Predicting Manifest Astigmatism in Keratoconus Patients.

NCT06798779 | Active Not Recruiting

• 1 other identifier: 2304410
• Study Type: observational
• Target: 800
• Locations: 1 country
• Sites: 1

Brief Summary

The cornea of the human eye has several functions. It is transparent to allow light into the eye and its shape focuses the incoming light onto the retina. The cornea is responsible for two-thirds of this focusing, while the human lens accounts for the remaining third. Keratoconus is a condition with onset in the second to third decades of life, where the cornea warps into an irregular shape. This irregularity reduces vision due to blurring of the image focused on the retina. Only partial improvement is achievable with traditional glasses because the corneal shape becomes irregular. The glasses prescriptions of patients with keratoconus often differ significantly from the measurements obtained from the cornea in a clinical setting. The predictability of the magnitude and variability of this disparity is poorly understood. As a result, determining the optimal glasses prescription for achieving the best vision correction often involves a time-consuming trial-and-error approach. Improved predictability could reduce the time required to identify the optimal glasses prescription, thereby increasing productivity. For surgical patients, better predictability would enable surgeons to select lenses that provide superior vision outcomes after treatment. In the optometry clinic at the West of England Eye Unit, a database of over 800 patients with glasses prescriptions and corresponding corneal scans has been compiled by the investigators. This is a sufficient dataset to train and assess the prediction accuracy of machine learning models (AI) of glasses measurements using corneal scan parameters as predictor variables.

Conditions

Keratoconus; Keratoconus, Artificial Intelligence, Support Vector Machine

Keywords

Keratoconus; Machine learning; Astigmatism; AI; Corneal topography; Regression; Glasses; Cylinder; Clinder axis; Sphere; Prediction

Outcome Measures

Primary Outcomes (1)

• The root mean squared error (RMSE) of the machine learning model's predictions for the patient's manifest astigmatism.

  The primary outcome measure will be derived from the machine learning regression analysis, with demographic and keratometry values serving as predictor variables (X) and manifest astigmatism as the outcome variable (Y). For each patient, the earliest Pentacam scan recorded during the study period, for which a manifest astigmatism measurement exists on the same day, will be used.

  Baseline

Secondary Outcomes (1)

• Shapley values for each predictor variable in the best-performing machine learning model.

  Baseline

Interventions

Glasses DEVICE

The patients will have had their vision corrected with glasses that may contain both spherical and cylindrical corrections. The glasses prescription that offers the best subjective correction of vision are being recorded.

Corneal topography DIAGNOSTIC_TEST

This is a scan of the cornea (front of the eye). This gives us information on how the cornea is refracting light for the patient.

Eligibility Criteria

• Age: 16 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

\- Keratoconus patients at the West of England Eye Unit in Exeter, United Kingdom

You may qualify if:

• All patients with a diagnosis of keratoconus who have had topography performed at the West of England Eye Unit with contemporaneous manifest refraction.

You may not qualify if:

• Poor quality topography scans.
• History of ocular surgery.
• Age \<16 years.

Sponsors & Collaborators

• Royal Devon and Exeter NHS Foundation Trust: lead

Study Sites (1)

West of England Eye Unit

Exeter, Devon, EX2 5DW, United Kingdom

Location

Related Publications (1)

• Elkadim M, Nasef MH, Alagorie AR, Allam WA. Corneal Topographic versus Manifest Refractive Astigmatism in Patients with Keratoconus: A Retrospective Cross-Sectional Study. Clin Ophthalmol. 2022 Jun 20;16:2033-2039. doi: 10.2147/OPTH.S361338. eCollection 2022.

  PMID: 35757018 BACKGROUND

MeSH Terms

Conditions

Keratoconus; Astigmatism

Interventions

Eyeglasses; Corneal Topography

Condition Hierarchy (Ancestors)

Corneal Diseases; Eye Diseases; Refractive Errors

Intervention Hierarchy (Ancestors)

Lenses; Optical Devices; Equipment and Supplies; Diagnostic Techniques, Ophthalmological; Diagnostic Techniques and Procedures; Diagnosis

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: January 20, 2025
• First Posted: January 29, 2025
• Study Start: June 1, 2024
• Primary Completion: May 31, 2025
• Study Completion: May 31, 2025
• Last Updated: January 29, 2025

Record last verified: 2025-01

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)