Glaucoma progression. Clinical practice guide

L. Jaumandreu1,2, A. Antón3,4,2, M. Pazos5,2, I. Rodriguez-Uña6,2, I. Rodriguez Agirretxe7,2, J.M. Martinez de la Casa8,2, M.E. Ayala3,2, M. Parrilla-Vallejo9,2, A. Dyrda3, L. Díez-Álvarez1,2, G. Rebolleda1,2, F.J. Muñoz-Negrete1,2
1Servicio de Oftalmología, Hospital Universitario Ramón y Cajal, IRYCIS, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain
2Red de Oftalmología RETICS OFTARED del Instituto de Salud Carlos III (ISCIII), Madrid, Spain
3Institut Català de la Retina (ICR), Barcelona, Spain
4Universitat Internacional de Catalunya (UIC), Barcelona, Spain
5Institut Clínic d'Oftalmologia, Hospital Clínic de Barcelona, IDIBAPS, Universitat de Barcelona, Barcelona, Spain
6Instituto Oftalmológico Fernández-Vega, Universidad de Oviedo, Oviedo, Spain
7Servicio de Oftalmología, Hospital Universitario Donostia, San Sebastián, Gipuzkoa, Spain
8Servicio de Oftalmología, Hospital Clinico San Carlos, Instituto de investigación sanitaria del Hospital Clínico San Carlos (IsISSC), IIORC, Universidad Complutense de Madrid, Madrid, Spain
9Servicio de Oftalmología, Hospital Universitario Virgen Macarena, Sevilla, Spain

Tài liệu tham khảo

Tham, 2014, Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis, Ophthalmology, 121, 2081, 10.1016/j.ophtha.2014.05.013 Guidelines for glaucoma cabe en la línea superior no tienesentido separar la palabra con tanto espacio disponible enla línea. Available from: https://bjo.bmj.com/content/bjophthalmol/105/Suppl1/1.full.pdf. Saunders, 2016, What rates of glaucoma progression are clinically significant?, Expert Rev Ophthalmol, 11, 227, 10.1080/17469899.2016.1180246 Shekelle, 2001, Validity of the Agency for Healthcare Research and Quality clinical practice guidelines: how quickly do guidelines become outdated?, JAMA, 286, 1461, 10.1001/jama.286.12.1461 Ministerio de Sanidad y Política Social, 2009 Brouwers, 2010, AGREE II: advancing guideline development, reporting and evaluation in health care, CMAJ, 182, E839, 10.1503/cmaj.090449 Shea, 2007, Development of AMSTAR: a measurement tool to assess the methodological quality of systematic reviews, BMC Med Res Methodol, 7, 10, 10.1186/1471-2288-7-10 Higgins, 2011, The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials, BMJ, 343, d5928, 10.1136/bmj.d5928 Scottish Intercollegiate Guidelines Network (SIGN), 2019 Prum, 2016, Primary Open-Angle Glaucoma Preferred Practice Pattern® Guidelines, Ophthalmology, 123, P41, 10.1016/j.ophtha.2015.10.053 Canadian Ophthalmological Society Glaucoma Clinical Practice Guideline Expert Committee, 2009, Canadian Ophthalmological Society evidence-based clinical practice guidelines for the management of glaucoma in the adult eye, Can J Ophthalmol, 44, S7 Gardiner, 2006, Normal age-related sensitivity loss for a variety of visual functions throughout the visual field, Optom Vis Sci, 83, 438, 10.1097/01.opx.0000225108.13284.fc Heijl, 1988, Perimetric threshold variability and age, Arch Ophthalmol, 106, 450, 10.1001/archopht.1988.01060130492014 Leske, 2007, Predictors of long-term progression in the early manifest glaucoma trial, Ophthalmology, 114, 1965, 10.1016/j.ophtha.2007.03.016 Lichter, 2001, Interim clinical outcomes in the Collaborative Initial Glaucoma Treatment Study comparing initial treatment randomized to medications or surgery, Ophthalmology, 108, 1943, 10.1016/S0161-6420(01)00873-9 De Moraes, 2012, Risk factors for visual field progression in the low-pressure glaucoma treatment study, Am J Ophthalmol, 154, 702, 10.1016/j.ajo.2012.04.015 Park, 2016, Impact of age and myopia on the rate of visual field progression in glaucoma patients, Medicine (Baltimore), 95, e3500, 10.1097/MD.0000000000003500 Heijl, 2003, Measuring visual field progression in the Early Manifest Glaucoma Trial, Acta Ophthalmol Scand, 81, 286, 10.1034/j.1600-0420.2003.00070.x Arnalich-Montiel, 2009, Performance of glaucoma progression analysis software in a glaucoma population, Graefes Arch Clin Exp Ophthalmol, 247, 391, 10.1007/s00417-008-0986-1 Heijl, 2008, A comparison of visual field progression criteria of 3 major glaucoma trials in early manifest glaucoma trial patients, Ophthalmology, 115, 1557, 10.1016/j.ophtha.2008.02.005 Ederer, 1994, The Advanced Glaucoma Intervention Study (AGIS): 1. Study design and methods and baseline characteristics of study patients, Control Clin Trials, 15, 299, 10.1016/0197-2456(94)90046-9 Musch, 1999, The Collaborative Initial Glaucoma Treatment Study: study design, methods, and baseline characteristics of enrolled patients, Ophthalmology, 106, 653, 10.1016/S0161-6420(99)90147-1 1998, Comparison of glaucomatous progression between untreated patients with normal-tension glaucoma and patients with therapeutically reduced intraocular pressures. Collaborative Normal-Tension Glaucoma Study Group, Am J Ophthalmol, 126, 487, 10.1016/S0002-9394(98)00223-2 Leske, 1999, Early Manifest Glaucoma Trial: design and baseline data, Ophthalmology, 106, 2144, 10.1016/S0161-6420(99)90497-9 Krupin, 2005, The Low-pressure Glaucoma Treatment Study (LoGTS): study design and baseline characteristics of enrolled patients, Ophthalmology, 112, 376, 10.1016/j.ophtha.2004.10.034 Nouri-Mahdavi, 2011, Influence of visual field testing frequency on detection of glaucoma progression with trend analyses, Arch Ophthalmol, 129, 1521, 10.1001/archophthalmol.2011.224 Artes, 2010, Longitudinal and cross-sectional analyses of visual field progression in participants of the Ocular Hypertension Treatment Study, Arch Ophthalmol, 128, 1528, 10.1001/archophthalmol.2010.292 Aoki, 2017, Investigating the usefulness of a cluster-based trend analysis to detect visual field progression in patients with open-angle glaucoma, Br J Ophthalmol, 101, 1658, 10.1136/bjophthalmol-2016-310069 Artes, 2002, Properties of perimetric threshold estimates from Full Threshold, SITA Standard, and SITA Fast Strategies, Invest Ophthalmol Vis Sci, 43, 2654 Wu, 2019, Comparing 10-2 and 24-2 visual fields for detecting progressive central visual loss in glaucoma eyes with early central abnormalities, Ophthalmol Glaucoma, 2, 95, 10.1016/j.ogla.2019.01.003 Tomairek, 2020, Studying the role of 10-2 visual field test in different stages of glaucoma, Eur J Ophthalmol, 30, 706, 10.1177/1120672119836904 Park, 2013, Parafoveal scotoma progression in glaucoma, Ophthalmology, 120, 1546, 10.1016/j.ophtha.2013.01.045 West, 2021, Value of 10-2 visual field testing in glaucoma patients with early 24-2 visual field loss, Ophthalmology, 128, 545, 10.1016/j.ophtha.2020.08.033 Phu, 2020, Ability of 24-2C and 24-2 grids to identify central visual field defects and structure-function concordance in glaucoma and suspects, Am J Ophthalmol, 219, 317, 10.1016/j.ajo.2020.06.024 Gardiner, 2014, Assessment of the reliability of standard automated perimetry in regions of glaucomatous damage, Ophthalmology, 121, 1359, 10.1016/j.ophtha.2014.01.020 Nouri-Mahdavi, 2007, Comparison of methods to predict visual field progression in glaucoma, Arch Ophthalmol, 125, 1176, 10.1001/archopht.125.9.1176 Casas-Llera, 2009, Visual field index rate and event-based glaucoma progression analysis: comparison in a glaucoma population, Br J Ophthalmol, 93, 1576, 10.1136/bjo.2009.158097 Antón, 2013, Glaucoma progression detection: agreement, sensitivity, and specificity of expert visual field evaluation, event analysis, and trend analysis, Eur J Ophthalmol, 23, 187, 10.5301/ejo.5000193 Medeiros, 2012, Integrating event- and trend-based analyses to improve detection of glaucomatous visual field progression, Ophthalmology, 119, 458, 10.1016/j.ophtha.2011.10.003 Cho, 2012, Progression detection in different stages of glaucoma: mean deviation versus visual field index, Jpn J Ophthalmol, 56, 128, 10.1007/s10384-011-0110-7 Heijl, 2009, Natural history of open-angle glaucoma, Ophthalmology, 116, 2271, 10.1016/j.ophtha.2009.06.042 Anderson, 2001, Natural history of normal-tension glaucoma, Ophthalmology, 108, 247, 10.1016/S0161-6420(00)00518-2 Chauhan, 2014, Rates of glaucomatous visual field change in a large clinical population, Invest Ophthalmol Vis Sci, 55, 4135, 10.1167/iovs.14-14643 Heijl, 2012, Rates of visual field progression in clinical glaucoma care, Acta Ophthalmol, 91, 406, 10.1111/j.1755-3768.2012.02492.x Salonikiou, 2018, Tolerable rates of visual field progression in a population-based sample of patients with glaucoma, Br J Ophthalmol, 102, 916, 10.1136/bjophthalmol-2017-310635 Wu, 2017, Frequency of testing to detect visual field progression derived using a longitudinal cohort of glaucoma patients, Ophthalmology, 124, 786, 10.1016/j.ophtha.2017.01.027 Fallon, 2017, Diagnostic accuracy of imaging devices in glaucoma: a meta-analysis, Surv Ophthalmol, 62, 446, 10.1016/j.survophthal.2017.01.001 Karvonen, 2020, Diagnostic performance of modern imaging instruments in glaucoma screening, Br J Ophthalmol, 104, 1399, 10.1136/bjophthalmol-2019-314795 Kansal, 2018, Optical coherence tomography for glaucoma diagnosis: an evidence based meta-analysis, PLoS One, 13, 10.1371/journal.pone.0190621 Holló, 2015, Influence of a new software version of the RTVue-100 optical coherence tomograph on the detection of glaucomatous structural progression, Eur J Ophthalmol, 25, 410, 10.5301/ejo.5000576 Leung, 2012, Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: a prospective analysis of age-related loss, Ophthalmology, 119, 731, 10.1016/j.ophtha.2011.10.010 Chauhan, 2020, Differential effects of aging in the macular retinal layers, neuroretinal rim, and peripapillary retinal nerve fiber layer, Ophthalmology, 127, 177, 10.1016/j.ophtha.2019.09.013 Wu, 2017, Impact of normal aging and progression definitions on the specificity of detecting retinal nerve fiber layer thinning, Am J Ophthalmol, 181, 106, 10.1016/j.ajo.2017.06.017 Holló, 2016, evaluation of retinal nerve fiber layer thickness and ganglion cell complex progression rates in healthy, ocular hypertensive, and glaucoma eyes with the Avanti RTVue-XR Optical Coherence Tomograph based on 5-year follow-up, J Glaucoma, 25, e905, 10.1097/IJG.0000000000000410 Zhang, 2016, Longitudinal and cross-sectional analyses of age effects on retinal nerve fiber layer and ganglion cell complex thickness by Fourier-domain OCT, Transl Vis Sci Technol, 5, 1 Jo, 2019, Effects of age on peripapillary and macular vessel density determined using optical coherence tomography angiography in healthy eyes, Invest Ophthalmol Vis Sci, 60, 3492, 10.1167/iovs.19-26848 Leung, 2013, Impact of age-related change of retinal nerve fiber layer and macular thicknesses on evaluation of glaucoma progression, Ophthalmology, 120, 2485, 10.1016/j.ophtha.2013.07.021 Huo, 2018, Age-related changes in and determinants of macular ganglion cell-inner plexiform layer thickness in normal Chinese adults, Clin Exp Ophthalmol, 46, 400, 10.1111/ceo.13067 Tatham, 2017, Detecting structural progression in glaucoma with optical coherence tomography, Ophthalmology, 124, S57, 10.1016/j.ophtha.2017.07.015 Lee, 2019, Rates of ganglion cell-inner plexiform layer thinning in normal, open-angle glaucoma and pseudoexfoliation glaucoma eyes: a trend-based analysis, Invest Ophthalmol Vis Sci, 60, 599, 10.1167/iovs.18-25296 Lee, 2017, Evaluation of ganglion cell-inner plexiform layer thinning in eyes with optic disc hemorrhage: a trend-based progression analysis, Invest Ophthalmol Vis Sci, 58, 6449, 10.1167/iovs.17-22547 Liu, 2015, Rates of retinal nerve fiber layer loss in contralateral eyes of glaucoma patients with unilateral progression by conventional methods, Ophthalmology, 122, 2243, 10.1016/j.ophtha.2015.07.027 Shin, 2017, Ganglion cell-inner plexiform layer change detected by optical coherence tomography indicates progression in advanced glaucoma, Ophthalmology, 124, 1466, 10.1016/j.ophtha.2017.04.023 Belghith, 2016, Structural change can be detected in advanced-glaucoma eyes, Invest Ophthalmol Vis Sci, 57, 511, 10.1167/iovs.15-18929 Kurysheva, 2021, Detection of primary angle closure glaucoma progression by optical coherence tomography, J Glaucoma, 30, 410, 10.1097/IJG.0000000000001829 Zhang, 2019, Predictive factors for the rate of visual field progression in the advanced imaging for glaucoma study, Am J Ophthalmol, 202, 62, 10.1016/j.ajo.2019.02.015 Zhang, 2017, Comparison of glaucoma progression detection by optical coherence tomography and visual field, Am J Ophthalmol, 184, 63, 10.1016/j.ajo.2017.09.020 Hou, 2018, Integrating macular ganglion cell inner plexiform layer and parapapillary retinal nerve fiber layer measurements to detect glaucoma progression, Ophthalmology, 125, 822, 10.1016/j.ophtha.2017.12.027 Leung, 2010, Evaluation of retinal nerve fiber layer progression in glaucoma: a study on optical coherence tomography guided progression analysis, Invest Ophthalmol Vis Sci, 51, 217, 10.1167/iovs.09-3468 Moghimi, 2018, Macular and optic nerve head vessel density and progressive retinal nerve fiber layer loss in glaucoma, Ophthalmology, 125, 1720, 10.1016/j.ophtha.2018.05.006 Park, 2019, Association between parapapillary choroidal vessel density measured with optical coherence tomography angiography and future visual field progression in patients with glaucoma, JAMA Ophthalmol, 137, 681, 10.1001/jamaophthalmol.2019.0422 Moghimi, 2019, Measurement floors and dynamic ranges of OCT and OCT angiography in glaucoma, Ophthalmology, 126, 980, 10.1016/j.ophtha.2019.03.003 Mohammadzadeh, 2020, Longitudinal macular structure–function relationships in glaucoma, Ophthalmology, 127, 888, 10.1016/j.ophtha.2020.01.023 Tan, 2019, Estimating visual field mean deviation using optical coherence tomographic nerve fiber layer measurements in glaucoma patients, Sci Rep, 9, 10.1038/s41598-019-54792-w Majoor, 2019, Contrast-to-noise ratios for assessing the detection of progression in the various stages of glaucoma, Transl Vis Sci Technol, 8, 8, 10.1167/tvst.8.3.8 Banegas, 2015, Agreement among spectral-domain optical coherence tomography, standard automated perimetry, and stereophotography in the detection of glaucoma progression, Invest Ophthalmol Vis Sci, 56, 1253, 10.1167/iovs.14-14994 Suda, 2018, Evaluation of structure-function relationships in longitudinal changes of glaucoma using the spectralis OCT follow-up mode, Sci Rep, 8, 10.1038/s41598-018-35419-y Ashimatey, 2016, Between-subject variability in healthy eyes as a primary source of structural-functional discordance in patients with glaucoma, Invest Ophthalmol Vis Sci, 57, 502, 10.1167/iovs.15-18633 Urata, 2020, Comparison of short- and long-term variability in standard perimetry and spectral domain optical coherence tomography in glaucoma, Am J Ophthalmol, 210, 19, 10.1016/j.ajo.2019.10.034 Suda, 2015, Comparison of longitudinal changes in functional and structural measures for evaluating progression of glaucomatous optic neuropathy, Invest Ophthalmol Vis Sci, 56, 5477, 10.1167/iovs.15-16704 Seth, 2018, 5-year disease progression of patients across the glaucoma spectrum assessed by structural and functional tools, Br J Ophthalmol, 102, 802, 10.1136/bjophthalmol-2017-310731 Garway-Heath, 2018, Combining optical coherence tomography with visual field data to rapidly detect disease progression in glaucoma: a diagnostic accuracy study, Health Technol Assess, 22, 1, 10.3310/hta22040 Zhang, 2016, Predicting development of glaucomatous visual field conversion using baseline Fourier-domain optical coherence tomography, Am J Ophthalmol, 163, 29, 10.1016/j.ajo.2015.11.029 Zhang, 2016, Baseline Fourier-domain optical coherence tomography structural risk factors for visual field progression in the advanced imaging for glaucoma study, Am J Ophthalmol, 172, 94, 10.1016/j.ajo.2016.09.015 Yu, 2016, Risk of visual field progression in glaucoma patients with progressive retinal nerve fiber layer thinning: a 5-year prospective study, Ophthalmology, 123, 1201, 10.1016/j.ophtha.2016.02.017 Abe, 2016, The relative odds of progressing by structural and functional tests in glaucoma, Invest Ophthalmol Vis Sci, 57, OCT421, 10.1167/iovs.15-18940 Na, 2015, Rates and patterns of macular and circumpapillary retinal nerve fiber layer thinning in preperimetric and perimetric glaucomatous eyes, J Glaucoma, 24, 278, 10.1097/IJG.0000000000000046 Hammel, 2017, Comparing the rates of retinal nerve fiber layer and ganglion cell-inner plexiform layer loss in healthy eyes and in glaucoma eyes, Am J Ophthalmol, 178, 38, 10.1016/j.ajo.2017.03.008 Hood, 2019, Structure-function agreement is better than commonly thought in eyes with early glaucoma, Invest Ophthalmol Vis Sci, 60, 4241, 10.1167/iovs.19-27920 Öhnell, 2016, Structural and functional progression in the early manifest glaucoma trial, Ophthalmology, 123, 1173, 10.1016/j.ophtha.2016.01.039 Öhnell, 2017, Detection of glaucoma progression by perimetry and optic disc photography at different stages of the disease: results from the Early Manifest Glaucoma Trial, Acta Ophthalmol, 95, 281, 10.1111/aos.13290 Medeiros, 2012, A combined index of structure and function for staging glaucomatous damage, Arch Ophthalmol, 130, 1107, 10.1001/archophthalmol.2012.827 National Institute for Health and Care Excellence (NICE), 2017 Gómez-Valverde, 2019, Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning, Biomed Opt Express, 10, 892, 10.1364/BOE.10.000892 Li, 2020, Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs, Graefes Arch Clin Exp Ophthalmol, 258, 851, 10.1007/s00417-020-04609-8 Liu, 2019, A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis, Lancet Digit Health, 1, e271, 10.1016/S2589-7500(19)30123-2 Medeiros, 2019, From machine to machine: an OCT-trained deep learning algorithm for objective quantification of glaucomatous damage in fundus photographs, Ophthalmology, 126, 513, 10.1016/j.ophtha.2018.12.033 Thompson, 2020, Assessment of a segmentation-free deep learning algorithm for diagnosing glaucoma from optical coherence tomography scans, JAMA Ophthalmol, 138, 333, 10.1001/jamaophthalmol.2019.5983 Asano, 2020, Validating the efficacy of the binomial pointwise linear regression method to detect glaucoma progression with multicentral database, Br J Ophthalmol, 104, 569, 10.1136/bjophthalmol-2019-314136 Berchuck, 2019, Estimating rates of progression and predicting future visual fields in glaucoma using a deep variational autoencoder, Sci Rep, 9, 18113, 10.1038/s41598-019-54653-6 Brigatti, 1997, Automatic detection of glaucomatous visual field progression with neural networks, Arch Ophthalmol, 115, 725, 10.1001/archopht.1997.01100150727005 Goldbaum, 2012, Progression of patterns (POP): a machine classifier algorithm to identify glaucoma progression in visual fields, Invest Ophthalmol Vis Sci, 53, 6557, 10.1167/iovs.11-8363 Lin, 2003, Neural networks to identify glaucomatous visual field progression, Am J Ophthalmol, 135, 49, 10.1016/S0002-9394(02)01836-6 Sample, 2005, Unsupervised machine learning with independent component analysis to identify areas of progression in glaucomatous visual fields, Invest Ophthalmol Vis Sci, 46, 3684, 10.1167/iovs.04-1168 Tucker, 2005, A spatio-temporal Bayesian network classifier for understanding visual field deterioration, Artif Intell Med, 34, 163, 10.1016/j.artmed.2004.07.004 Wang, 2019, An artificial intelligence approach to detect visual field progression in glaucoma based on spatial pattern analysis, Invest Ophthalmol Vis Sci, 60, 365, 10.1167/iovs.18-25568 Yousefi, 2016, Unsupervised Gaussian mixture-model with expectation maximization for detecting glaucomatous progression in standard automated perimetry visual fields, Transl Vis Sci Technol, 5, 2, 10.1167/tvst.5.3.2 Yousefi, 2020, Monitoring glaucomatous functional loss using an artificial intelligence-enabled dashboard, Ophthalmology, 127, 1170, 10.1016/j.ophtha.2020.03.008 Yousefi, 2014, Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points, IEEE Trans Biomed Eng, 61, 1143, 10.1109/TBME.2013.2295605 Yousefi, 2014, Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements, IEEE Trans Biomed Eng, 61, 2112, 10.1109/TBME.2014.2314714 Yousefi, 2015, Detecting glaucomatous change in visual fields: analysis with an optimization framework, J Biomed Inform, 58, 96, 10.1016/j.jbi.2015.09.019 Yousefi, 2018, Detection of longitudinal visual field progression in glaucoma using machine learning, Am J Ophthalmol, 193, 71, 10.1016/j.ajo.2018.06.007 Bowd, 2012, Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements, Invest Ophthalmol Vis Sci, 53, 2382, 10.1167/iovs.11-7951 Christopher, 2018, Retinal nerve fiber layer features identified by unsupervised machine learning on optical coherence tomography scans predict glaucoma progression, Invest Ophthalmol Vis Sci, 59, 2748, 10.1167/iovs.17-23387 Demirel, 2009, Predicting progressive glaucomatous optic neuropathy using baseline standard automated perimetry data, Invest Ophthalmol Vis Sci, 50, 674, 10.1167/iovs.08-1767 Garcia, 2019, Using Kalman filtering to forecast disease trajectory for patients with normal tension glaucoma, Am J Ophthalmol, 199, 111, 10.1016/j.ajo.2018.10.012 Kazemian, 2018, Personalized prediction of glaucoma progression under different target intraocular pressure levels using filtered forecasting methods, Ophthalmology, 125, 569, 10.1016/j.ophtha.2017.10.033 Lee, 2020, Machine learning classifiers-based prediction of normal-tension glaucoma progression in young myopic patients, Jpn J Ophthalmol, 64, 68, 10.1007/s10384-019-00706-2 Liu, 2013, Longitudinal modeling of glaucoma progression using 2-dimensional continuous-time hidden Markov model, Med Image Comput Comput Assist Interv, 16, 444 Medeiros, 2021, Detection of progressive glaucomatous optic nerve damage on fundus photographs with deep learning, Ophthalmology, 128, 383, 10.1016/j.ophtha.2020.07.045 Park, 2019, Visual field prediction using recurrent neural network, Sci Rep, 9, 8385, 10.1038/s41598-019-44852-6 Schell, 2014, Using filtered forecasting techniques to determine personalized monitoring schedules for patients with open-angle glaucoma, Ophthalmology, 121, 1539, 10.1016/j.ophtha.2014.02.021 Song, 2018, Clinical prediction performance of glaucoma progression using a 2-dimensional continuous-time hidden Markov model with structural and functional measurements, Ophthalmology, 125, 1354, 10.1016/j.ophtha.2018.02.010 Wen, 2019, Forecasting future Humphrey Visual Fields using deep learning, PLoS One, 14, 10.1371/journal.pone.0214875 Belghith, 2015, Learning from healthy and stable eyes: a new approach for detection of glaucomatous progression, Artif Intell Med, 64, 105, 10.1016/j.artmed.2015.04.002 Damji, 2003, Target IOP Workshop participants Canadian perspectives in glaucoma management: setting target intraocular pressure range, Can J Ophthalmol, 38, 189, 10.1016/S0008-4182(03)80060-1 Sihota, 2018, Simplifying “target” intraocular pressure for different stages of primary open-angle glaucoma and primary angle-closure glaucoma, Indian J Ophthalmol, 66, 495, 10.4103/ijo.IJO_1130_17 Founti, 2020, Risk factors for visual field deterioration in the United Kingdom Glaucoma Treatment Study, Ophthalmology, 127, 1642, 10.1016/j.ophtha.2020.06.009 Bak, 2020, Pre-perimetric open angle glaucoma with young age of onset: natural clinical course and risk factors for progression, Am J Ophthalmol, 216, 121, 10.1016/j.ajo.2020.03.026 Heijl, 2002, Reduction of intraocular pressure and glaucoma progression: results from the Early Manifest Glaucoma Trial, Arch Ophthalmol, 120, 1268, 10.1001/archopht.120.10.1268 Garway-Heath, 2015, Latanoprost for open-angle glaucoma (UKGTS): a randomised, multicentre, placebo-controlled trial, Lancet, 385, 1295, 10.1016/S0140-6736(14)62111-5 Vass, 2007, Medical interventions for primary open angle glaucoma and ocular hypertension, Cochrane Database Syst Rev, 10.1002/14651858.CD003167.pub3 Grupo de trabajo de la Guía de práctica clínica sobre el glaucoma de ángulo abierto, 2017 Yokoyama, 2019, Effects of brimonidine and timolol on the progression of visual field defects in open-angle glaucoma: a single-center randomized trial, J Glaucoma, 28, 575, 10.1097/IJG.0000000000001285 Chauhan, 2010, Canadian Glaucoma Study: 3. Impact of risk factors and intraocular pressure reduction on the rates of visual field change, Arch Ophthalmol, 128, 1249, 10.1001/archophthalmol.2010.196 Boland, 2013, Comparative effectiveness of treatments for open-angle glaucoma: a systematic review for the U.S. Preventive Services Task Force, Ann Intern Med, 158, 271, 10.7326/0003-4819-158-4-201302190-00008 Garg, 2019, Primary selective laser trabeculoplasty for open-angle glaucoma and ocular hypertension: clinical outcomes, predictors of success, and safety from the laser in glaucoma and ocular hypertension trial, Ophthalmology, 126, 1238, 10.1016/j.ophtha.2019.04.012 Jay, 1989, The benefit of early trabeculectomy versus conventional management in primary open angle glaucoma relative to severity of disease, Eye (Lond), 3, 528, 10.1038/eye.1989.84 Jay, 1988, Early trabeculectomy versus conventional management in primary open angle glaucoma, Br J Ophthalmol, 72, 881, 10.1136/bjo.72.12.881 Migdal, 1994, Long-term functional outcome after early surgery compared with laser and medicine in open-angle glaucoma, Ophthalmology, 101, 1651, 10.1016/S0161-6420(94)31120-1 Janz, 2001, The Collaborative Initial Glaucoma Treatment Study: interim quality of life findings after initial medical or surgical treatment of glaucoma, Ophthalmology, 108, 1954, 10.1016/S0161-6420(01)00874-0 Burr, 2012, Medical versus surgical interventions for open angle glaucoma, Cochrane Database Syst Rev, 10.1002/14651858.CD004399.pub3