Abstract
Purpose of review
The purpose of this review is to examine contemporary techniques for detecting the progression of glaucoma. We provide a general overview of detection principles and review evidence-based diagnostic strategies and specific considerations for detecting glaucomatous progression in patients with mild, moderate and severe disease.
Recent findings
Diagnostic techniques and technologies for glaucoma have dramatically evolved in recent years, affording clinicians an expansive toolkit with which to detect glaucoma progression. Each stage of glaucoma, however, presents unique diagnostic challenges. In mild disease, either structural or functional changes can develop first in disease progression. In moderate disease, structural or functional changes can occur either in tandem or in isolation. In severe disease, standard techniques may fail to detect further disease progression, but such detection can still be measured using other modalities.
Summary
Detecting disease progression is central to the management of glaucoma. Glaucomatous progression has both structural and functional elements, both of which must be carefully monitored at all disease stages to determine when interventions are warranted.
Keywords: glaucoma progression, optical coherence tomography, structure and function relationship, visual field
INTRODUCTION
Glaucoma is a chronic, progressive optic neuropathy associated with characteristic atrophy of the optic nerve and associated visual field abnormalities. Glaucoma is thus composed of both structural and functional elements. The management of glaucoma is inherently tied to structural and functional evaluation, with interventions triggered by clinical evidence of disease progression. As structure and function may progress differently in glaucoma, no single test is sufficient for the surveillance of glaucoma. Instead, integration of both structural and functional data, tailored to disease stage and individual patients’ circumstances, is needed. These structural and functional measurement techniques have evolved considerably overtime. In this review, we provide an overview of evidence-based strategies for detection of glaucomatous progression at different disease stages.
GENERAL PRINCIPLES OF DETECTING GLAUCOMA PROGRESSION
Detecting structural progression in glaucoma
Glaucoma is characterized by loss of retinal ganglion cells (RGCs) with corresponding thinning of the retinal nerve fibre layer (RNFL) and structural changes of the optic nerve head [1]. The development of optical coherence tomography (OCT) in the early 1990 s revolutionized objective measurements of the retina and RNFL [2]. Measurement of peripapillary RNFL thickness is the primary OCT metric in the evaluation of glaucoma, representing loss of ganglion cell axons as they approach the optic nerve [3]. Scans yield RNFL measurements, which are typically interpreted in aggregate (e.g. mean circumpapillary thickness, ‘cpRNFL’) and subdivided into quadrants and sectors. OCT software compares measurements to age-matched normative values and represents them as colour-coded defects. In general, a short-term average cpRNFL change of 4–5 μm, or a sector change of 7–8 μm is suspicious for glaucoma progression [3,4]. In addition, algorithms such as guided progression analysis (GPA) track progression by flagging deviations from a prior baseline (event-based analysis) or quantifying changes over time (trend-based analysis).
Given the high density of ganglion cells in the macula, macular thickness measurements are also important for monitoring glaucoma [5]. Macular OCT metrics include the total macular thickness, macular RNFL (mRNFL), ganglion cell layer with inner plexiform layer (GCIPL) and ganglion cell complex (GCC = mRNFL+ GCIPL). In addition, certain OCT devices provide direct measurements of the optic nerve head (ONH), such as Bruch’s membrane opening-minimum rim width (BMO-MRW) and Bruch’s membrane opening-minimum rim area (BMO-MRA), although these metrics are not currently widely used clinically [6]. No single metric has demonstrated definitive superiority; rather, studies suggest that a combined approach is superior to any particular metric [7].
Despite rapid improvements in OCT technology with faster image acquisition times and better resolution, limitations still exist. Scan artifacts are common, and can result in falsely-reassuring and falsely-alarming measurements [8]. Signal strength and reliability can be affected by factors such as patient cooperation/movement and media opacities. Decentration (a peripapillary scan not properly centred on the optic nerve) can produce the appearance of false RNFL thickening or thinning in certain sectors. Segmentation algorithms are subject to error, especially in eyes with abnormal retinal or nerve head architecture from other causes such as high myopia. Comorbidities such as macular degeneration or epiretinal membranes can also confound measurements. Furthermore, although all commercially available SD-OCT devices share common overall design and function principles, scanning protocols and segmentation algorithms differ. Thus, comparisons of RNFL thickness values from different devices can be misleading and is not recommended [9]. The optimal frequency of OCT imaging has been a subject of debate; recent studies have suggested that twice-yearly testing is generally sufficient to detect clinically significant disease progression [10■].
Detecting functional progression in glaucoma
Characteristic visual field (VF) defects in glaucoma correspond anatomically to damaged retinal ganglion nerve fibre bundles. The Humphrey Field Analyzer (HFA; Carl Zeiss Meditec, Dublin, CA, USA) 24–2 is the most common testing pattern in modern clinical practice, assessing the central 24 degrees of the visual field via 54 testing points each separated by 6 degrees. Additional testing patterns can be employed to test more peripheral vision (e.g. 30–2), or to focus more closely on central vision (10–2). A recent modification of the 24–2 pattern, the 24-C, seeks to maintain peripheral testing while strategically adding an additional 10 central points to better assess central vision [11■].
To assess disease progression, perimeters employ both event-based and trend-based analytic software, which identify significant deviations from baseline readings and model rates of change, respectively. Event-based GPA software on the HFA, for example, uses a methodology developed from the Early Manifest Glaucoma Trial to flag potential visual field progression in comparison to baseline testing. The algorithm flags ‘possible progression’ when three of the same test point locations in the 24–2 pattern exhibit statistically significant decrease in sensitivity on two consecutive tests, and ‘probable progression’ on three consecutive tests [12,13]. This modality requires only two baseline examinations, and is sensitive to focal and/or early areas of progression despite apparent global stability [13]. However, event-based analyses are prone to bias from outlier results, and do not take fluctuation amplitude into account. Trend-based progression analysis software calculates linear regression models of global performance indices such as mean deviation or visual field index (VFI) over time to project future probabilistic rates of progression. Trend-based analysis is less sensitive to outlier tests and can be adjusted to exclude them. However, it requires multiple test sessions spread over several years to yield meaningful regressions and is less sensitive to focal progression. Recent advances in artificial intelligence and machine learning may offer additional possibilities for predicting future progression by combining baseline visual field data with other clinical datapoints [14■].
In spite of innovations such as faster testing patterns and pupil tracking, standard automated perimetry (SAP) is still heavily dependent on patient and operator factors, which commonly introduce artifact and both inter-test and intra-test variability. Neurocognitive decline is a frequent comorbidity in the glaucoma patient population, and is an important source of visual field variability [15]. Perimetry is time-intensive for patients and practices, inducing logistical and economic constraints on testing frequency. Test frequency should be tied to disease severity and overall suspicion for progression, with every attempt made to utilize consistent test settings to enable useful comparisons.
DETECTING DISEASE PROGRESSION IN MILD GLAUCOMA
Mild glaucoma is defined by glaucomatous optic nerve abnormalities without SAP visual field abnormalities (International Classification of Diseases/ICD-10), or by a visual field mean deviation less than −6 dB (Hodapp-Parrish-Anderson/HPA Criteria). The nature of the structure-function relationship in early glaucoma – and especially the question of which element can be detected first – has been the subject of intensive study and debate. Classic investigations comparing optic disc cupping and visual field change [16], and subsequent clinical-pathologic case studies of cadaveric eyes [17], demonstrated that RGC loss can precede detectible visual field changes, coining the concept of ‘preperimetric glaucoma’. However, other studies have additionally demonstrated perimetric progression in ocular hypertension prior to structural changes [18]. The current understanding is that either structural or functional loss may be initially detected in early glaucoma, highlighting the importance of carefully monitoring for both [5,19,20].
Changes in RNFL thickness can be a sensitive metric for evaluating early-stage glaucomatous changes [21]. It is important, however, to assess the numerical thickness values for abnormal asymmetry and progression. Overreliance on OCT colour coding may lead a clinician to overlook early thinning, known as ‘green disease’ or ‘progression in the green’ (Fig. 1) [22], or to falsely diagnose glaucoma based on yellow or red coding of anatomic disc variants [23]. Comparison of the two eyes of the same patient is important; an inter-eye difference in average RNFL thickness of greater than 9 μm has been shown to be a statistically significant indicator of early glaucoma [24].
FIGURE 1.

Progression in the green in mild glaucoma. Cirrus SD-OCT scan from the same patient at baseline (a, left) and at 4-year follow-up (b, right). At follow up, average and quadrant RNFL measurements remain “green,” signalling normal-range results as compared with age-matched normative values, yet focal and asymmetric RNFL thinning is seen superiorly and inferiorly (red arrows). Furthermore, the average RNLF thickness has decreased from 103 to 95 mm. New macular ganglion cell layer and inner plexiform layer (GCL+IPL) thinning was also noted inferiorly (blue arrows), and a small new paracentral visual field defect was noted on perimetry (green arrow), all suggestive of glaucomatous progression. Adapted from original images provided by Donald Budenz, MD, MPH.
As the macula has high density of RGCs, early glaucomatous damage can be detected in this region with OCT as well [5]. The rate of average GCIPL thinning has been shown to be greater in early glaucoma progressors, and can be assessed with OCT GPA [25]. Some studies have, however, suggested that GCIPL parameters are of lower predictive value in preperimetric glaucoma than RNFL metrics [26]. Other studies suggest that an indexed combination of OCT datapoints from the ONH and macula may be more sensitive for detecting early glaucomatous changes than any individual metric by itself [7]. Finally, studies using newer wide-field OCT technology – simultaneously capturing a large area of the posterior pole – have shown some promise in detecting early glaucoma [27■,28].
Early visual field changes in mild glaucoma often affect the nasal aspect of the visual field as a function of early arcuate nerve fibre damage [29]. Early visual field progression patterns can vary, however, and their initial location and propagation have been associated with different ophthalmic and systemic factors [30]. A significant proportion of early visual field defects can also revert to normal on subsequent examinations [31]. Clinicians must therefore be mindful of any new, enlarging or persistently deepening defect of the visual field, regardless of location. Event-based progression analysis can be very useful in early glaucoma to detect areas of early/focal progression, even when global indexes and associated trend-based analyses appear to be relatively stable [13].
The optimal frequency of visual field testing in early/mild glaucoma is a subject of some debate. An average of three annual tests in the first 2 years after glaucoma diagnosis has demonstrated good sensitivity to promptly identify patients with rapidly progressing disease (e.g. mean deviation decline by greater than 1 dB/year) [32]. Other studies have suggested two consecutive baseline tests followed by two yearly tests is reasonably specific to rule out rapid progression [33]. Recent machine learning-based algorithms have demonstrated the potential to predict rapid progressors even with only one baseline visual field test, when combined with additional clinical data [34■].
Finally, the importance of a complete ophthalmic history and physical examination should be emphasized in early glaucoma. Risk factors such as family history of glaucoma and measurement of central corneal thickness (CCT) are vital for risk stratification, as is the more recent inclusion of corneal hysteresis to inform a patient’s risk profile [35]. Intraocular pressure should be carefully measured at every encounter. Important qualitative and characteristic changes can be detected through direct and photographic examination of the optic nerve. In particular, disc haemorrhage is well documented as a marker of progressing and/or inadequately controlled glaucoma [36]. Other important qualitative physical examination findings include monitoring for progression of focal rim thinning/notching and peripapillary atrophy. Serial fundus photography is useful to catalogue and monitor these qualitative findings, especially for long-term monitoring of glaucoma suspects [37].
DETECTING DISEASE PROGRESSION IN MODERATE GLAUCOMA
Moderate glaucoma is defined as optic nerve damage consistent with glaucoma and glaucomatous visual field abnormalities in one hemifield greater than 5 degrees from fixation (ICD-10). HPA criteria define moderate glaucoma based on a visual field mean deviation between −6 and −12 dB and certain PSD criteria. Structural and functional deficits are typically present; a key challenge in this stage is the variability in how quickly, and in what order, these deficits progress. Both event and trend-based guided progression analyses can be of great use in detecting meaningful progression (Fig. 2). However, often structural and functional changes do not occur simultaneously.
FIGURE 2.

Guided progression analysis in moderate glaucoma. Progression is detected by event-based (a) and trend-based (b) GPA output from a Humphrey Field Analyzer for a single patient. Unshaded triangles in (a) represent statistically significant deviation from a baseline test; half-shaded triangles represent statistically significant deviation from baseline on two consecutive tests. The linear regression in (b) depicts a rate of progression of −1.5% VFI per year (95% CI 4.4%/year). Adapted from original images provided by Grace Richter, MD, MPH.
In the moderate stage of glaucoma, the correlation between RGC count, average RNFL thickness and SAP mean deviation is nonlinear, with early RGC loss translating to relatively larger changes in RNFL thickness than mean deviation decline. This suggests that OCT is comparatively more sensitive to glaucoma progression than SAP in earlier stages of disease. Increasingly rapid progression of VF loss can subsequently be encountered as RNFL thinning plateaus. This asymmetric structure-then-function pattern of progression has been termed the ‘broken stick model’; Liu et al. [38] found that detectible visual field loss began after the neuroretinal rim reached one-third normal thickness. Thus, a finding of OCT RNFL and/or macular progression should be noted and treated even in the absence of VF change, as such changes often predict future VF progression [39■]. The rate of OCT progression bears prognostic value with rapid thinning, defined in one recent study as mean OCT RNFL thinning greater than 2 μm/year, correlating with subsequent visual field defects [40■]. Of note, age-related mean RNFL thinning proceeds at comparably modest rate of nearly 0.2–0.5 μm/year [3,6,41,42].
Detection of progression in moderate glaucoma often carries significant consequences, as management may require more aggressive medical or procedural interventions. In cases of uncertainty, the use of additional corroborating data points is useful. True progression is of higher likelihood, for example, in the setting of intraocular pressure persistently elevated above target [43■■,44■], or in the setting of recent disc haemorrhages. Inter-test variability increases proportionally with glaucoma severity; [45] as such, there should be a low threshold for repeating tests to confirm progression. In addition, clinicians should consider supplemental testing, such as a 10–2 HVF to assess possible new central field loss noted on a 24–2 testing scheme.
DETECTING DISEASE PROGRESSION IN SEVERE GLAUCOMA
Severe glaucoma is defined as glaucomatous visual field abnormalities affecting both hemifields and/or losses within 5 degrees of fixation (ICD-10) or mean deviation less than −12 dB (HPA). Test-retest variability (long-term fluctuation) increases in more advanced stages of glaucoma; variability can even occur with increasing frequency during a single examination (short-term fluctuation) [13,46]. Structural and functional losses ultimately approach the limit of our machines’ ability to reliably measure further losses. This lower bound threshold at which a metric’s further progression is no longer detectible is termed the ‘floor effect’. The floor effect does not represent a biological phenomenon, but a technological limitation. Measurements should be viewed with caution when approaching the floor range, as a sequence of similar readings can easily be misinterpreted as ‘stable’ when progression is still occurring (Fig. 3). Exact floor effect thresholds vary across devices; for OCT, average RNFL floors occur in the range of 40–50 μm [47]. SAP exhibits a relatively large range given the logarithmic nature of its measurement scale, and may continue to show progression beyond the RNFL floor.
FIGURE 3.

Floor effect in severe glaucoma. Series of OCT RNFL and OCT macula-ganglion cell plus inner plexiform layer (GCIPL) scans and trend-based GPA of visual fields for a patient with advanced glaucoma. As the OCT RNFL (a) approaches a measurement floor, there is minimal variability, giving the appearance of stability. Sequential visual fields (c) also demonstrated a floor effect, with high variability and without definitive progression. However, macular/GCIPL OCT (b), demonstrated progressive thinning, (red arrows) indicative of continued disease progression. Adapted from original images provided by David Greenfield, MD.
Late-stage structural progression can still be measured via OCT measurements of the macula, as GCIPL tissue often remains above the measurement floor even after RNFL measurement reaches the floor (Fig. 3) [48,49]. Furthermore, the central macula has been shown to correlate relatively well with the central 10 degrees of the visual field, rendering it a clinically meaningful target to monitor progression even for patients with diffuse RNFL thinning and profoundly constricted peripheral visual fields [50].
For patients with visual field impairments that exceed the limitations of SAP, nonstandard perimetric tests can still be useful. When the paracentral 10 degrees are impacted, a centre-focused testing strategy such as HVF 10–2 may better assess progression. Increasing the perimeter stimulus size from the standard size III to size V has additionally been shown to reduce test variability in advanced glaucoma [51]. Switching to this new testing pattern does, however, impose its own specific challenges, including loss of common metrics such as mean deviation, PSD and VFI, and an inability to reliably compare results with prior SAP tests. With even further progression, kinetic (Goldmann) perimetry can also offer advantages over static perimetry for certain patients, providing sharper demarcation of visual field boundaries and more control in cases wherein fixation is a challenge [52]. However, comparison of kinetic perimetry is mostly qualitative.
In the most advanced cases, there may come a point where structural and functional tests are insufficient to yield useful information for progression monitoring. Instead, information on progression may be gleaned from patients’ subjective reports of their visual acuity and visual function. Grounding inquiries in concrete examples, such as a patient’s activities of daily life and safety, may yield meaningful information [53]. Subjective reporting is, nonetheless, quite variable, and self-reported progression may not reflect biological changes.
CONCLUSION
Structural and functional measurement techniques in glaucoma have evolved considerably over the recent years, providing clinicians with an expansive toolkit to detect glaucomatous progression. Despite the recent proliferation of complex computer algorithms and high-definition imaging modalities, no single metric has demonstrated universal superiority. The detection of glaucomatous progression at all stages of disease relies on clinical judgement, derived from the critical assessment of both structural and functional testing in conjunction with intraocular pressure measurements, clinical experience and each patient’s specific clinical context.
KEY POINTS.
Surveillance for progression of glaucoma necessitates careful monitoring of both structural and functional parameters at all disease stages.
Structural and functional changes in glaucoma can occur simultaneously or asynchronously, and either may precede the other.
No single metric is sufficient for monitoring glaucomatous progression; data must be drawn from multiple modalities, and interpreted within the context of a patient’s particular circumstances.
Although novel devices and analytic software have greatly expanded diagnostic capabilities, expert judgement must still be employed to identify artifact and algorithmic errors, and to adapt to measurement limitations in both early and late-stage glaucoma.
Acknowledgements
The authors thank Drs Donald Budenz, Grace Richter and David Greenfield for contributing key components of the figures presented in this manuscript.
Financial support and sponsorship
There were no sources of funding used in the preparation of this manuscript.
Footnotes
Conflicts of interest
Daniel Liebman has no potential conflicts of interest to disclose. Joanne Wen has no conflicts of interest to disclose. Lucy Shen is a consultant for FireCyte Therapeutics and AbbVie, with no bearing on the content of this manuscript. The authors otherwise have no relevant conflicts of interest to disclose.
REFERENCES AND RECOMMENDED READING
Papers of particular interest, published within the annual period of review, have been highlighted as:
■ of special interest
■■ of outstanding interest
- 1.Weinreb RN, Aung T, Medeiros FA. The pathophysiology and treatment of glaucoma: a review. JAMA 2014; 311:1901–1911. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Huang D, Swanson EA, Lin CP, et al. Optical coherence tomography. Science 1991; 254:1178–1181. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Tatham AJ, Medeiros FA. Detecting structural progression in glaucoma with optical coherence tomography. Ophthalmology 2017; 124(12 Suppl):S57. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Leung CK, Cheung CY, Weinreb RN, et al. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: a variability and diagnostic performance study. Ophthalmology 2009; 116:1257–1263; e2. [DOI] [PubMed] [Google Scholar]
- 5.Hood DC, Raza AS, de Moraes CGV, et al. Glaucomatous damage of the macula. Prog Retin Eye Res 2013; 32:1–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Chen TC, Hoguet A, Junk AK, et al. Spectral-domain OCT: helping the clinician diagnose glaucoma: a report by the American Academy of Ophthalmology. Ophthalmology 2018; 125:1817–1827. [DOI] [PubMed] [Google Scholar]
- 7.Mwanza J-C, Warren JL, Budenz DL. Combining spectral domain optical coherence tomography structural parameters for the diagnosis of glaucoma with early visual field loss. Invest Ophthalmol Vis Sci 2013; 54:8393–8400. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Liu Y, Simavli H, Que C, et al. Patient characteristics associated with artifacts in spectralis optical coherence tomography imaging of the retinal nerve fiber layer in glaucoma. Am J Ophthalmol 2015; 159:565–576; e2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Leite MT, Rao HL, Weinreb RN, et al. Agreement among spectral-domain optical coherence tomography instruments for assessing retinal nerve fiber layer thickness. Am J Ophthalmol 2011; 151:85–92; e1. [DOI] [PubMed] [Google Scholar]
- 10.Mahmoudinezhad G, Moghimi S, Proudfoot JA, et al. Effect of testing frequency on the time to detect glaucoma progression with OCT and OCT angiography. Am J Ophthalmol 2022; S0002–9394:00348–00358. [DOI] [PMC free article] [PubMed] [Google Scholar]; ■ This study demonstrated that a twice-yearly OCT testing rate was generally sufficient to detect clinically significant glaucoma progression. Circumpapillary retinal nerve fibre layer with OCT and circumpapillary capillary density with OCT angiography demonstrated comparable performance.
- 11.Hong JW, Baek MS, Lee JY, et al. Comparison of the 24–2 and 24–2C visual field grids in determining the macular structure-function relationship in glaucoma. J Glaucoma 2021; 30:887–894. [DOI] [PubMed] [Google Scholar]; ■ This study demonstrated potential superiority of a 24–2C HVF grid over the standard 24–2 pattern to demonstrate structure-function relationships in glaucomatous eyes.
- 12.Heijl A, Leske MC, Bengtsson B, et al. Measuring visual field progression in the Early Manifest Glaucoma Trial. Acta Ophthalmol Scand 2003; 81:286–293. [DOI] [PubMed] [Google Scholar]
- 13.Aref AA, Budenz DL. Detecting visual field progression. Ophthalmology 2017; 124(12 Suppl):S51–S56. [DOI] [PubMed] [Google Scholar]
- 14.Dixit A, Yohannan J, Boland MV. Assessing glaucoma progression using machine learning trained on longitudinal visual field and clinical data. Ophthalmology 2021; 128:1016–1026. [DOI] [PMC free article] [PubMed] [Google Scholar]; ■ This study demonstrated the proficiency of convolutional long short-term memory machine-learning models to assess glaucoma progression. Supplementing machine learning models with additional clinical data enhanced model performance.
- 15.Diniz-Filho A, Delano-Wood L, Daga FB, et al. Association between neurocognitive decline and visual field variability in glaucoma. JAMA Ophthalmol 2017; 135:734–739. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Read RM, Spaeth GL. The practical clinical appraisal of the optic disc in glaucoma: the natural history of cup progression and some specific disc-field correlations. Trans Am Acad Ophthalmol Otolaryngol 1974; 78:O255–O274. [PubMed] [Google Scholar]
- 17.Quigley HA, Addicks EM, Green WR. Optic nerve damage in human glaucoma. III. Quantitative correlation of nerve fiber loss and visual field defect in glaucoma, ischemic neuropathy, papilledema, and toxic neuropathy. Arch Ophthalmol 1982; 100:135–146. [DOI] [PubMed] [Google Scholar]
- 18.Strouthidis NG, Grimm J, Williams GA, et al. A comparison of optic nerve head morphology viewed by spectral domain optical coherence tomography and by serial histology. Invest Ophthalmol Vis Sci 2010; 51:1464–1474. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.World Glaucoma Association. 8th Consensus Meeting: progression of glaucoma [Internet]. [cited 12 June 2022]. https://wga.one/wga/consensus-8/. [Accessed 2nd October 2022].
- 20.Malik R, Swanson WH, Garway-Heath DF. The structure-function’ relationship in glaucoma – past thinking and current concepts. Clin Exp Ophthalmol 2012; 40:369–380. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Harwerth RS, Vilupuru AS, Rangaswamy NV, et al. The relationship between nerve fiber layer and perimetry measurements. Invest Ophthalmol Vis Sci 2007; 48:763–773. [DOI] [PubMed] [Google Scholar]
- 22.Sayed MS, Margolis M, Lee RK. Green disease in optical coherence tomography diagnosis of glaucoma. Curr Opin Ophthalmol 2017; 28:139–153. [DOI] [PubMed] [Google Scholar]
- 23.Lee EJ, Han JC, Kee C. Nasalised distribution of peripapillary retinal nerve fibre layers in large discs. Br J Ophthalmol 2017; 101:1643–1648. [DOI] [PubMed] [Google Scholar]
- 24.Mwanza J-C, Durbin MK, Budenz DL, et al. Interocular symmetry in peripapillary retinal nerve fiber layer thickness measured with the Cirrus HD-OCT in healthy eyes. Am J Ophthalmol 2011; 151:514–521; e1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Shin JW, Sung KR, Lee GC, et al. Ganglion cell-inner plexiform layer change detected by optical coherence tomography indicates progression in advanced glaucoma. Ophthalmology 2017; 124:1466–1474. [DOI] [PubMed] [Google Scholar]
- 26.Begum VU, Addepalli UK, Yadav RK, et al. Ganglion cell-inner plexiform layer thickness of high definition optical coherence tomography in perimetric and preperimetric glaucoma. Invest Ophthalmol Visual Sci 2014; 55:4768–4775. [DOI] [PubMed] [Google Scholar]
- 27.Kim H, Park HM, Jeong HC, et al. Wide-field optical coherence tomography deviation map for early glaucoma detection. Br J Ophthalmol 2021; 21:. doi: 10.1136/bjophthalmol-2021-319509. [DOI] [PubMed] [Google Scholar]; ■ This study demonstrated the potential of the wide-field SS-OCT modality to distinguish early glaucomatous changes by applying a normative wide-field database to 400 eyes, half of which were healthy and half of which had early glaucoma.
- 28.Kim H, Lee JS, Park HM, et al. A wide-field optical coherence tomography normative database considering the Fovea-Disc relationship for glaucoma detection. Transl Vis Sci Technol 2021; 10:7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Werner EB, Beraskow J. Peripheral nasal field defects in glaucoma. Ophthalmology 1979; 86:1875–1878. [DOI] [PubMed] [Google Scholar]
- 30.Kim JM, Kyung H, Shim SH, et al. Location of initial visual field defects in glaucoma and their modes of deterioration. Invest Ophthalmol Vis Sci 2015; 56:7956–7962. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Keltner JL, Johnson CA, Levine RA, et al. Normal visual field tests following glaucomatous visual field endpoints in the Ocular Hypertension Treatment Study (OHTS). Arch Ophthalmol 2005; 123:1201–1206. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Crabb DP, Russell RA, Malik R, et al. Frequency of visual field testing when monitoring patients newly diagnosed with glaucoma: mixed methods and modelling [Internet]. Southampton (UK): NIHR Journals Library; 2014. [cited 12 June 2022]. (Health Services and Delivery Research). http://www.ncbi.nlm.nih.gov/books/NBK259972/. [Accessed 2nd October 2022]. [PubMed] [Google Scholar]
- 33.Wu Z, Saunders LJ, Daga FB, et al. Frequency of testing to detect visual field progression derived using a longitudinal cohort of glaucoma patients. Ophthalmology 2017; 124:786–792. [DOI] [PubMed] [Google Scholar]
- 34.Shuldiner SR, Boland MV, Ramulu PY, et al. Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning. PLoS One 2021; 16:e0249856. [DOI] [PMC free article] [PubMed] [Google Scholar]; ■ This study demonstrated the ability of a machine learning model to accurately predict fast-progressors using a single visual field baseline in conjunction with additional clinical data. This suggests significant potential for artificial intelligence/machine learning platforms to reduce the number of time-consuming visual field tests to risk-stratify at the time of diagnosis.
- 35.Zimprich L, Diedrich J, Bleeker A, et al. Corneal hysteresis as a biomarker of glaucoma: current insights. Clin Ophthalmol 2020; 14:2255–2264. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Ishida K, Yamamoto T, Sugiyama K, et al. Disk hemorrhage is a significantly negative prognostic factor in normal-tension glaucoma. Am J Ophthalmol 2000; 129:707–714. [DOI] [PubMed] [Google Scholar]
- 37.http://fyra.io. Is there still a role for optic disc photography? [Internet]. Glaucoma Today. Bryn Mawr Communications; [cited 12 June 2022]. https://glaucomatoday.com/articles/2016-may-june/is-there-still-a-role-for-optic-disc-photography. [Accessed 2nd October 2022]. [Google Scholar]
- 38.Liu WW, McClurkin M, Tsikata E, et al. Three-dimensional neuroretinal rim thickness and visual fields in glaucoma: a broken-stick model. J Glaucoma 2020; 29:952–963. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Nouri-Mahdavi K, Mohammadzadeh V, Rabiolo A, et al. Prediction of visual field progression from OCT structural measures in moderate to advanced glaucoma. Am J Ophthalmol 2021; 226:172–181. [DOI] [PMC free article] [PubMed] [Google Scholar]; ■ This study demonstrated the ability to accurately predict visual field progression based on baseline and longitudinal OCT structural data. Models utilizing GCIPL-based metrics outperformed models that only used RNFL-based metrics.
- 40.Swaminathan SS, Jammal AA, Berchuck SI, et al. Rapid initial OCT RNFL thinning is predictive of faster visual field loss during extended follow-up in glaucoma. Am J Ophthalmol 2021; 229:100–107. [DOI] [PMC free article] [PubMed] [Google Scholar]; ■ This study used 1150 eyes from the Duke Glaucoma Registry to demonstrate that rapid RNFL thinning over their first five OCT scans predicted concurrent and subsequent rates of visual field decline. This finding suggests that ‘rapid progressors’ can be identified early in their treatment course, with implications for recommended treatment intensity.
- 41.Celebi ARC, Mirza GE. Age-related change in retinal nerve fiber layer thickness measured with spectral domain optical coherence tomography. Invest Ophthalmol Vis Sci 2013; 54:8095–8103. [DOI] [PubMed] [Google Scholar]
- 42.Leung CKS, Yu M, Weinreb RN, et al. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: a prospective analysis of age-related loss. Ophthalmology 2012; 119:731–737. [DOI] [PubMed] [Google Scholar]
- 43.Jammal AA, Thompson AC, Mariottoni EB, et al. Impact of intraocular pressure control on rates of retinal nerve fiber layer loss in a large clinical population. Ophthalmology 2021; 128:48–57. [DOI] [PMC free article] [PubMed] [Google Scholar]; ■■ This study used a large population database to establish a linear relationship between IOP and average rate of RNFL thinning. These findings provide quantitative support for the establishment of stringent individualized target pressures.
- 44.Villasana GA, Bradley C, Ramulu P, et al. The effect of achieving target intraocular pressure on visual field worsening. Ophthalmology 2022; 129:35–44. [DOI] [PMC free article] [PubMed] [Google Scholar]; ■ This study demonstrated a stepwise relationship between IOP above target pressure and visual field worsening, with ramifications in eyes with moderate glaucoma.
- 45.Yohannan J, Wang J, Brown J, et al. Evidence-based criteria for assessment of visual field reliability. Ophthalmology 2017; 124:1612–1620. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Flammer J, Drance SM, Zulauf M. Differential light threshold: short- and long-term fluctuation in patients with glaucoma, normal controls, and patients with suspected glaucoma. Arch Ophthalmol 1984; 102:704–706. [DOI] [PubMed] [Google Scholar]
- 47.Bowd C, Zangwill LM, Weinreb RN, et al. Estimating optical coherence tomography structural measurement floors to improve detection of progression in advanced glaucoma. Am J Ophthalmol 2017; 175:37–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Bowd C, Zangwill LM, Weinreb RN, et al. Estimating OCT structural measurement floors to improve detection of progression in advanced glaucoma. Am J Ophthalmol 2017; 175:37–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Belghith A, Medeiros FA, Bowd C, et al. Structural change can be detected in advanced-glaucoma eyes. Invest Ophthalmol Vis Sci 2016; 57:OCT511–OCT518. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Ohkubo S, Higashide T, Udagawa S, et al. Focal relationship between structure and function within the central 10 degrees in glaucoma. Invest Ophthalmol Vis Sci 2014; 55:5269–5277. [DOI] [PubMed] [Google Scholar]
- 51.Gardiner SK, Demirel S, Goren D, et al. The effect of stimulus size on the reliable stimulus range of perimetry. Transl Vis Sci Technol 2015; 4:10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Ma X, Tang L, Chen X, et al. Periphery kinetic perimetry: clinically feasible to complement central static perimetry. BMC Ophthalmol 2021; 21:343. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.de Moraes CG, Liebmann JM, Medeiros FA, et al. Management of advanced glaucoma: characterization and monitoring. Survey Ophthalmol 2016; 61:597–615. [DOI] [PubMed] [Google Scholar]
