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NCT Number: NCT05509010

AI Driven National Platform for CT cOronary Angiography for clinicaL and industriaL applicatiOns Registry

The overall aim is to build an AI driven national Platform for CT cOronary angiography for clinicaL and industriaL applicatiOns (APOLLO) for automated anonymization, reporting, Agatston scoring and plaque quantification in CAD. It is a "one-stop" platform spanning diagnosis to clinical management and prognosis, and aid in predicting pharmacotherapy response.

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Key information

Conditions

CAD Arterial Occlusive Diseases Arteriosclerosis Cardiovascular Diseases Coronary Artery Disease Coronary Disease Coronary Stenosis Heart Diseases Myocardial Ischemia Pathological Conditions, Anatomical Pathological Conditions, Signs and Symptoms Plaque, Amyloid Plaques; Epicardial Vascular Diseases

Age range

21 year–80 year

Sex eligibility

All sexes

Study type

Observational

Primary location

National Heart Centre Singapore, Singapore

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About this study

Coronary artery disease (CAD), a blockage of the blood vessels, affects 6% of the general population and up to 20% of those over 65 years of age. CAD is a leading cause of cardiac mortality in Singapore and worldwide, with 19% of deaths in Singapore due to CAD (MOH website).

Numbers of CAD cases are increasing due to ageing and the higher prevalence of contributary diseases such as diabetes. Computed Tomography Coronary Angiography (CTCA) is the first-line investigation for CAD as indicated by the National Institute for Clinical Excellence (NICE) guidelines. Recent Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) and Scottish Computed Tomography of the Heart (SCOT-HEART) trials support CTCA as the dominant means for evaluating coronary anatomy and physiology as it increases diagnostic certainty, improves efficiency of triage to invasive catheterization and reduces radiation exposure when compared to functional stress testing.

Currently, CAD report generation requires 3-6 hours of a CT specialist's time to annotate scans, with inter-observer variability of 20%. In addition, there is no effective singular toolkit to analyse Agatston scores (a measure of calcified CAD), severity of stenosis, and plaque characterisation.

These problems have severely constrained the effectiveness of CTCA as a diagnostic and research tool. The investigators plan to build upon Singapore's competitive advantages in artificial intelligence (AI) to provide a solution to these gaps.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Age ≥21 years old
  • Signed informed consent
  • Clinically indicated for evaluation by CTCA

Exclusion criteria

  • Individuals unable to provide informed consent
  • Known complex congenital heart disease
  • Planned invasive angiography for reasons other than CAD
  • Non-cardiac illness with life expectancy < 2 years
  • Pregnancy
  • Concomitant participation in another clinical trial in which subject is subject to investigational drug or device
  • Cardiac event and/or coronary revascularization (percutaneous coronary intervention (PCI) and/or coronary artery bypass grafting (CABG) and/or valvular repair/replacement prior to CTCA
  • Glomerular Filtration Rate ≤ 30mL/min
  • Known allergy to iodinated contrast agent
  • Contraindications to beta blockers or nitroglycerin or adenosine

Treatment and study plan

CTCA

Procedure

CTCA is performed as part of routine care procedure.

Primary outcomes

  1. AI precision toolkits: AI stenosis reporting

    Time frame: baseline

    Stenosis reporting: Severity of stenosis and accurate anatomical localization of stenosis.

    The significance of a stenosis is determined by visual estimation of the maximal grade of luminal narrowing caused by the plaque. As recommended in SCCT guideline (Leipsic et al., 2014) , coronary stenosis can be graded as minimal, mild, moderate, severe and total occluded separately. Following the guideline, a stenosis will be classified as obstructive and non-obstructive. The location of the stenosis uses the SCCT model (Leipsic et al., 2014)

  2. AI precision toolkits: Agatston scoring

    Time frame: baseline

    Agatston scoring: Agatston scoring of calcified plaque. As recommended in SCCT clinical practical guidelines (Leipsic et al., 2014), Agatston scoring programs generally identify pixels that exceed 130 HU as a level corresponding to calcium on a non-contrast study (Agatston et al., 1990) . The reader needs to identify each lesion discrete calcific focus) in each vessel distribution. The summed score for each vessel is generated by the scoring program based on an area-density (Agatston score) (Agatston et al., 1990) measurement of each calcified focus. The total coronary Agatston score is the sum of all calcified lesions in all coronary beds.

  3. AI precision toolkits: Plaque

    Time frame: baseline

    Plaque analysis: Plaque volume, burden, type and anatomical locations. Coronary segmentation and plaque analysis is performed for segments with diameter ≥1.5 mm. Location of plaque uses the SCCT model (Leipsic et al., 2014). For each plaque, the reader marks its start-and end-points, quantifies plaque area,volume and plaque burden, and specifies its type (non-calcified, calcified, or mixed) (Achenbach et al., 2004) . Additionally, non-calcified plaque can be further divided into low attenuation plaque (LAP). A HU <30 will signify LAP and >30 will signify non-LAP.

  4. AI precision toolkits: EAT analysis

    Time frame: baseline

    EAT analysis: Total volume and anatomical locations. EAT and pericardial adipose tissue (PAT) are metabolically active fat surrounding the coronary artery and the heart, being associated with increased risk of cardiovascular disease (Villasante et al, 2019) . EAT can be quantified on non-contrast CT scans. The annotations on the CT scans are obtained by manually drawing the pericardium first to define the region. EAT is identified using the adipose tissue attenuation references between -190 and -30 HU (Oikonomou et al., 2018) . Due to the CT scan noise and changing of attenuation, the HU value of fat can vary, so the final EAT region is verified by an experienced radiologist or cardiologist.

Secondary outcomes

  1. AI outcome analysis

    Time frame: one to five years from baseline

    • Mortality (All-cause and/or cardiovascular)

  2. AI outcome analysis

    Time frame: one to five years from baseline

    Major-adverse cardiovascular events (myocardial infarction, stroke, heart failure, revascularisation, arrhythmias, etc)

  3. AI outcome analysis

    Time frame: one to five years from baseline

    Re-hospitalisation

Study contacts

Contact information is provided by the study sponsor or research team.

Associate Professor Zhong Liang

CONTACT

[email protected]

+65 6704 2237

Dr Lohendran Baskaran

CONTACT

[email protected]

Sponsors and collaborators

Lead sponsor

National Heart Centre Singapore

Other

Collaborators

  • Agency for Science, Technology and Research

Registry information

Official study title

AI Driven National Platform for CT cOronary Angiography for clinicaL and industriaL applicatiOns Registry (APOLLO)

Acronym: APOLLO

Important dates

Study start
2021
Primary completion
2027
Study completion
2027
First posted
Aug 19, 2022
Registry last updated
Aug 19, 2022

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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