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| ID | Type | Description | Link |
|---|---|---|---|
| PI23/01856 | Other Identifier | Institut d'Investigacions Biomèdiques August Pi i Sunyer |
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Two primary care-based screening systems will be tested to identify subjects with referrable glaucoma to hospital care.
Subjects between 45 to 64 years old living in the metropolitan area of Barcelona will be invited to participate in a one-time visit, with an optic disc examination and intraocular pressure (IOP).
The criteria for referring a patient will be the detection of glaucoma but with two different approaches depending on which Integrated Practice Unit (IPU) the patients will be allocated to: one arm using an Artificial Intelligence (AI) reading software of the optic disc picture; and the other one will base their referral after an ophthalmic examination performed by an ophthalmologist.
In both circuits, an optic nerve head photography will be obtained, and a masked reading center will be established to determine the ground truth for diagnosis.
This screening trial will explore the level of agreement between both systems and the cost-effectiveness of each of them.
Secondary analyses will include potential diagnostic composite scores (including other ancillary tests, such as optical coherence tomography images, that could maximize the screening process); the identification of population and disease characteristics (type of glaucoma, intraocular pressure) that could increase the effectivity and adherence to the screening process.
The purpose of this study is twofold: to validate in our population an Artificial Intelligence (AI) reading software of the optic disc picture, after comparing the estimated result (glaucoma/suspect/normal) to the ground truth; and to conduct a clinical trial where the level of agreement between both systems and the cost-effectiveness of each of them will be tested
In the first phase, a set of patients from our reference population will be selected. A standard-of-care ophthalmic examination with the usual ancillary tests to confirm or rule out the presence of glaucoma (including an optic disc retinography), will be performed. The patient (and the test) will be examined by a glaucoma specialist who will determine the status of the patient.
Then, the retinography will be analyzed by the AI software, providing the estimated result (glaucoma/suspect/normal). The level of agreement between the ground truth and the casted result will confirm the diagnostic accuracy.
In the second phase, a second set of patients will be recruited. In this case, the patients will be randomly allocated to either of the two arms of the study: In arm A the ancillary tests (including the retinography) will be performed, and the software will analyze the retinography, therefore providing the glaucoma status result. In arm B, the patients (and the test) will be examined by a glaucoma specialist who will then determine the status of the patient.
All the patients, irrespective of the diagnosis and the arm of the study will be then explored by another glaucoma specialist (reading center), who will be blinded to where the diagnosis comes from (AI software or glaucoma specialist), to the determine the level of agreement between the two screening systems
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| AI software examination (A) | Experimental | In arm A, the visual acuity, refraction status and the ancillary tests will be performed. The obtained optic disc retinography will be analyzed by the AI software to determine whether the patient was a glaucomatous, suspect or healthy patient. |
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| Ophthalmic examination (B) | Active Comparator | In arm B, the visual acuity, refraction status and the ancillary tests will be performed, and then the patients (and the test) will be examined by a glaucoma specialist who will determine the status of the patient (glaucomatous, suspect or healthy). |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Software analysis | Diagnostic Test | The tested AI software analyzes the optic disc retinography to determine if the patient is healthy, a glaucoma suspect, or a glaucoma case |
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| Measure | Description | Time Frame |
|---|---|---|
| Diagnostic agreement between the AI software and the ophthalmic examination | Level of agreement between the casted result by the AI software and the ophthalmic examination. This will be determined by the reading person (study chair) | 18 months |
| Health-Related Quality of Life (HRQoL) | Health-Related Quality of Life (HRQoL) assessed by Euro Quality of Life -5 Dimensions (EQ-5D), for each arm of the clinical trial. It consists of a visual analog scale, ranging from 0 to 100 (0 being the worst imaginable health and 100 the best health the patient can imagine) | 18 months |
| Demographics | Quantitative analysis of age, gender, ethnicity, and family history of glaucoma differences between the two arms | 18 months |
| Measure | Description | Time Frame |
|---|---|---|
| Intraocular pressure | Intraocular pressure values of glaucoma, suspects, and healthy patients of each arm of the study (in mmHg) | 18 months |
| Optical coherence tomography (OCT) | OCT values of glaucoma, suspects, and healthy patients of each arm of the study (thickness reported in micrometers) |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Nestor Ventura Abreu, MD, PhD | Contact | +34932275400 | 9336 | neventura@clinic.cat |
| Name | Affiliation | Role |
|---|---|---|
| Marta Pazos, MD, PhD | Glaucoma Consultant - Head of Ophthalmic Surgery Department | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Hospital Clínic - ICOF | Recruiting | Barcelona | Barcelona | 08028 | Spain |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 31344328 | Background | Hemelings R, Elen B, Barbosa-Breda J, Lemmens S, Meire M, Pourjavan S, Vandewalle E, Van de Veire S, Blaschko MB, De Boever P, Stalmans I. Accurate prediction of glaucoma from colour fundus images with a convolutional neural network that relies on active and transfer learning. Acta Ophthalmol. 2020 Feb;98(1):e94-e100. doi: 10.1111/aos.14193. Epub 2019 Jul 25. | |
| 34003218 |
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Individual Participant Data that could identify any patient will not be shared with other researchers. Patients will only be de-identified if a major health risk is detected, or under legal requirement by the competing authorities.
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| ID | Term |
|---|---|
| D005901 | Glaucoma |
| ID | Term |
|---|---|
| D009798 | Ocular Hypertension |
| D005128 | Eye Diseases |
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| Ophthalmologist examination | Diagnostic Test | The ophthalmologist (a glaucoma specialist) will analyze the tests and will examine the patient to determine if the patient is healthy, a glaucoma suspect or a glaucoma case |
|
| 18 months |
| Visual field | Visual field defects (mean deviation, in decibels) of glaucoma, suspects, and healthy patients of each arm of the study | 18 months |
| Cost-effective analysis of both screening methods | Cost-effective analysis will be conducted on each arm of the study comparing direct costs, and degree of visual impairment and comparing it to other screening programs (case-finding scenario) Mean costs and effects to estimate the Incremental Cost-Effectiveness Ratio (ICER, in euros, €) for artificial intelligence software screening versus ophthalmic examination will be compared | 18 months |
| Risk score with parameters associated with positive screening of glaucoma | Analysis of the demographics (present or absent), ocular characteristics (present or absent), OCT values (in micrometers), and visual field values (in decibels) that could be associated with an increased likeliness of glaucoma diagnosis The degree of contribution of each parameter will be analyzed in a multivariate logistic regression, and then a risk score will be created using a scale from 1 to 100 (with being 1 the lowest value and 100 being the highest) to show how each parameter contributes to a "positive glaucoma diagnosis" | 6 months |
| US Preventive Services Task Force; Davidson KW, Barry MJ, Mangione CM, Cabana M, Caughey AB, Davis EM, Donahue KE, Doubeni CA, Krist AH, Kubik M, Li L, Ogedegbe G, Owens DK, Pbert L, Silverstein M, Stevermer J, Tseng CW, Wong JB. Screening for Colorectal Cancer: US Preventive Services Task Force Recommendation Statement. JAMA. 2021 May 18;325(19):1965-1977. doi: 10.1001/jama.2021.6238. |
| 31904596 | Background | Tan NYQ, Friedman DS, Stalmans I, Ahmed IIK, Sng CCA. Glaucoma screening: where are we and where do we need to go? Curr Opin Ophthalmol. 2020 Mar;31(2):91-100. doi: 10.1097/ICU.0000000000000649. |