Identifying Factors Associated With Fast Visual Field Progression in Patients With Ocular Hypertension Based on Unsupervised Machine Learning.

Publisher: Wolters Kluwer Health, Inc Country of Publication: United States NLM ID: 9300903 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1536-481X (Electronic) Linking ISSN: 10570829 NLM ISO Abbreviation: J Glaucoma Subsets: MEDLINE

Publication: <2015- > : Philadelphia, PA : Wolters Kluwer Health, Inc.
Original Publication: New York, N.Y. : Raven Press, c1992-

Ocular Hypertension*/physiopathology ; Ocular Hypertension*/diagnosis ; Disease Progression* ; Unsupervised Machine Learning* ; Visual Fields*/physiology ; Intraocular Pressure*/physiology ; Visual Field Tests*; Humans ; Male ; Female ; Middle Aged ; Cross-Sectional Studies ; Aged ; Vision Disorders/physiopathology ; Vision Disorders/diagnosis ; Risk Factors ; Follow-Up Studies ; Glaucoma, Open-Angle/physiopathology ; Glaucoma, Open-Angle/diagnosis

Prcis: We developed unsupervised machine learning models to identify different subtypes of patients with ocular hypertension in terms of visual field (VF) progression and discovered 4 subtypes with different trends of VF worsening. We then identified factors associated with fast VF progression.
Purpose: To identify ocular hypertension (OHT) subtypes with different trends of visual field (VF) progression based on unsupervised machine learning and to discover factors associated with fast VF progression.
Design: Cross-sectional and longitudinal study.
Participants: A total of 3133 eyes of 1568 ocular hypertension treatment study (OHTS) participants with at least 5 follow-up VF tests were included in the study.
Methods: We used a latent class mixed model (LCMM) to identify OHT subtypes using standard automated perimetry (SAP) mean deviation (MD) trajectories. We characterized the subtypes based on demographic, clinical, ocular, and VF factors at the baseline. We then identified factors driving fast VF progression using generalized estimating equation (GEE) and justified findings qualitatively and quantitatively.
Main Outcome Measure: Rates of SAP mean deviation (MD) change.
Results: The LCMM model discovered four clusters (subtypes) of eyes with different trajectories of MD worsening. The number of eyes in clusters were 794 (25%), 1675 (54%), 531 (17%), and 133 (4%). We labeled the clusters as improvers (cluster 1), stables (cluster 2), slow progressors (cluster 3), and fast progressors (cluster 4) based on their mean of MD decline rate, which were 0.08, -0.06, -0.21, and -0.45 dB/year, respectively. Eyes with fast VF progression had higher baseline age, intraocular pressure (IOP), pattern standard deviation (PSD) and refractive error (RE), but lower central corneal thickness (CCT). Fast progression was associated with being male, heart disease history, diabetes history, African American race, and stroke history.
Conclusions: Unsupervised clustering can objectively identify OHT subtypes including those with fast VF worsening without human expert intervention. Fast VF progression was associated with higher history of stroke, heart disease and diabetes. Fast progressors were more from African American race, males, and had higher incidence of glaucoma conversion. Subtyping can provide guidance for adjusting treatment plans to slow vision loss and improve quality of life of patients with a faster progression course.
(Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Update of: ArXiv. 2023 Sep 26:arXiv:2309.15867v1.. (PMID: 37808089)

Goldberg IJGitsC. How common is glaucoma worldwide? 2000:1–8.
Lee PP, Walt JG, Doyle JJ, et al. A multicenter, retrospective pilot study of resource use and costs associated with severity of disease in glaucoma. Arch Ophthal. 2006;124:12–19.
Traverso CE, Walt JG, Kelly SP, et al. Direct costs of glaucoma and severity of the disease: a multinational long term study of resource utilisation in Europe. Br J Ophthalmol. 2005;89:1245–1249.
Gordon MO, Beiser JA, Brandt JD, et al. The ocular hypertension treatment study: baseline factors that predict the onset of primary open-angle glaucoma. Arch Ophthalmol. 2002;120:714–720; discussion 829–730.
Leske MC, Heijl A, Hyman L, et al. Early manifest glaucoma trial: design and baseline data. Ophthalmology. 1999;106:2144–2153.
O’Leary N, Chauhan BC, Artes PH. Visual field progression in glaucoma: estimating the overall significance of deterioration with permutation analyses of pointwise linear regression (PoPLR). Invest Ophthalmol Vis Sci. 2012;53:6776–6784.
Gardiner SK, Crabb DP. Examination of different pointwise linear regression methods for determining visual field progression. Invest Ophthalmol Vis Sci. 2002;43:1400–1407.
Advanced Glaucoma Intervention Study. 2. Visual field test scoring and reliability. Ophthalmology. 1994;101:1445–1455.
Katz J. Scoring systems for measuring progression of visual field loss in clinical trials of glaucoma treatment. Ophthalmology. 1999;106:391–395.
Bengtsson B, Heijl A. A visual field index for calculation of glaucoma rate of progression. Am J Ophthalmol. 2008;145:343–353.
Yousefi S, Goldbaum MH, Balasubramanian M, et al. Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements. IEEE Trans Biomed Eng. 2014;61:2112–2124.
Bowd C, Weinreb RN, Balasubramanian M, et al. Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiers. PLoS One. 2014;9:e85941.
Yousefi S, Goldbaum MH, Balasubramanian M, et al. Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points. IEEE Trans Biomed Eng. 2014;61:1143–1154.
Yousefi S, Balasubramanian M, Goldbaum MH, et al. Unsupervised Gaussian mixture-model with expectation maximization for detecting glaucomatous progression in standard automated perimetry visual fields. Transl Vis Sci Technol. 2016;5:1–19.
Yousefi S, Elze T, Pasquale LR, et al. Glaucoma monitoring using manifold learning and unsupervised clustering. Paper presented at: 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ); November 19–21, 2018.
Yousefi S, Kiwaki T, Zheng Y, et al. Detection of longitudinal visual field progression in glaucoma using machine learning. Am J Ophthalmol. 2018;193:71–79.
Herle M, Micali N, Abdulkadir M, et al. Identifying typical trajectories in longitudinal data: modelling strategies and interpretations. Eur J Epidemiol. 2020;35:205–222.
Proust-Lima C, Philipps V, Liquet B. Estimation of extended mixed models using latent classes and latent processes: the R package lcmm. J Stat Softw. 2017;78:1–56.
Boucquemont J, Loubere L, Metzger M, et al. Identifying subgroups of renal function trajectories. Nephrol Dial Transplant. 2017;32(suppl_2):ii185–ii193.
Massara P, Keown-Stoneman CD, Erdman L, et al. Identifying longitudinal-growth patterns from infancy to childhood: a study comparing multiple clustering techniques. Int J Epidemiol. 2021;50:1000–1010.
Jacobson TY, Nho K, Risacher SL, et al. Longitudinal latent class mixture model identifies analysis subclasses of cognitive/neurodegeneration trajectory withdifferential patterns of genetic association. Alzheimer’s Dement. 2021;17:e056640.
Swaminathan SS, Jammal AA, Rao JS, et al. Improved prediction of perimetric loss in glaucomatous eyes using latent class mixed modeling. Ophthalmol Glaucoma. 2023;6:642–650.
Kass MA, Heuer DK, Higginbotham EJ, et al. The ocular hypertension treatment study: a randomized trial determines that topical ocular hypotensive medication delays or prevents the onset of primary open-angle glaucoma. Arch Ophthalmol. 2002;120:701–713.
Keltner JL, Johnson CA, Quigg JM, et al. Confirmation of visual field abnormalities in the ocular hypertension treatment study. Ocular Hypertension Treatment Study Group. Arch Ophthalmol. 2000;118:1187–1194.
Fan R, Bowd C, Christopher M, et al. Detecting glaucoma in the ocular hypertension treatment study using deep learning: implications for clinical trial endpoints. TechRxiv; 2021.
Proust C, Jacqmin-Gadda H, Taylor JM, et al. A nonlinear model with latent process for cognitive evolution using multivariate longitudinal data. Biometrics. 2006;62:1014–1024.
Proust-Lima C, Amieva H, Jacqmin-Gadda H. Analysis of multivariate mixed longitudinal data: a flexible latent process approach. Br J Math Stat Psychol. 2013;66:470–487.
Kastner A, Stuart KV, Montesano G, et al. Calcium channel blocker use and associated glaucoma and related traits among UK Biobank participants. JAMA Ophthalmol. 2023;141:956–964.
Scheetz TE, Faga B, Ortega L, et al. Glaucoma risk alleles in the ocular hypertension treatment study. Ophthalmology. 2016;123:2527–2536.
Keltner JL, Johnson CA, Levine RA, et al. Normal visual field test results following glaucomatous visual field end points in the ocular hypertension treatment study. Arch Ophthalmol. 2005;123:1201–1206.
Lee BL, Wilson MR Ocular Hypertension Treatment S. Ocular Hypertension Treatment Study (OHTS) commentary. Curr Opin Ophthalmol. 2003;14:74–77.
Chauhan BC, Malik R, Shuba LM, et al. Rates of glaucomatous visual field change in a large clinical population. Invest Ophthalmol Vis Sci. 2014;55:4135–4143.
Wright DM, Konstantakopoulou E, Montesano G, et al. Visual field outcomes from the multicenter, randomized controlled laser in glaucoma and ocular hypertension trial (LiGHT). Ophthalmology. 2020;127:1313–1321.
Jammal AA, Thompson AC, Mariottoni EB, et al. Rates of glaucomatous structural and functional change from a large clinical population: the Duke Glaucoma Registry Study. Am J Ophthalmol. 2021;222:238–247.
Hu R, Racette L, Chen KS, et al. Functional assessment of glaucoma: uncovering progression. Surv Ophthalmol. 2020;65:639–661.
Huopaniemi I, Nadkarni G, Nadukuru R, et al. Disease progression subtype discovery from longitudinal EMR data with a majority of missing values and unknown initial time points. AMIA Annu Symp Proc. 2014;2014:709–718.
Pathak M, Demirel S, Gardiner SK. Nonlinear trend analysis of longitudinal pointwise visual field sensitivity in suspected and early glaucoma. Transl Vis Sci Technol. 2015;4:8.
Verboon P, Pat-El R. Clustering longitudinal data using R: A Monte Carlo study. Methodology. 2022;18:144–163.
Weinreb RN, Friedman DS, Fechtner RD, et al. Risk assessment in the management of patients with ocular hypertension. Am J Ophthalmol. 2004;138:458–467.
Drance SM. Correlation between optic disc changes and visual field defects in chronic open-angle glaucoma. Trans Sect Ophthalmol Am Acad Ophthalmol Otolaryngol. 1976;81:224–226.
De Moraes CG, Juthani VJ, Liebmann JM, et al. Risk factors for visual field progression in treated glaucoma. Arch Ophthalmol. 2011;129:562–568.
Pantalon AD, Feraru C, Chiselita D. Risk factors and long term progression in open angle glaucoma patients. Rom J Ophthalmol. 2016;60:174–180.
Wang SY, Melles R, Lin SC. The impact of central corneal thickness on the risk for glaucoma in a large multiethnic population. J Glaucoma. 2014;23:606–612.
Kim YJ, Yun SC, Na JH, et al. Glaucoma progression in eyes with a history of refractive corneal surgery. Invest Ophthalmol Vis Sci. 2012;53:4485–4489.
Rivera JL, Bell NP, Feldman RM. Risk factors for primary open angle glaucoma progression: what we know and what we need to know. Curr Opin Ophthalmol. 2008;19:102–106.
Mitchell P, Hourihan F, Sandbach J, et al. The relationship between glaucoma and myopia: the Blue Mountains Eye Study. Ophthalmology. 1999;106:2010–2015.
Park HL, Hong KE, Park CK. Impact of age and myopia on the rate of visual field progression in glaucoma patients. Medicine (Baltimore). 2016;95:e3500.
Naito T, Yoshikawa K, Mizoue S, et al. Relationship between visual field progression and baseline refraction in primary open-angle glaucoma. Clin Ophthalmol. 2016;10:1397–1403.
Zhao R, Chen X, Liu X, et al. Direct cup-to-disc ratio estimation for glaucoma screening via semi-supervised learning. IEEE J Biomed Health Inform. 2020;24:1104–1113.
Chan TCW, Bala C, Siu A, et al. Risk factors for rapid glaucoma disease progression. Am J Ophthalmol. 2017;180:151–157.
Murakami Y, Lee BW, Duncan M, et al. Racial and ethnic disparities in adherence to glaucoma follow-up visits in a county hospital population. Arch Ophthalmol. 2011;129:872–878.
Khachatryan N, Pistilli M, Maguire MG, et al. Primary open-angle African American glaucoma genetics (POAAGG) study: gender and risk of POAG in African Americans. PLoS One. 2019;14:e0218804.

R01 EY033005 United States EY NEI NIH HHS

Date Created: 20240802 Date Completed: 20241104 Latest Revision: 20241218

20241218

PMC11534539

10.1097/IJG.0000000000002472

39092996