Detection of primary angleclosure suspect with different mechanisms of angle closure using multivariate prediction models
AuthorZhang, Y; Dong, Z; Zhang, Q; Li, L; Thomas, R; Li, SZ; He, MG; Wang, NL
Source TitleActa Ophthalmologica
University of Melbourne Author/sHe, Mingguang
AffiliationOphthalmology (Eye & Ear Hospital)
Document TypeJournal Article
CitationsZhang, Y., Dong, Z., Zhang, Q., Li, L., Thomas, R., Li, S. Z., He, M. G. & Wang, N. L. (2020). Detection of primary angleclosure suspect with different mechanisms of angle closure using multivariate prediction models. ACTA OPHTHALMOLOGICA, https://doi.org/10.1111/aos.14634.
Access StatusOpen Access
PURPOSE: We had found that a multivariate prediction model used for the detection of primary angle-closure suspects (PACS) by combining multiple static and dynamic anterior segment optical coherence tomography (ASOCT) parameters had an area under the receiver operating characteristic curve (AUC) of 0.844. We undertook this study to evaluate this method in screening of PACS with different dominant mechanisms of angle closure (AC). METHODS: The right eyes of subjects aged ≥40 years who participated in the 5-year follow-up of the Handan Eye Study and had undergone gonioscopy and ASOCT examinations under light and dark conditions were included. All ASOCT images were analysed by the Zhongshan Angle Assessment Program. The dominant AC mechanism in each eye was determined to be pupillary block (PB), plateau iris configuration (PIC) or thick peripheral iris roll (TPIR). Backward logistic regression (LR) was used for inclusion of variables in the prediction models. LR, Naïve Bayes' classification (NBC) and neural network (NN) were evaluated and compared using the AUC. RESULTS: Data from 796 subjects (413 PACS and 383 normal eyes) were analysed. The AUCs of LR, NBC and NN in the PB group were 0.920, 0.918 and 0.917. The AUCs of LR, NBC and NN in the PIC group were 0.715, 0.708 and 0.707. The AUCs of LR, NBC and NN in TPIR group were 0.867, 0.833 and 0.886. CONCLUSIONS: Prediction models showed the best performance for detection of PACS with PB mechanism for AC and have potential for screening of PACS.
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