Data Use Application 013
Lead applicant organisation name
Name of the legal entity that signs the contract to access the data.
University College London. Lead applicant: Wing Shing Low, Optometrist, Research student MSc.
Project title
The title of the project/research study request that the applicant is investigating through the use of health data.
Comparing Kalman filter and linear regression for prediction of visual field trajectories at Moorfields Eye Hospital
Lay summary
A concise and clear description of the project. This should outline the problem, objective and the expected outcomes in language that is understandable to the general public.
Glaucoma is a world-leading cause of irreversible blindness, causing gradual damage to the optic nerve and
constriction of visual fields (tunnel vision). The only treatment is prevention through lowering intraocular pressure, and monitoring visual field progression. However the speed of progression varies greatly between individuals and after treatment, making it difficult to predict trajectories. A Kalman filter – a machine learning algorithm – is designed to handle such changes, and has been developed based on data from clinical trials. This was shown to give better predictions than current methods, but validation on real-world data is needed before deployment can be considered.
To address the gap, this project aims to evaluate performance of a previously developed Kalman Filter algorithm in prediction of visual field progression in an independent, real-world, out-of-sample dataset of patients in a glaucoma clinic.
Public benefit statement
A description in plain English of the anticipated outcomes, or impact of project on the general public.
More accurate prediction of how glaucoma progresses will allow earlier detection of change, and earlier intervention to prevent vision loss. The number of repeat tests could be reduced for those with stable disease, reducing time and cost burden associated with hospital visits. This will in turn release valuable healthcare resources for those who need it most, leading to better outcomes for all.
Latest approval date
The last date the data access request for this project was approved by a data custodian.
17 April 2025
Dataset(s) name
The name of the dataset(s) being accessed.
Bespoke glaucoma dataset
Access type
Determines whether the data will be accessed with an Trusted Research Environment (TRE) or via data release.
Data provisioned to the research applicant through INSIGHT's Secure Research Environment.
Data sensitivity level
The level of identifiability of the data being accessed, as defined by Understanding Patient Data.
Anonymised