A diagnostic model based on color vision examination for dysthyroid optic neuropathy using Hardy-Rand-Rittler color plates

• Published in: Graefe's archive for clinical and experimental ophthalmology Vol. 261; no. 9; pp. 2669 - 2678
• Main Authors: Liang, Jiaqi, Tian, Peng, Wang, Jing, Fan, Shuxian, Deng, Xiaowen, Zhang, Jiafeng, Zhang, Jia, Wang, Mei, Zeng, Peng
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2023; Springer Nature B.V
• Subjects: Accuracy; Color blindness; Color vision; Decision trees; Medicine; Medicine & Public Health; Oculoplastics and Orbit; Ophthalmology; Optic neuropathy; HRR color test; Random forest; Red-green deficiency; Decision tree; Dysthyroid optic neuropathy
• ISSN: 0721-832X; 1435-702X
• DOI: 10.1007/s00417-023-06062-9

Purpose To investigate color vision deficiency and the value of Hardy-Rand-Rittler (HRR) color plates in monitoring dysthyroid optic neuropathy (DON) to improve the diagnosis of DON. Methods The participants were divided into DON and non-DON (mild and moderate-to-severe) groups. All the subjects underwent HRR color examination and comprehensive ophthalmic examinations. The random forest and decision tree models based on the HRR score were constructed by R software. The ROC curve and accuracy of different models in diagnosing DON were calculated and compared. Results Thirty DON patients (57 eyes) and sixty non-DON patients (120 eyes) were enrolled. The HRR score was lower in DON patients than in non-DON patients (12.1 ± 6.2 versus 18.7 ± 1.8, p  < 0.001). The major color deficiency was red-green deficiency in DON using HRR test. The HRR score, CAS, RNFL, and AP100 were found to be important factors in predicting DON from random forest and selected by decision tree to construct the multifactor model. The sensitivity, specificity, and the area under the curve (AUC) of the HRR score were 86%, 72%, and 0.87, respectively. The HRR score decision tree had a sensitivity, specificity, and AUC of 93%, 57%, and 0.75, respectively, with an accuracy of 82%. The data of the multifactor decision tree were 90%, 89%, and 0.93 for sensitivity, specificity, and AUC, respectively, with an accuracy of 91%. Conclusion The HRR test was valid as screening method for DON. The multifactor decision tree based on the HRR test improved the diagnostic efficacy for DON. An HRR score of less than 12 and red-green deficiency may be characteristic of DON.