Glaucoma Data Standards

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A common challenge facing glaucoma clinical care and research is the collection and organization of relevant data related to an individual or group of individual's disease.[1][2][3] The uptake in use of electronic health records (EHR) has improved the accessibility of clinical history and examination data to the glaucoma practitioner; however, sharing important data between computer systems remains a challenge. In the clinical setting, this is exemplified by the segregation of clinical exam data stored in the EHR from diagnostic testing data which is stored in a picture archive and communication system (PACS). In order to facilitate communication between the multiple systems, they must agree on a set of data standards used to describe diagnostic tests, findings, diagnoses, and image data.

The challenge is multiplied in glaucoma research where a hundreds to thousands of individual patient's data needs to be extracted from multiple EHRs (and PACS) and be transformed into a common format.[4] Only then can the data be used to answer medically interesting questions. For high quality research utilizing machine learning, the inherent risk of biases (or non-generalizability) in training data sets necessitates the use of clinical and diagnostic testing data from a diverse and geographically widespread patient population. Data standards facilitate the collection and integration from multiple EHRs which is often needed to obtain adequately diverse source data.[5][6]

Patient safety and healthcare spending is also impacted by the (lack of) standardized data representations. The AAO Preferred Practice Pattern (PPP) guidelines for primary open-angle glaucoma[7] and suspected glaucoma[8]recommend the use of automated perimetry and RNFL evaluation to assess the progression of disease. The POAG PPP states that "repeat and confirmatory visual field examinations are recommended for test results that are unreliable or show a new glaucomatous defect" before changing treatment. To reduce waste in healthcare, diagnostic testing information needs to be effectively communicated between referring providers. The widespread use of data standards for reporting on VF and OCT measures (including reliability) is essential to the reduction of wasted duplication or delays in care.

Additional work is necessary to create and promote data standards for use in glaucoma and ophthalmology in general. Clinical exam measures as fundamental as visual acuity present challenges when extracted from EHRs for use in research.[9] In other areas, data standards exist but are poorly implemented by diagnostic device vendors.[10]

Glaucoma Imaging Data

Visual Field (VF)

Visual fields are one of the primary diagnostic imaging tests used to assess glaucoma and other optic neuropathies.

Table 2. Examples of visual field devices, testing strategies, patterns, and reported metrics.
Vendor Device Testing Strategies Testing Patterns Testing Metrics Analysis Features
Zeiss Humphrey Field Analyzer 3
  • 10-2
  • 24-2
  • 24-2c
  • 30-2
  • 60-4
  • Nasal Step
  • Foveal threshold
  • Visual field index (VFI)[15]
  • Mean deviation (MD)
  • Pattern standard deviation (PSD)
  • Glaucoma hemifield test (GHT)[16]
  • Guided progression analysis (GPA)
Haag-Streit Octopus 900
  • Tendency oriented perimetry (TOP)
  • Dynamic
  • Normal (4-2-1 bracketing)
  • Goldmann kinetic perimetry
  • Glaucoma G1-Program
  • Glaucoma G2-Program
  • 24-2
  • 30-2
  • Macula M-Program
  • 10-2
  • Estermann (monocular/binocular)
  • Mean sensitivity (MS)
  • Mean defect (MD)
  • Loss variance (sLV)
  • Cluster trend
  • Polar trend

Optical Coherence Tomography (OCT)

Vendor Device Technology Testing Metrics Analysis Features
Zeiss Stratus
  • Frequency domain OCT
  • Circular scan of 3.4 mm diameter around optic disc
  • RNFL thickness
    • Average
    • Quadrant
    • Clock-hours
  • Macular GCL/IPL thickness
    • Averag
  • Cup-to-disc ratio
  • RNFL thickness TSNIT graph over time
Zeiss Cirrus 5000 & 6000
  • Spectral domain OCT
  • 100k A-scans/second (Cirrus 6000)
  • 27-68k A-scans/second (Cirrus 5000)
  • 5 υm axial resolution
  • 15 υm lateral resolution
  • RNFL thickness
    • Average
    • Quadrant
    • Clock-hours
  • Macular GCL/IPL thickness
    • Average
    • 6-sector
  • Cup-to-disc ratio
  • RNFL thickness deviation maps
  • Ganglion cell analysis
  • Combined GCL/IPL and RNFL thickness deviation maps
  • Guided progression analysis (GPA)
Heidelberg Engineering Spectralis OCT
  • Spectral domain OCT
  • 40 kHz scan rate
  • 7 υm axial resolution
  • 14 υm lateral resolution
  • 25-48 B-scans/second
  • RNFL thickness
    • Average
    • Quadrant
    • 6-sector
  • Asymmetry analysis
  • Progression analysis
Topcon Maestro2
  • Spectral domain OCT
  • 50k A-scans/second
  • <6 υm axial resolution
  • <20 υm lateral resolution
  • Optic nerve, macula, and anterior segment imaging
  • cpRNFL thickness
    • Average
    • Quadrant
    • Clock-hours
  • Macular GCL+IPL thickness
    • Average
    • 6-sector
  • Cup-to-disc ratio
  • RNFL thickness trend analysis
  • GCL+ and GCL++ thickness maps
  • GCL thickness trend analysis

Flowchart for Data Extraction

Relevant Data Standards

SNOMED — Systematized Nomenclature of Medicine

SNOMED is a comprehensive clinical terminology developed to describe all concepts related to medicine. Each concept in SNOMED is assigned a unique code, provides unambiguous meanings, is associated with related concepts, and is maintained on an ongoing basis.[17] In 2004 the National Library of Medicine recognized its importance and made SNOMED-CT freely available in the US. A study in 2005 was found to have the broadest coverage in ophthalmology (among 5 controlled terminologies available at the time) and has since become the standard for describing ophthalmic terms.[18]

The SNOMED CT browser is available at which provides easy online access to the hierarchy of disorders, findings, observations, procedures, etc. For example, glaucoma (as a disorder) is represented as SCTID: 23986001 and found at The hierarchy of glaucoma related disorders can be navigated in the online browser to identify more- or less-specific disorders.

Table 3. Examples of SNOMED-CT terms
Semantic Meaning Example SCTID
Disorder Secondary glaucoma due to aphakia 15374009
Finding Raised intraocular pressure 112222000
Observable entity Snellen visual acuity 422673001
Procedure Injection of filtering bleb following glaucoma surgery 428494000
Physical object Nonvalved ophthalmic drainage device 410444004
Substance Latanoprost 386926002
Medicinal product Product containing only latanoprost 776481004
Qualifier value Intracameral route 418821007

DICOM — Digital Imaging and Communications in Medicine

The Digital Imaging and Communications in Medicine (DICOM) standard is the standard for communicating medical imaging tests and results.[19] All EHR and PACS software needs to be DICOM compliant.[20] In addition to storing raw imaging data alongside computed metrics from the diagnostic test, DICOM has capabilities for producing structured reports that encode what has been found in ophthalmic images using hierarchical lists of findings, coded or numeric content in addition to plain text, and presenting relationships between features present in the image.[21]

A list of ophthalmology-related DICOM supplements and vendors' conformance (statements) with these standards can be found at the AAO: A number of DICOM supplements establish standards for communicating ophthalmic results of interest to glaucoma specialists and researchers (Table 1).

Table 1. A few of the many DICOM standards relevant to storing and communicating glaucoma-relevant information.
Supplement Date Published Description
Ophthalmic Tomography Image Storage SOP Class (Supplement 110) 2007 Describes the storage of the retinal nerve fiber layer (RNFL) in addition to the characterization of the anterior chamber angle exam from tomography scans through the anterior segment.
Ophthalmic Visual Field (OPV) Static Perimetry Measurements Storage SOP Class (Supplement 146) 2010 Describes the storage and representation of visual field data including quality measures (fixation losses, false positive rate, etc.), foveal sensitivity, mean sensitivity, and mean deviation.
Ophthalmic Thickness Map Storage SOP Class (Supplement 152) 2011 Describes the storage and representation of thickness measurements such as retinal nerve fiber layer (RNFL) maps.

LOINC — Logical Observation Identifiers Names and Codes

OMOP CDM — Observational Medical Outcomes Partnership Common Data Model

The Observational Health Data Sciences and Informatics program (OHDSI) is a collaborative to leverage an international network of researchers and health databases through large-scale analytics. OHDSI maintains the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) designed to standardize the structure and content of observation data to enable efficient research analyses. To facilitate the comparison and analysis of multi-institutional data, the All of Us Research Program transforms ingested EHR data into the OMOP CDM.[22]

The OHDSI Eye Care & Vision Research workgroup ( exists to advance the development and implementation of data standards in ophthalmology, optometry, and vision sciences.[23] It's aim is to support studies using observational ophthalmic data for generating insights to improve health and vision outcomes. Amongst its accomplishments are conducting gap analyses of two large, well-known EHR systems for eye care to examine where OMOP standards are lacking for commonly used data elements. The workgroup has had presences at AAO and ARVO annual meetings. It is collaborating with Verana Health on transforming the AAO Intelligent Research In Sight (IRIS) Registry to use the OMOP CDM.

Tools & Code

Hypothetical Use Cases for Using Data Standards

Clinical Example

Dozens of patients are seen each day in a busy glaucoma clinic. Most patients will have had multiple glaucoma imaging tests done over months to years requiring comparison to assess for glaucoma progression. An inefficient workflow would require opening the PACS each time for each patient and manually viewing past tests to assess for the rate of progression over time. A more efficient workflow would extract meaningful metrics from the visual field and OCT and import those into the EHR to colocate glaucoma metrics alongside the patient's vision and IOP history. Having an imaging silo or PACS alone for this diagnostic data is suboptimal because it won't have the patient's clinical exam, medication, or surgical history.

When visual field devices support existing DICOM standards, then a compliant PACS would be able to extract meaningful glaucoma metrics such as mean deviation and pattern standard deviation from the patient's clinical testing and share that data with the EHR through standard FHIR interfaces. The EHR would then be able to show the change in MD/PSD over time alongside the patient's IOP and VA.

Related EyeWiki Pages


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