Proliferative Vitreoretinopathy, Proliferative Vitreoretinopathy in Rhegmatogenous Retinal Detachment, Retinal Detachment Rhegmatogenous
Conditions
Keywords
Cytokines, Imaging
Brief summary
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.
Detailed description
This is an observational cohort study of 50 participants with uncomplicated rhegmatogenous retinal detachment. Participants will have their vitreous fluid collected at the time of surgery for cross-sectional analysis of cytokine milieu and a series of pre-operative and post-operative non-invasive imaging over 3 months. Unfortunately, 15-20% of the patients with primary retinal detachment will have recurrent retinal detachments following surgery secondary to an anomalous scarring process called proliferative vitreoretinopathy (PVR). Therefore, aims of this study are to: 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. Above will guide future treatments for PVR and further identify high risk populations not just from a clinical perspective but with the utilisation of their imaging and molecular biomarkers.
Interventions
None listed
Sponsors
University College, London
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Adults ≥18 years * Uncomplicated primary rhegmatogenous retinal detachment * PVD present * No PVR-A/B/C * Phakic or pseudophakic.
Exclusion criteria
* Patients \<18 years * Patients lacking capacity * Previous vitrectomy * Previous cryopexy * Aphakia * No fundal view * Diabetic retinopathy of any severity * Retinal detachment secondary to infective causes e.g. acute retinal necrosis, toxoplasmosis scars * Retinal detachment secondary to congenital defects e.g. optic disc pit/coloboma * Exudative retinal detachment * Tractional retinal detachment * Ongoing involvement in another ocular trial.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Characterise the cytokine milieu in an uncomplicated RRD eye. | 3 months | Study cytokine profile using a multiplex assay that includes all relevant cytokines. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Develop a risk model for development of PVR after retinal detachment surgery using imaging and molecular biomarkers. | 3 months | 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. | 3 months | Using ultra-widefield retinal imaging and optical coherence tomography in human retinal detachment models. |
Countries
United Kingdom
Outcome results
None listed