗證高度近視（high myopia, HM）特定的圍視神經纖維層（peripapillary retinal nerve fiber layer, pRNFL）厚度的正常數據庫，以區分高度近視青光眼（high myopia glaucoma, HMG）和高度近視。這項研究使用了一個大樣本的非病理性HM眼睛的pRNFLT正常數據庫，並在外部數據集中驗證其效能。研究結果顯示該正常數據庫在區分HMG和HM方面具有較高的診斷性能。該研究對於定義HM眼睛的正常數據庫具有重要意義，並提供了一種根據OCT掃描結果區分HMG和HM的有效工具。這項研究為亞洲地區患有HM眼疾的人群提供了一種潛在的青光眼診斷方法。

English Abstract

The purpose of this study was to develop and validate a high myopia-specific normative database of peripapillary retinal nerve fiber layer (pRNFL) thickness to differentiate between high myopia (HM) and highly myopic glaucoma (HMG) in a Chinese population. Previous studies trying to establish normative databases for HM eyes had several limitations, including poor imaging capability for HM eyes, small sample size, and lack of validation in external data. This study used swept-source (SS) optical coherence tomography (OCT) images and a large sample size to build a normative database for nonpathologic HM eyes. The database was then validated with an external dataset. The results showed that the HM-specific normative database had good diagnostic performance in distinguishing between HM and HMG. However, the diagnostic performance was better for global pRNFL thickness compared to quadrant pRNFL thickness. The study concluded that this normative database can be an effective tool for differentiating between HM and HMG based on OCT scans.