Journal of cataract and refractive surgery · 2013
Keratoconus diagnosis with optical coherence tomography–based pachymetric scoring system.
СтатьяНаблюдательное исследованиеПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
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Qin Bing, Chen Shihao, Brass Robert, Li Yan, Tang Maolong, Zhang Xinbo, Wang Xiaoyu, Wang Qinmei, Huang David
Аннотация
PURPOSE: To develop an optical coherence tomography (OCT) pachymetry map–based keratoconus risk scoring system.
SETTINGS: Doheny Eye Institute, University of Southern California, Los Angeles, California, and Brass Eye Center, New York, New York, USA; Department of Ophthalmology, Affiliated Eye Hospital of Wenzhou Medical College, Wenzhou, China.
DESIGN: Cross-sectional study.
METHODS: Fourier-domain OCT was used to acquire corneal pachymetry maps in normal and keratoconus subjects. Pachymetric variables were minimum, minimum−median, superior–inferior (S–I), superonasal–inferotemporal (SN–IT), and the vertical location of the thinnest cornea (Ymin). A logistic regression formula and a scoring system were developed based on these variables. Keratoconus diagnostic accuracy was measured by the area under the receiver operating characteristic (ROC) curve.
RESULTS: One hundred thirty-three eyes of 67 normal subjects and 82 eyes from 52 keratoconus subjects were recruited. The keratoconus logistic regression formula = 0.543 × minimum + 0.541 × (S–I) − 0.886 × (SN–IT) + 0.886 × (minimum–median) + 0.0198 × Ymin. The formula gave better diagnostic power with the area under the ROC than the best single variable (formula = 0.975, minimum = 0.942; P<.01). The diagnostic power with the area under the ROC of the keratoconus risk score (0.949) was similar to that of the formula (P=.08).
CONCLUSION: The OCT corneal pachymetry map–based logistic regression formula and the keratoconus risk scoring system provided high accuracy in keratoconus detection. These methods may be useful in keratoconus screening.
SETTINGS: Doheny Eye Institute, University of Southern California, Los Angeles, California, and Brass Eye Center, New York, New York, USA; Department of Ophthalmology, Affiliated Eye Hospital of Wenzhou Medical College, Wenzhou, China.
DESIGN: Cross-sectional study.
METHODS: Fourier-domain OCT was used to acquire corneal pachymetry maps in normal and keratoconus subjects. Pachymetric variables were minimum, minimum−median, superior–inferior (S–I), superonasal–inferotemporal (SN–IT), and the vertical location of the thinnest cornea (Ymin). A logistic regression formula and a scoring system were developed based on these variables. Keratoconus diagnostic accuracy was measured by the area under the receiver operating characteristic (ROC) curve.
RESULTS: One hundred thirty-three eyes of 67 normal subjects and 82 eyes from 52 keratoconus subjects were recruited. The keratoconus logistic regression formula = 0.543 × minimum + 0.541 × (S–I) − 0.886 × (SN–IT) + 0.886 × (minimum–median) + 0.0198 × Ymin. The formula gave better diagnostic power with the area under the ROC than the best single variable (formula = 0.975, minimum = 0.942; P<.01). The diagnostic power with the area under the ROC of the keratoconus risk score (0.949) was similar to that of the formula (P=.08).
CONCLUSION: The OCT corneal pachymetry map–based logistic regression formula and the keratoconus risk scoring system provided high accuracy in keratoconus detection. These methods may be useful in keratoconus screening.