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

NCT05509010 | Recruiting DMC

• 1 other identifier: APOLLO
• Study Type: observational
• Target: 8,000
• Locations: 1 country
• Sites: 3

Brief Summary

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.

Conditions

CAD; Coronary Artery Disease; Coronary Stenosis; Plaques; Epicardial

Keywords

CTCA; CT Coronary Angiography

Outcome Measures

Primary Outcomes (4)

• AI precision toolkits: AI stenosis reporting

  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)

  baseline

• AI precision toolkits: Agatston scoring

  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.

  baseline

• AI precision toolkits: Plaque

  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.

  baseline

• AI precision toolkits: EAT analysis

  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.

  baseline

Secondary Outcomes (3)

• AI outcome analysis

  one to five years from baseline

• AI outcome analysis

  one to five years from baseline

• AI outcome analysis

  one to five years from baseline

Study Arms (2)

Retrospective

4000 patients who were clinically evaluated by CTCA from 1 Jan 2007 to 31 Oct 2017.

Procedure: CTCA

Prospective

4000 patients who are clinically evaluated by CTCA.

Procedure: CTCA

Interventions

CTCA PROCEDURE

CTCA is performed as part of routine care procedure.

Prospective; Retrospective

Eligibility Criteria

• Age: 21 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients who are clinically evaluated by CTCA

You may qualify if:

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

You may not qualify if:

• 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

Sponsors & Collaborators

• National Heart Centre Singapore: lead
• Agency for Science, Technology and Research: collaborator

Study Sites (3)

National Heart Centre Singapore

Singapore, 169609, Singapore

RECRUITING

National University Hospital

Singapore, Singapore

RECRUITING

Tan Tock Seng Hospital

Singapore, Singapore

RECRUITING

Related Publications (3)

• Leng S, Cheng N, Tan E, Baskaran L, Teo L, Yew MS, Ngiam KY, Huang W, Chai P, Ong CC, Sia CH, Singh M, Loong YT, Raffiee NAS, Wang X, Allen J, Tan SY, Chan M, Lee HK, Zhong L. Deep learning-based quantification of epicardial adipose tissue volume from non-contrast computed tomography images: a multi-centre study. Eur Heart J Digit Health. 2025 Oct 13;6(6):1223-1233. doi: 10.1093/ehjdh/ztaf116. eCollection 2025 Nov.

  PMID: 41267847 DERIVED

• Baskaran L, Leng S, Dutta U, Teo L, Yew MS, Sia CH, Chew NW, Huang W, Lee HK, Vaughan R, Ngiam KY, Lu Z, Wang X, Tan EWP, Cheng NZY, Tan SY, Chan MY, Zhong L; APOLLO investigators. Cohort profile: AI-driven national Platform for CCTA for clinicaL and industriaL applicatiOns (APOLLO). BMJ Open. 2024 Dec 2;14(12):e089047. doi: 10.1136/bmjopen-2024-089047.

  PMID: 39622571 DERIVED

• Wang X, Leng S, Lu Z, Huang S, Lee BH, Baskaran L, Yew MS, Teo L, Chan MY, Ngiam KY, Lee HK, Zhong L, Huang W. Context-aware deep network for coronary artery stenosis classification in coronary CT angiography. Annu Int Conf IEEE Eng Med Biol Soc. 2023 Jul;2023:1-4. doi: 10.1109/EMBC40787.2023.10340650.

  PMID: 38083399 DERIVED

MeSH Terms

Conditions

Coronary Artery Disease; Coronary Stenosis; Plaque, Amyloid

Condition Hierarchy (Ancestors)

Coronary Disease; Myocardial Ischemia; Heart Diseases; Cardiovascular Diseases; Arteriosclerosis; Arterial Occlusive Diseases; Vascular Diseases; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms

Study Officials

• Liang Zhong

  National Heart Centre Singapore

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Associate Professor Zhong Liang

CONTACT

+65 6704 2237; zhong.liang@nhcs.com.sg

Dr Lohendran Baskaran

CONTACT

lohendran.baskaran@singhealth.com.sg

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 19, 2022
• First Posted: August 19, 2022
• Study Start: October 1, 2021
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): December 1, 2027
• Last Updated: August 19, 2022

Record last verified: 2022-08

Data Sharing

• IPD Sharing: Will not share

Locations

SG (3)