About this trial
Disease or general study area: Uncomplicated rhegmatogenous retinal detachment (RRD) and risk of proliferative vittroretinopathy (PVR)
Purpose and nature of the study:
1. Characterise the cytokine profile of vitreous fluid in uncomplicated RRD. 2. Develop a risk model to predict development of PVR after retinal detachment surgery using imaging and molecular biomarkers. 3. To develop deep learning/artificial intelligence (AI) models for PVR detection in retinal detachment.
Inclusion criteria:
50 adult ( ≥18 years) patients with uncomplicated rhegmatogenous retinal detachments without PVR.
What participating will involve:
Pre- and post-operative assessments and intervention will follow standard of care for patients with rhegmatogenous retinal detachments.
Additional intervention will include non-invasive imaging of anterior chamber flare, vitreous, wide-field retina, macula optical coherence tomography (OCT) and macula OCT-angiography (OCT-A) as well as, seeking participant's consent on collecting their vitreous fluid at time of their surgery for cytokine analysis.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Adults ≥18 years
Uncomplicated primary rhegmatogenous retinal detachment
PVD present
No PVR-A/B/C
Disqualifiers
Patients <18 years
Patients lacking capacity
Previous vitrectomy
Previous cryopexy
Trial population
* Adults ≥18 years * Uncomplicated primary rhegmatogenous retinal detachment * Moorfields Eye Hospital
Trial design
Cohort
Prospective
Treatments tested in this trial
Not listed
Trial groups
Trial outcomes
Primary outcomes
Characterise the cytokine milieu in an uncomplicated RRD eye.
Study cytokine profile using a multiplex assay that includes all relevant cytokines.
Secondary outcomes
Develop a risk model for development of PVR after retinal detachment surgery using imaging and molecular biomarkers.
Study demographics, existing and novel risk factors from imaging aqueous, vitreous, retinal vasculature and retinal structure, and cytokine molecular biology. Use a risk model for risk-stratification of patients who develop PVR re detachment.
To develop deep learning AI models for PVR detection in retinal detachment.
Using ultra-widefield retinal imaging and optical coherence tomography in human retinal detachment models.
Sponsors and contacts
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This trial is not recruiting at the moment. You can still explore other options: