Systemic factors associated with 10-year glaucoma progression in South Korean population: a single center study based on electronic medical records.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Original Publication: London : Nature Publishing Group, copyright 2011-

Glaucoma, Open-Angle*/epidemiology ; Glaucoma, Open-Angle*/complications ; Optic Disk* ; Glaucoma*/complications; Humans ; Electronic Health Records ; Visual Fields ; Tomography, Optical Coherence/methods ; Retinal Ganglion Cells ; Intraocular Pressure ; Republic of Korea/epidemiology ; Disease Progression

Glaucoma is a multifactorial disease where various systemic features are involved in the progression of the disease. Based on initial systemic profiles in electronic medical records, this study aimed to develop a model predicting factors of long-term rapid retinal nerve fiber layer (RNFL) thinning over 5 years in 505 patients with primary open-angle glaucoma. Eyes with faster or slower RNFL thinning were stratified using a decision tree model, and systemic and ophthalmic data were incorporated into the models based on random forest and permutation methods, with the models interpreted by Shapley additive explanation plots (SHAP). According to the decision tree, a higher lymphocyte ratio (> 34.65%) was the most important systemic variable discriminating faster or slower RNFL thinning. Higher mean corpuscular hemoglobin (> 32.05 pg) and alkaline phosphatase (> 88.0 IU/L) concentrations were distinguishing factors in the eyes with lymphocyte ratios > 34.65% and < 34.65%, respectively. SHAP demonstrated larger baseline RNFL thickness, greater fluctuation of intraocular pressure (IOP), and higher maximum IOP as the strongest ophthalmic factors, while higher lymphocyte ratio and higher platelet count as the strongest systemic factors associated with faster RNFL thinning. Machine learning-based modeling identified several systemic factors as well as previously acknowledged ophthalmic risk factors associated with long-term rapid RNFL thinning.
(© 2023. The Author(s).)

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Date Created: 20230111 Date Completed: 20230113 Latest Revision: 20230228

20231215

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