Study of Imaging and Molecular Biomarkers in Uncomplicated Rhegmatogenous Retinal Detachment
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 50
- 试验地点
- 1
- 主要终点
- Characterise the cytokine milieu in an uncomplicated RRD eye.
研究概览
简要总结
Disease or general study area: Uncomplicated rhegmatogenous retinal detachment (RRD) and risk of proliferative vittroretinopathy (PVR)
Purpose and nature of the study:
- Characterise the cytokine profile of vitreous fluid in uncomplicated RRD.
- Develop a risk model to predict development of PVR after retinal detachment surgery using imaging and molecular biomarkers.
- 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.
详细描述
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:
- Characterise the cytokine profile of vitreous fluid in uncomplicated RRD.
- Develop a risk model to predict development of PVR after retinal detachment surgery using imaging and molecular biomarkers.
- 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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Adults ≥18 years
- •Uncomplicated primary rhegmatogenous retinal detachment
- •PVD present
- •No PVR-A/B/C
- •Phakic or pseudophakic.
排除标准
- •Patients <18 years
- •Patients lacking capacity
- •Previous vitrectomy
- •Previous cryopexy
- •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.
结局指标
主要结局
Characterise the cytokine milieu in an uncomplicated RRD eye.
时间窗: 3 months
Study cytokine profile using a multiplex assay that includes all relevant cytokines.
次要结局
- Develop a risk model for development of PVR after retinal detachment surgery using imaging and molecular biomarkers.(3 months)
- To develop deep learning AI models for PVR detection in retinal detachment.(3 months)