Keratoconus
Conditions
Brief summary
The purpose of this study is to create a database of keratoconic eyes with two or more corneal topographies/tomographies, at least 5 months apart
Detailed description
Title: Keratometry values such as K1, K2 and the angle between these two; Value and location of the thinnest corneal point; Pachymetry progression (radial change of pachymetry); IS value (i.e., ratio of average curvature in superior and inferior sections) Description: The primary endpoint is to obtain a database, containing at least two valid corneal biometry measurements (Scheimpflug) recorded at least 5 months apart, for a predetermined number of suitable keratoconus patients. These data will be used to create a personalized three-dimensional model of the cornea at each time point, which permits classifying corneas according to shape and stage, as well as assessing the influence of patient age, gender, family history and ophthalmic habits (e.g. eye rubbing) on keratoconus progression. Based on corneal changes over time, an estimate of the underlying biomechanical changes will be made. All these data will then be combined to develop software for automated keratoconus detection and progression risk assessment to help ophthalmologists decide when to perform crosslinking on their patients. The primary variables are the elevation parameters derived directly from the Scheimpflug measuring device export files, along with the demographic and medical information (if available). Time frame: 5 months Once a predictive model for keratoconus progression speed based on multiple measurements, this can be improved to make predictions based on a single measurement. Furthermore, the database obtained in this work will also be a valuable resource to analyse the variation in keratoconus shape, which may lead to an improved classification of keratoconus types
Interventions
None listed
Sponsors
European Vision Institute Clinical Research Network
University Hospital, Antwerp
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
12 Years to 40 Years
Inclusion criteria
* Age between 12-40 years. * Clinically diagnosed mild or moderate keratoconus in one or both eyes * Two or more Scheimpflug measurements (type Pentacam HR, Pentacam AXL, Ziemer Galilei, CSO Sirius) of good technical quality, separated at least five months apart.
Exclusion criteria
* Corneal scarring present in both eyes. * Known corneal or retinal pathologies, apart from keratoconus * Known ocular procedures/ treatments (including crosslinking) * Known systemic diseases (e.g. diabetes, MS, HIV/AIDS, hypertension,…), except allergies * Change in contact lenses between measurements (e.g. start wearing lenses, change from corneal to scleral lenses, etc.) * Fluorescein drops instilled into the eye before Scheimpflug measurement.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| To obtain a database, containing at least two valid corneal Scheimpflug measurements recorded at least 5 months apart, of 1500 keratoconus patients. | 5 months | These data will be used to create a personalized three-dimensional model of the cornea at each time point, which permits classifying corneas according to shape and stage, as well as assessing the influence of patient age, gender, family history and ophthalmic habits (e.g. eye rubbing) on keratoconus progression. Based on corneal changes over time, an estimate of the underlying biomechanical changes will be made. |
| To develop software to automatically detect keratoconus and estimate the keratoconus progression speed | 18 months | The data from Outcome 1 will be combined to develop software for automated keratoconus detection and progression risk assessment to help ophthalmologists decide when to perform cross-linking on their patients. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| To improve the predictive model for keratoconus progression speed so it can work using only one single measurement | 18 months | Once a predictive model for keratoconus progression speed based on multiple measurements, this can be improved to make predictions based on a single measurement. |
Countries
Belgium
Outcome results
None listed