Clinical trial NCT05626517: JARVISDHL Screening Tools
Official title: JARVISDHL Screening Tools
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Coronary Artery Disease
Lead sponsor: National Heart Centre Singapore
Sponsor class: OTHER
Collaborators: name: Singapore Eye Research Institute
First posted: 2022-11-23
Enrollment: 2000
Sex: ALL
Minimum age (years): 21
Maximum age (years): 99
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Participants that have recently undergone an investigation for heart in NHCS (such as computed tomographic coronary angiography \[CTCA\] or non-contrast computed tomography coronary artery calcium \[CAC\] imaging) or participants from SERI, who are diagnosed with Diabetes for more than or equal to 10 years

Brief summary: The main purpose of this study is to pioneer an easy risk stratification tools, which is developed using novel artificial intelligence (AI) algorithms, that will be able to detect common and fatal heart diseases easily simply through a picture of the back of the eye, the retina. The retinal images will be analysed using a computer application with the risk stratification tool to predict health outcome of individual. The study also aims to correlate between clinical characteristics, lifestyle (eg. exercise, sleep, erectile dysfunction) and diet to retinal and coronary vasculature and clinical outcomes.

Detailed description: Cardiovascular disease (CVD) is ranked 1st for mortality rate globally. In 2016, approximately 17.9 million people died from CVDs, representing 31% of all global deaths.

Retinal vasculature has been characterised as the 'window' to the body's circulatory system and been correlated with several diseases that perturbate systemic micro- and macro-vasculature such as hypertension, stroke and chronic kidney disease.

As microvascular changes often precede macrovascular changes, retinal imaging has the capability to be an easily available, non-invasive biomarker to screen for multiple vascular pathologies. A deep learning system (DLS) is a novel,state-of-art artificial intelligence (AI) technology that has achieved robust diagnostic performance for medical imaging analysis. The integration of deep learning (DL) into retinal image evaluation has accelerated its potential further. Its examination of fundus photographs has demonstrated abilities to detect several signs that are undetected by the human eye. With recent correlations being established between CAD and retinal vasculature aberrancies such as reduced calibres and fractal dimensions, there is a clear niche for AI to predict cardiac diseases with the use of retinal fundus photographs.

Eligibility criteria: Inclusion Criteria for NHCS:

1. 21 years of age or older
2. Sufficient language skills in English or Chinese
3. Provided informed consent
4. Completed CTCA or CT CAC in the past 6 months.

Inclusion Criteria for SERI:

1. 21 years of age or older
2. Sufficient language skills in English or Chinese
3. Provided informed consent
4. Patient diagnosed with Diabetes for more than or equal to 10 years.

Exclusion Criteria for NHCS:

1. Patients whose informed consent was not obtained.
2. Age \<21 years old
3. Patients with cardiac event (acute myocardial infarction, Stroke, heart failure, angina requiring hospitalization) and/or coronary revascularization (percutaneous coronary intervention (PCI) and/or coronary artery bypass grafting (CABG) prior to CTCA

Exclusion Criteria in SERI:-

1. Patient whose informed consent was not obtained.
2. Age \<21 years old
3. Patients with cardiac event (acute myocardial infarction, Stroke, heart failure, angina requiring hospitalization) and/or coronary revascularization (percutaneous coronary intervention (PCI) and/or coronary artery bypass grafting (CABG) prior to CTCA

Primary outcomes:
measure: AI model and retinal imaging
timeFrame: Through study completion, an average of 2 years
Secondary outcomes:
measure: cross sectional performance of Cardiovascular risk stratification tool
timeFrame: Through study completion, an average of 2 years
measure: longitudinal major cardiovascular event rate in subjects identified as high risk
timeFrame: Through study completion, an average of 2 years
measure: major cardiovascular event rate in subjects identified as high risk
timeFrame: Through study completion, an average of 2 years
measure: vision threatening retinopathy(VTDR) rate and rate of vision loss in subjects identified as high risk for VTDR
timeFrame: Through study completion, an average of 2 years
Other outcomes:
Locations:
city: Singapore
country: Singapore
facility: National Heart Centre Singapore
Study officials:
affiliation: Singapore National Eye Centre
name: Gavin Tan Siew Wei
role: PRINCIPAL_INVESTIGATOR
affiliation: National Heart Centre Singapore
name: Weiting Huang
role: PRINCIPAL_INVESTIGATOR
References: