PROPOSED Local Coverage Determination (LCD)

Artificial Intelligence Enabled CT Based Quantitative Coronary Topography (AI-QCT)/Coronary Plaque Analysis (AI-CPA)

DL39863

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Proposed LCD
Proposed LCDs are works in progress that are available on the Medicare Coverage Database site for public review. Proposed LCDs are not necessarily a reflection of the current policies or practices of the contractor.

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Note History

Contractor Information

Proposed LCD Information

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Source LCD ID
N/A
Proposed LCD ID
DL39863
Original ICD-9 LCD ID
Not Applicable
Proposed LCD Title
Artificial Intelligence Enabled CT Based Quantitative Coronary Topography (AI-QCT)/Coronary Plaque Analysis (AI-CPA)
Proposed LCD in Comment Period
Source Proposed LCD
Original Effective Date
N/A
Revision Effective Date
N/A
Revision Ending Date
N/A
Retirement Date
N/A
Notice Period Start Date
N/A
Notice Period End Date
N/A

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Issue

Issue Description

This policy was developed based on an LCD request for coverage for Quantitative Coronary Plaque Analysis (QCPA) using Artificial Intelligence Enabled CT Based Quantitative Coronary Topography (AI-QCT)/Coronary Plaque Analysis (AI-CPA).

Issue - Explanation of Change Between Proposed LCD and Final LCD

CMS National Coverage Policy

CMS Internet-Only Manual, Pub 100-03, Medicare National Coverage Determinations Manual, Chapter 1, Part 4, 220.

The Protecting Access to Medicare Act (PAMA) of 2014, Section 218(b), established a new program to increase the rate of appropriate advanced diagnostic imaging services provided to Medicare beneficiaries.

42 CFR §414.92 codifies the Appropriate use Criteria Program policies.

CMS Publications

Title XVIII of the Social Security Act, §1862 (a)(1)(A) allows coverage and payment for only those services that are reasonable and necessary for the diagnosis or treatment of illness or injury or to improve the functioning of a malformed body member.

Title XVIII of the Social Security Act section 1862 (1) (5) (D). 21st Century Cures Act of 2016 (Public Law 114-255)- The 21st Century Cures Act of 2016 added language to section 1862(l)(5)(D) of the Social Security Act (the Act) directing the Secretary of the Department of Health and Human Services (DHHS) to improve the transparency of the LCD process.

Title XVIII of the Social Security Act, §1862 (a)(1)(D) Items and services related to research and experimentation.

Title XVIII of the Social Security Act, §1862 (a)(7) states Medicare will not cover any services or procedures associated with routine physical checkups.

Title XVIII of the Social Security Act, §1833 (e) prohibits Medicare payment for any claim which lacks the necessary information to process the claim.

42 CFR §410.32 indicates that diagnostic tests may only be ordered by the treating physician (or other treating practitioner acting within the scope of his or her license and Medicare requirements).

CMS Publication 100-3, National Coverage Determination Manual, Chapter 1
220.1 Computerized Tomography

CMS Publication 100-4, Medicare Claims Processing Manual, Chapter 13
20 Payment Conditions for Radiology Services

CMS IOM Publication 100-08, Medicare Program Integrity Manual, Chapter 13, Section 13.5.4 - Reasonable and Necessary Provisions in an LCD.

CMS Publication 100-9, Contractor Beneficiary and Provider Communication Manual, Chapter 5
20 Correct Coding Initiative

Coverage Guidance

Coverage Indications, Limitations, and/or Medical Necessity

Indications of Coverage

AI-QCT/AI-CPA using CCTA* is considered reasonable and medically necessary as a diagnostic study when:

  1. The patient is eligible for CCTA*, AND
  2. The patient presents with acute chest pain and no known CAD1 and is classified as (one or both):
    • Intermediate risk **
    • CAD-RADS 2 and CAD-RADS 3 category on CCTA***, AND

      3. Cardiac evaluation is negative or inconclusive for acute coronary syndrome (ACS)1

*See L33559 for criteria for CCTA. AI-QCT/AI-CPA using CCTA* should be performed in patients with stable coronary symptoms. It should not be performed until after the base study (CCTA) has been completed and interpreted.  Software to perform AI-QCT/AI-CPA must be FDA cleared. 

**Intermediate and high-risk as defined in the 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain1

*** CAD-RADS 2 and CAD-RADS 3 category as defined by CAD-RADS™ 2.0–2022 Coronary Artery Disease Reporting and Data System (CAD-RADS): an Expert Consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR), and the North America Society of Cardiovascular Imaging (NASCI).2,3

Limitations of Coverage

AI-QCT/AI-CPA is not considered reasonable or necessary in the following clinical circumstance:

  1. The test is never covered for screening, i.e., in the absence of signs, symptoms, or disease.
  2. No contraindications to CCTA*
  3. Not in conjunction with invasive coronary catheterization
  4. Not to be used with normal CCTA results (CAD RADS=0 or no plaque disease)
  5. Not to be used with high grade stenosis (>70%) or CAD RADS-4 and RADS-5
  6. Recent MI (30 days or less)
  7. Unstable coronary symptoms
  8. Not to be used for surveillance

Definitions

Artificial Intelligence Enabled CT Based Quantitative Coronary Topography (AI-QCT)/Coronary Plaque Analysis (AI-CPA)- artificial intelligence application to imaging obtained through coronary CT scans to calculate coronary artery dimensions and degree of stenosis per vessel and coronary plaque composition and burden. 4

Calcified Plaque- Higher density plaque, mostly composed of calcium, thought to be associated with lower clinical risk than non-calcified plaque. Traditionally, the overall burden of calcified plaque has been assessed indirectly through a coronary artery calcium score (CACS) or using cut off >350 HU.5

Coronary Artery Disease (CAD)- Narrowing of the coronary arteries usually caused by plaque and atherosclerosis that can lead to ischemia of the heart.1

  • Known CAD includes patient with prior anatomic testing with identified nonobstructive atherosclerotic plaque and obstructive CAD.1

Coronary Artery Disease Reporting and Data System (CAD-RADS)- A standardized method to communicate findings of CCTA.2

Category

Degree of maximal coronary stenosis

Interpretation

CAD-RADS 0

0%

Absence of CAD

CAD-RADS 1

1-24%

Minimal non-obstructive CAD

CAD-RADS 2

25-49%

Mild non-obstructive CAD

CAD-RADS 3

50-69%

Moderate stenosis

CAD-RADS 4

70-99% or left main ≥50% or 3-vessel obstructive (≥70%) disease

Severe stenosis

CAD-RADS 5

100%

Total coronary artery occlusion or sub-total occlusion

CAD-RADS N

Non-diagnostic study

Obstructive CAD cannot be excluded

Coronary Computed Tomography Angiography (CCTA)- a non-invasive test using advanced computed tomography angiography imaging to view the tissues and blood vessels of the heart. This can be used to determine the presence and extend of CAD.

Coronary Plaque Analysis (CPA)- Analysis of coronary plaque composition and burden.

Fibrotic Plaque- a plaque with density of 131-350 HU.

High Risk Plaque (HRP)- High risk plaque findings include napkin-ring sign, low-attenuation plaque, positive remodeling, low CT attenuation and spotty calcification.5,6

Invasive Coronary Angiography (ICA)- Invasive procedure done at the time of cardiac catheterization to look at the arteries of the heart and can determine the presence and extend of CAD.

Low Attenuation Plaque (LAP)-Low density plaque with dark appearance on CCTA and higher lipid content usually defined as attenuation of <30 Hounsfield unit and associated with higher clinical risk. Density is measured in Hounsfield Units (HU) and low attenuation is -50 to 50HU.5,7

Major Adverse Cardiac Events (MACE)- Fatal and non-fatal myocardial infarction. Some studies also include unstable angina requiring hospitalization or revascularization.6

Non-Calcified Plaque (NCP) – A lower density plaque, often earlier in development and associated with higher clinical risk with density of 50-130 HU.5,7 NCP can be further classified into LAP, fibrous and fibrofatty plaques.

Nonobstructive CAD- CAD with <50% stenosis.1

Obstructive CAD- CAD with >50% stenosis1

Quantitative Coronary Plaque Analysis (QCPA)- Imaging technique that provides objective and reproducible measurements of coronary artery dimensions and composition of the atherosclerotic plaques.

Quantitative Coronary Topography (QCT)- CT scan imaging that provides objective and reproducible measurements of the coronary artery dimensions and degree of stenosis per vessel.

Provider Qualifications

The Medicare Program Integrity Manual states services will be considered medically reasonable and necessary only if performed by appropriately trained providers.

Patient safety and quality of care mandate that healthcare professionals who interpret CCTA and QCPA and AI-QCT/AI-CPA are appropriately trained and/or credentialed by a formal residency/fellowship program. Credentialing or privileges are required for procedures performed in inpatient and outpatient settings.4

All aspects of care must be within the provider’s medical licensure and scope of practice. Reimbursement for procedures utilizing imaging techniques may be made to providers who meet training requirements for the procedures in this policy only if their respective state allows such in their practice act and formally licenses or certifies the practitioner to use and interpret these imaging modalities. At a minimum, training must cover and develop an understanding of anatomy and drug pharmacodynamics and kinetics as well as proficiency in diagnosis and management of disease, the technical performance of the procedure, and utilization of the required associated imaging modalities. Supervision, interpretation, and reports shall/must be performed by a physician with the advanced training requirements and or credentialing for CCTA and AI-QCT/AI-CPA. The technical and professional portions must meet the criteria for performance for CCTA (L33559).

Providers must also meet the FDA requirements which includes “The software is not intended to replace the skill and judgment of a qualified medical practitioner and should only be used by people who have been appropriately trained in the software’s functions, capabilities and limitations.”8 Radiology technicians must also meet all training requirements for performance of AI-QCT/AI-CPA.

Notice: Services performed for any given diagnosis must meet all the indications and limitations stated in this LCD, the general requirements for medical necessity as stated in CMS payment policy manuals, all existing CMS national coverage determinations, and all Medicare payment rules.

Summary of Evidence

A Contractor Advisory Meeting “Non-Invasive Technology for Coronary Artery Plaque Analysis” was hosted 5/25/23 by CGS Administrators, Noridian Healthcare Solutions, National Government Services, Palmetto GBA, and WPS Government Health Administrators. The transcript and audio are available on each MACs website.

Background

CCTA has become an effective gate keeper for invasive angiography helping guide referral for obstructive CAD. It has been demonstrated to be superior to exercise electrocardiography and single-photon emission computed tomography (SPECT) for detection of obstructive CAD (>50% stenosis).9,10. Studies have demonstrated excellent prognostic value of a normal CCTA for both short and long term mortality rates.11 The updated 2021 American College of Cardiology and American Heart Association Chest Pain Guideline1 states CCTA has become a first line tool in evaluation of acute and chronic coronary artery disease particularly in symptomatic patients with stable symptoms and intermediate or high pre-test probability of obstructive coronary artery disease, or among intermediate-risk acute chest pain patients.

CCTA can also provide information on plaque burden and adverse coronary artery plaque characteristics which has been demonstrated to be an independent predictor of disease and prognosis.11 The characterization of coronary atherosclerotic plaques can be calculated from CCTA, but the process is time consuming and often with variable results.4,12 At least 5 different software have been developed as an adjunct to CCTA to aid in the visualization, reduce evaluation time, and improve accuracy of this assessment. The gold standard is considered intravascular ultrasound (IVUS) and optical coherence tomography (OCT) which are the tools that are often used to validate the software.5 This is intended to improve clinical diagnosis and management of CAD.13

Applications and Limitations of CCTA

SCOT-HEART trial demonstrated that the addition of CCTA to standard of care significantly improved diagnostic certainty of angina.14,15 In this prospective, open labeled, parallel group, multicentered trial 4146 patients with stable chest pain received standard of care (SOC) plus CCTA (n=2073) or SOC alone (n=2073). Primary end point was death from CAD or nonfatal myocardial infarction at 5 years. They reported that the 5-year death rate was lower in the CCTA group as compared to the SOC group (2.3% [48 patients] vs. 3.9% [81 patients]; hazard ratio, 0.59; 95% confidence interval [CI], 0.41 to 0.84; p = 0.004). Rates of ICA and coronary revascularization were higher in the CCTA group compared to SOC in the first few months of follow-up, with no differences in the overall use of ICA and coronary revascularization reported at 5 years. During follow up, CCTA assigned patients were more likely to have initiated preventive therapies when compared to the SOC alone group (19.4% [402 patients] vs. 14.7% [305 patients]; odds ratio, 1.40; 95% confidence interval [CI], 1.19 to 1.65) and antianginal therapies (13.2% [273 patients] vs. 10.7% [221 patients]; odds ratio, 1.27; 95% CI, 1.05 to 1.54). The authors conclude the use of CCTA resulted in more correct diagnoses of coronary heart disease than standard care alone, increase use of appropriate therapies, and the resultant change in management lead to fewer clinical events in the CCTA group than in the SOC group.

The Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) trial compared CCTA to functional testing for stable chest pain. This RCT enrolled 10,003 patients with stable chest pain who underwent CCTA or functional testing (exercise electrocardiography, nuclear stress testing, or stress echocardiography) and followed for 25 months. They conclude that initial evaluation with CCTA did not improve clinical outcomes compared to functional testing.16 Sub-analysis of this population to explore prognostic value of these test found the prevalence of normal test results and incidence rate of events in these patients were lower in the CCTA group (n=4500) in comparison to functional testing (n=4602) (33.4% versus 78.0%, and 0.9% versus 2.1%, respectively; both p<0.001). They reported that CCTA offered higher discriminatory ability in predicting events over functional test.17 A observational cohort within this same cohort explored the role of high-risk plaque in predicting MACE in this population. They found high risk plaque were present in 676 (15%) and carried a 70% increased risk of MACE independent of cardiovascular risk factors (6.4% vs. 2.4%; hazard ratio, 2.73; 95%CI, 1.89-3.93).18 They also reported there was no significant difference in MACE in patients with significant stenosis and high risk plaque as opposed to those with significant stenosis without high risk plaques, and high risk plaques are a stronger predictor of MACE in women and younger patients. This data is limited by the low absolute MACE rate within the population and low positive predictive value of high-risk plaques. Compared to the SCOT-HEART trial the shorter duration of follow-up (2 vs. 5 years) and low event rate in the PROMISE trial likely contributed to the variability within these results. This study is pertinent to it explores the role of plaques in predictors of MACE.

The role of plaque burden was further explored in the Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging registry (PARADIGM study).19 This was a prospective, multinational study that enrolled 2,252 patients at 13 sites without history of coronary artery disease who underwent serial CCTA at an interscan interval of >2 years. Plaques were analyzed for the percent diameter stenosis, percent atheroma volume (PAV), plaque composition, and presence of high-risk plaque (HRP), defined by the presence of 2 or more of the following: low-attenuation plaque, positive arterial remodeling, or spotty calcifications. The population was further divided into statin-naive (n=474) and statin-taking (n=781) patients. They found that the group on statins experience a slower rate of overall PAV progression (1.76 ± 2.40% per year vs. 2.04 ± 2.37% per year, respectively; p=0.002), and annual incidence of new HRP features were lower at 0.9% per year vs. 1.6% per year, respectively; all p < 0.001). The reported more rapid progression of calcified PAV (1.27 ± 1.54% per year vs. 0.98 ± 1.27% per year, respectively; p < 0.001) with slower progression of noncalcified PAV in statin-taking patients (0.49 ± 2.39% per year vs. 1.06 ± 2.42%per year and 0.9% per year vs. 1.6% per year, respectively; all p < 0.001). The rates of progression to >50% diameter stenosis was not different (1.0% vs. 1.4%, respectively; p > 0.05) concluding that statins did not affect the progression of percentage of stenosis severity of coronary artery lesions but induced phenotypic plaque transformation. They conclude statins were associated with slower progression of overall coronary atherosclerosis volume, with increased plaque calcification and reduction of HRP features. This is pertinent as this study demonstrates a potential role of plaque analysis in clinical management of coronary lesions.

The Effect of Alirocumab on Atherosclerotic Plaque Volume, Architecture and Composition (ARCHITECT) study was a phase IV, open-label, multicenter, single-arm clinical trial designed to access plaque burden in patients with familial hypercholesterolemia.20 The investigators explored the change in plaque burden in patients (n=104) being treated with alirocumab in addition to statins. They reported that the global coronary plaque burden changed from 34.6% (32.5%–36.8%) at entry to 30.4% (27.4%–33.4%) at follow-up, which represents a –4.6% (–7.7% to –1.9%) statistically significant regression p<0.001). Plaque burden was measured with QAngio CT (Research Edition V2.1.16.1; Medis Specials). Limitations include mean age of patients below Medicare population (mean 53.3), lack of control arm, short tern follow-up and uncertainty in how these findings impact clinical outcomes. This study is pertinent that it demonstrates a role for measuring plaque burden for atherosclerotic CAD and that intervention can impact plaque burden and composition.

Artificial Intelligence Enabled, CT Based, Quantitative Coronary Plaque Analysis (AI-CPA)

Studies have identified higher plaque burden as a risk factor for MACE. Plaque burden is challenging to calculate as it is very time intensive with high variability even among expert readers. Software program to aid in the calculation of coronary artery stenosis, plaque analysis and fractional flow reserve (FFR) have been developed.

Clinical Validity

The CT Evaluation by Artificial Intelligence for Atherosclerosis, Stenosis and Vascular Morphology (CLARIFY) study was a prospective, blinded diagnostic cohort study designed for external validation. It was conducted at multiple sites and included 232 patients who underwent CCTA for acute and stable chest pain.4 The CCTA was followed by evaluation with FDA-cleared software service that performs AI-driven coronary artery segmentation and labeling, lumen and vessel wall determination, plaque quantification (Cleerly, Inc.). The mean age of the subjects was 60 ± 12 years with 37% females and most with co-morbidities. After the CCTA was performed the results were downloaded and AI-aided CCTA analyses were conducted in a blinded manner using the Cleerly software platform. The CCTA was also analyzed by 3 blinded Level 3 readers and consensus of the individual reads was considered the “ground truth” for the study.

AI performance was excellent for accuracy, sensitivity, specificity, positive predictive value, and negative predictive value as follows: >70% stenosis: 99.7%, 90.9%, 99.8%, 93.3%, 99.9%, respectively; >50% stenosis: 94.8%, 80.0%, 97.0, 80.0%, 97.0%, respectively. The CAD-RADs categorization comparing expert readers to the AI results reported 78% agreement (182/232) and 98.3% (228/232) agreed within one category. The most common disagreement was between CAD-RADs 0 and AI CAD-RADS 1 (n= 29 12.5% per patient, n=161 17.4% per vessel). There was >99% category agreement between expert readers and AI-read studies for CAD-RADS 0-3 and CAD-RADS 4-5 using a threshold of >70% stenosis. Bland-Altman plots depict agreement between expert reader and AI reporting high risk plaque features were found in 49/232 (21.1%) patients using AI and in 31/232 (13.4%) by consensus expert readers which had an 82% agreement. The software analysis time was 9.7 ± 3.2 minutes and time to AI-QCT/AI-CPA analysis and report generation 23.7 ± 6.4 minutes.

The authors conclude that the AI determined reading had highest correlation to the consensus of the expert readers rather than an individual reader suggesting improved accuracy over an individual reader alone. They found the AI approach identified a wide range of atherosclerosis plaque volume and plaque composition in all coronary arteries and their branches, which may offer a benefit over individual readers where this assessment is dependent on the image phase and other factors which limit consistency especially with less experienced readers. Limitations of this study include lack of control group, ground truth is consensus of 3 expert readers without validation to invasive approaches, lack of a guideline basis reference standard for CCTA atherosclerosis quantification, and sample size too small to validate in high-risk population as only 15% of the studied population had anatomically obstructive stenosis.

Myocardial perfusion imaging (MPI) is the most common non-invasive stress imaging modality applied during stress testing in the US. However, MPI has been reported to have a limited performance in ischemia detection.21,22 A retrospective, multicenter, diagnostic cohort study enrolled 301 subjects and was designed to compare diagnostic performance of myocardial perfusion imaging (MPI) and CCTA with AI-QCT using Cleerly, Inc software.23 The primary endpoint being evaluated was detection of obstructive CAD. The study was a retrospective post hoc analysis from the prospective 23-center Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia (CREDENCE) trial. The mean study age was 64.4 ± 10.3 with stable symptoms of myocardial ischemia referred for nonemergent invasive angiography.

For patients with no ischemia on MPI (n=102) AI-QCT/AI-CPA identified obstruction ≥50% in 54% and included 20% with severe stenosis (≥ 70%). For the 199 patients with ischemia on MIP, AI-QCT identified nonobstructive stenosis (<50%) in 23%. They reported that AI-QCT had a significantly higher AUC than MPI for predicting ≥ 50% stenosis by CCTA (0.88 vs. 066), ≥ 70% (0.92 vs 0.81) and FFR <0.8- (0.90 vs 0.71) (p<0.001). An AI-QCT result of ≥ 50% stenosis and ischemia on stress MPI had sensitivity of 95% versus 74% and specificity of 63% versus 43% for detecting ≥ 50% stenosis by CCTA measurement. The authors conclude that CCTA with AI-QCT/AI-CPA has a higher diagnostic performance than MPI for detecting obstructive CAD. They suggest that “a scenario of performing coronary CCTA with AI-QCT in all patients and those showing ≥ 70% stenosis undergoing invasive angiography would reduce invasive angiography utilization by 39%; a scenario of performing MPI in all patients and those showing ischemia undergoing coronary CCTA with AI-QCT and those with ≥ 70% stenosis on AI-QCT undergoing invasive angiography would reduce invasive angiography utilization by 49%.” The study is limited by retrospective design which is not a sufficient study design to establish non-inferiority, lack of long-term outcome data, lack of generalizability (mostly men and younger than Medicare population therefore not necessarily applicable to real world practice.

A challenge with CCTA is the risk of overestimation of CAD and studies have demonstrated that less experienced readers have a higher rate of overestimation as compared to expert readers and risk an increase in unnecessary invasive procedures.24 Investigations to reduce this are underway and include the addition of AI-enabled solutions. In a retrospective report data from the CREDENCE trial 25 612 study participants with stable chest without a history of CAD referred for nonemergent ICA underwent CCTA, invasive coronary angiogram (ICA) and invasive FFR. CCTA images were interpreted on per-lesion and per-segment basis for lumen and vessel volume, diameter stenosis, plaque composition and volume, number of lesions, and the presence of HRP features using semiautomated plaque analysis software (QAngioCT Research Edition, version 3.1.4.1; Medis Medical Imaging) and fractional flow reserve measurements by Heartflow. The authors conclude that quantification of obstructive and nonobstructive plaque was superior to functional imaging and improved predictions of stress induced alterations in perfusion.

A sub study of 303 subjects from the CREDENCE Trial were evaluated with AI-QCT/AI-CPA with software by Cleerly, Inc.26 In this cohort they compared AI-QCT/AI-CPA to core lab–interpreted coronary CTA, core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR). They report the prevalence of stenosis ≥50% was observed in 67.0% (n=202 of 303) of patients and 36.0% (n=308 of 848) of vessels, while presence of stenosis ≥ 70% was observed in 39.0% (n=119 of 303) of patients and 19.0% (n=157 of 848) of vessels. The per-patient sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for ≥50% stenosis was 94%, 68%, 81%, 90%, and 84%, respectively, and for detection of ≥70% stenosis was 94%, 82%, 69%, 97%, and 86%, respectively. Correlations of AI-QCT vs. QCA for % stenosis on a per-territory and per-patient basis were 0.728 and 0.717, respectively (p < 0.0001 for both). Evaluation of predominately calcified (≥50%) vs. predominately non-calcified vessels (<50%) reported significantly lower per vessel specificity in the predominately calcified vessels as compared to predominately non-calcified (86.0% calcified vs 95.3% noncalcified; p < 0.0001) at 50% threshold and even lower for 70% threshold ((92.7% calcified vs 92.9% noncalcified; p < 0.0001).There was discordance of >30% between the AI-QCT determined stenosis as compared to the QCA in 8.1% (74 of 909 vessels) in which 1 or both stenoses was ≥50%. Of the 157 vessels that were ≥70% by AI-QCT, 60.5% (n= 95) had a concordant QCA of ≥70% and 62 were discordant. FFR QCA and AI-QCT had similar accuracy (85.0% and 86.2%; P ¼ 0.217), respectively for predicting an FFR of <0.8. False positive rate with AI-QCT/AI-CPA was 39.4%, with 62/157 vessels reported to have stenosis ≥70% by AI-QCT/AI-CPA but found to have <70% on invasive CCTA. While invasive FFR was more accurate with 66.1% having FFR of <0.8.

A population of 303 patients from the CREDENCE trial who underwent CCTA prior to ICA and FFR were evaluated with AI-QCT/AI-CPA.27 They correlated percent atheroma volume in patients with 50% stenosis on ICA with non-obstructive CAD into single vessel, 2 vessels, and 3 vessels/left main disease groups and further classified by ischemic or non-ischemic. Definition of plaque stage thresholds of 0, 250, 750 mm3 and 0, 5, and 15% PAV resulted in 4 clinically distinct stages in which patients with no, nonobstructive, single VD and multi-vessel disease were optimally distributed. The proposed the following staging criteria based on atherosclerotic plaque burden by QTC related to stenosis severity: Stage 0 (Normal, 0% PAV, 0 mm3 TPV), Stage 1 (Mild, >0–5% PAV or >0–250 mm3 TPV), Stage 2 (Moderate, >5–15% PAV or >250–750 mm3 TPV) and Stage 3 (Severe, >15% PAV or >750 mm3 TPV).

The authors conclude lower specificity, positive predictive value, and accuracy when the AI-based evaluation were compared with QCA vs the previously employed reference standard of consensus of L3 expert readers, primarily because of an increase in false positive diagnoses by the AI-based evaluation.4 They report discriminatory power of the AI-based evaluation appears to be high, with a per-patient AUC of 0.88 for ≥50% stenosis and of 0.92 for ≥70% stenosis threshold, and a per-vessel AUC of 0.90 for ≥50% stenosis and of 0.95 for ≥70% stenosis. While these results were higher than that reported in the Clarify study, they conclude the composite of this data demonstrates evidence-based evaluation is an important adjunct to CCTA results.

Limitations of the study include retrospective design, focused on the outcome measure of stenosis severity, and did not include evaluation for plaque characteristics which is being investigated in a separate study, does not include evaluation of mild lesions, population not generalizable (mostly male and younger than the Medicare population), small sample size in patients with more severe disease.

A longitudinal study followed 1577 patients who underwent coronary CCTA for cardiovascular events over 10.5 years (range 6.0-11.4). For each subject they calculated Morise Score, coronary artery disease severity and coronary total plaque volume (TPV). They reported 3.7% (59/1577) had cardiac death or acute coronary syndrome during the study period. They reported that coronary TPV provided additive prognostic value over clinical risk assessed with the Morise Score and coronary artery disease severity (rise in C-index from 0.744 to 0.769, P= 0.03). The use of Morise Score and TPV was superior with reclassification of 800 (51%) of patients compared to Morise score alone.28 The authors conclude that coronary TPV up to 10 years and can be used to reclassify patient into different risk groups compared with clinical risk alone. The study was limited as no information was available regarding changes in medical therapy after coronary CCTA, although ASA and statin therapy was recommended when signs of CAD were evident.

A sub-analysis of the SCOT-HEART trial evaluated 1,769 participants (43% female) who underwent CCTA and explored sex difference in stenosis, adverse plaque characteristics and quantitative assessments of total, calcified, non-calcified and LAP burden. The authors report that females were more likely to have normal coronary arteries and less likely to have adverse plaque characteristics (p<0.001 for all measurements). While the percentage who went on to have MI was low over 4.7 years, 1.4% in women and 3% in men, the women who experienced MI had the similar findings to the men. Low attenuation plaque burden was a strong predictor of MI in both men and women.29

An international, multicenter, retrospective study included 9 cohorts of patients undergoing CCTA at 11 sites. a novel deep learning convolutional neural network was trained to segment coronary plaque in 921 patients. An external independent test site validated 175 patients and found good or excellent agreement between the deep-learning network and expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0.964) and percent diameter stenosis (ICC 0.879; both p<0·0001). In additional 50 patients that were assessed by intravascular ultrasound were also found to have excellent agreement for deep learning total plaque volume (ICC 0.949) and minimal luminal area (ICC 0.904). The deep learning plaque analysis time was 5.65 seconds (SD 1.87) versus 25.66 minutes (6.79) taken by experts. The subjects were followed for a median of 4.7 years and investigators found a deep learning-based total plaque volume of 238.5 mm3 or higher was associated with an increased risk of myocardial infarction (hazard ratio [HR] 5.36, 95% CI 1.70–16.86; p=0.0042) after adjustment for the presence of deep learning-based obstructive stenosis (HR 2.49, 1.07–5.50; p=0.0089) and the ASSIGN clinical risk score (HR 1.01, 0.99–1.04; p=0.35). The authors concluded that the deep learning model has prognostic value for future myocardial infarction risk.30

CCTA has a Level 1A recommendation for initial evaluation of acute and stable chest pain in patients without known but suspected CAD per the AHA/ACC Guidelines. However despite CCTA’s high sensitivity a moderate specificity suggest a risk of false positives and overestimation of stenosis.31 A sub-analysis from the CREDENCE trial aims to compare AI-QCT, CCTA and FFRCT for discriminating coronary ischemia at the patient and vessel levels.32 In this study AI-QCT/AI-CPA was calculated using Cleerly, Inc. software and in the CREDENCE trial FFR-CT was calculated by Heartflow, Inc software in a blinded fashion. Area under receiver operative characteristics curve (AUC) with 95% confidence intervals was used to compare the modalities.

In comparing the three modalities for discriminating ischemia at a ≥505 stenosis threshold they report the following: AI-QCT with accuracy, sensitivity, specificity, PPV, and NPV of 82%, 95%, 66%, 76%, and 92%, respectively, and FFRCT’s of 69%, 59%, 82%, 79%, and 63%, respectively. Comparatively, CCTA achieved intermediate range outcomes with performance measures, 73%, 75%, 71%, 75% and 71%, respectively. They conclude accuracy was greatest for AI-QCT/AI-CPA, FFR-CT demonstrated high specificity and PPV but weaker in other measures and comparable to the results reported with CCTA.

In comparing for discriminating ischemia at the vessel level they report accuracy, sensitivity, specificity, PPV and NPV for AI-QCT at the vessel level were 84%, 89%, 83%, 65%, and 95%, FFRCT’s of 7%, 60%, 83%, 56% and 85%, and CCTA 79%, 65%, 84%, 59% and 87%, respectively. The area under the receiver operative characteristics curve (AUC) for discriminating patient-level ischemia by AI-QCT, CCTA and FFRCT was 0.90, 0.77 and 0.73, respectively (p<0.001) and 0.93, 0.83 and 0.79, respectively, at the vessel level (p<0.001). The author concludes AI-QCT/AI-CPA achieved equal specificity to FFRCT for discriminating ischemia at the vessel level while maintaining superior sensitivity and overall accuracy compared to both CCTA and FFRCT.

The study is limited by all the limitations that impacted the CREDENCE trial from which the data was obtained and the retrospective study design which is not sufficient to determine superiority or non-inferiority between different these different modalities.

A retrospective report analyzed 79 patients with end stage renal disease referred for CCTA and underwent AI-QCT/AI-CPA with Cleerly, Inc software.33 This study provided disease distribution and plaque analysis specific to the ESRD population. They reported higher low-density non-calcified-plaque, non-calcified-plaque, calcified-plaque, length, and total plaque volume in patients with >50% stenosis and obstructive lesions. They found more calcified-plaque and percent atheroma volume in patients >65 years old.

A staging criterion for plaque volume is proposed by Min et al.27 This is an important step for measuring clinical utility and outcome related to plaque in the future. The authors report that while atherosclerotic plaque characterization by CCTA enables quantification of CAD and has been demonstrated as a strong predictor of future risks of MACE a clinically useful threshold to understand patient disease burden to guide diagnosis and management was lacking. A population of 303 patients from the CREDENCE trial25 who had CCTA and FFR prior to ICA data was analyzed with AI-QCT/AI-CPA with Cleerly, Inc. software. They correlated percent atheroma volume in patients with 50% stenosis on ICA with non-obstructive CAD into single vessel, 2 vessels, and 3 vessels/left main disease groups and further classified by ischemic or non-ischemic. Definition of plaque stage thresholds of 0, 250, 750 mm3 and 0, 5, and 15% PAV resulted in 4 clinically distinct stages in which patients with no, nonobstructive, single VD and multi-vessel disease were optimally distributed. The proposed the following staging criteria based on atherosclerotic plaque burden by QTC related to stenosis severity: Stage 0 (Normal, 0% PAV, 0 mm3 TPV), Stage 1 (Mild, >0–5% PAV or >0–250 mm3 TPV), Stage 2 (Moderate, >5–15% PAV or >250–750 mm3 TPV) and Stage 3 (Severe, >15% PAV or >750 mm3 TPV).

Another post-hoc analysis from the CREDENCE study 25 also investigated the relationship been coronary stenosis and plaque characteristics and age using AI-QCT/AI-CPA with Cleerly, Inc. software. The cohort of patients >65 (n=154) had more plaque volume and calcified plaque than those <65 (n=139).34 On a per lesion level they reported those >65 had more calcified plaque in both obstructive and non-obstructive lesions while the <65 cohort had more plaque volume, non-calcified plaques and low-density non-calcified plaques and lesion length in obstructive lesions. The authors conclude this may aid in approaches to management.

A large observational, retrospective, consecutive, international, multicenter cohort study included 11,808 patients who underwent clinically indicated CCTA. Analysis for total plaque volume and composition was performed with AI-CPA with software from Heartflow. Mean age was 62.7 ± 12.2 years with 45.9% women. The authors report median total plaque volume was 223 mm3 (IQR: 29-614 mm3) and was significantly higher in male participants (360 mm3; IQR: 78-805 mm3) compared with female participants (108 mm3; IQR: 10-388 mm3) (P < 0.0001). These results do not address clinical outcomes of the cohort but do provide nomograms. The authors call for future investigation to validate the use of the nomograms to confirm the potential role of AI-AI-CPA as a risk tool for guiding clinical decision.35

Omori et al.36 conducted the multicenter study to investigate the performance of AI-QCT in the detection of low-density noncalcified plaque (LD-NCP) using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS). Patients were enrolled if they were indicated to undergo CCTA and IVUS, NIRS-IVUS, or optical coherence tomography. A total of 133 atherosclerotic plaques (n=47) were evaluated. The area under the curve of LD-NCP<30HU was 0.97 (95% confidence interval [CI]: 0.93–1.00] with an optimal volume threshold of 2.30 mm3. Using <30 HU and 2.3 mm3, accuracy, sensitivity, and specificity were 94% (95% CI: 88–96%], 93% (95% CI: 76–98%), and 94% (95% CI: 88–98%), respectively. As compared to 42%, 100%, and 27% using <30 HU and >0 mm3 volume of LD-NCP (p < 0.001). A strong correlation was reported between AI-QCT and IVUS measurements which included vessel area (r2 = 0.87), lumen area (r2 = 0.87), plaque burden (r2 = 0.78) and lesion length (r2 = 0.88), respectively. Authors concluded AI-QCT served as an adequate tool to detect significant LD-NCP with maxLCBI4mm ≥ 400 as the reference standard.

Hakim et al.37 conducted a study to compare the accuracy of ESS computation of local ESS metrics by CCTA vs IVUS imaging from a registry of 59 patients who underwent both IVUS and CCTA for suspected CAD. IVUS and CCTA measurement of plaque characteristics correlated when measured and included vessel, lumen, plaque area and minimal luminal area per artery, resulting in 12.7 + 4.3 vs 10.7 + 4.5 mm2, r = 0.63; 6.8 +2.7 vs 5.6 + 2.7 mm2, r = 0.43; 5.9 + 2.9 vs 5.1 +3.2 mm2, r = 0.52; 4.5 + 1.3 vs 4.1 + 1.5 mm2, r = 0.67 respectively. Endothelial shear stress (ESS) metrics of local minimal, maximal, and average ESS were moderately correlated when measured with IVUS and CCTA resulting in 2.0 +1.4 vs 2.5 + 2.6 Pa, r = 0.28; 3.3 + 1.6 vs 4.2 + 3.6 Pa, r = 0.42; 2.6 + 1.5 vs 3.3 + 3.0 Pa, r = 0.35, respectively. Authors conclude CCTA and IVUS produce similar outcomes when evaluating ESS.

Clinical Utility

Min et al.38 reported on outcomes from the CONFIRM trial which was comprised of 23,854 consecutively enrolled patients who underwent CCTA for suspected CAD at 12 centers. 34% of the registry was asymptomatic and had low pre-test probability of CAD at enrollment. CAD by CCTA was defined as none (0% stenosis), mild (1% to 49% stenosis), moderate (50% to 69% stenosis), or severe (>70% stenosis). Severity of CAD was determined by considering individual patient, per-vessel, and per-segment. The study cohort had a high prevalence of cardiovascular risk factors and symptoms who were ages 57 + 13 years and were 54% male, with most presenting with intermediate or high pretest likelihood of obstructive CAD. A total of 404 deaths were reported at a mean survival examined at 2.3 + 1.1 years (median 2.1 years; interquartile range: 1.5 to 3.1 years). Authors concluded that higher rates of mortality are associated in individuals without known CAD, nonobstructive and obstructive CAD by CCTA and vary further depending on age and sex. The risk of death in individuals without CAD by CCTA was very low.

Chang et al.39 conducted the ICONIC study which was a nested case-control study within the CONFIRM study consisting of a cohort of 25,251 patients undergoing CCTA to identify atherosclerotic features associated with precursors of acute coronary syndromes (ACS). Duration of follow up was 3.4 + 2.1 years. ACS patients were propensity matched 1:1 with nonevent patients with no prior CAD for risk factors and CCTA- obstructive (>50%) CAD. A total of 234 patients with ACS and control pairs were included. At baseline, >65% of patients with ACS had nonobstructive CAD while 52% had HRP, meaning the group with non-obstructive CAD at baseline represented the group that experienced the greatest number of MACE. Characteristics such as percent diameter stenosis (%DS), cross-sectional plaque burden, fibrofatty and necrotic core volume, and HRP resulted in an increased hazard ration of ACS (1.010 per %DS, 95% confidence interval [CI]: 1.005 to 1.015). Plaque analysis identified 129 “culprit lesion precursors”. Three quarters of these had <50% stenosis and 31% showing HRP. Authors concluded that evaluating plaque composition assists with identifying high risk cases.

A 2018 systematic review included 13 studies that reported on CCTA derived CPA and MACE and included 552 MACES in 13977 patients for a rate of 3.9%. In terms of plaque morphology, the strongest association was observed for noncalcified plaque (hazard ratio [HR], 1.45; 95% confidence interval [CI], 1.24–1.70; P<0.001), with weaker associations found for partially calcified (HR, 1.37; 95% CI, 1.18–1.60; P<0.001) and calcified plaques (HR, 1.23; 95% CI, 1.16–1.30; P<0.001).6 High risk plaque findings include napkin-ring sign, low-attenuation plaque, positive remodeling, and spotty calcification. All HRP findings were strongly associated with MACE and ≥ 2 HRP findings had the highest risk of MACE (HR, 9.17; 95% CI, 4.10–20.50; P<0.001). The authors conclude HRP is an independent predictor of MACE but acknowledges that clinical impact of these findings was not established.

A post hoc analysis of the SCOT-HEART trial assessed whether noncalcified LAP burden on CCTA might be a predictor of the future risk of MI. Authors investigated the association between the future risk MACE and low-attenuation plaque burden (% plaque to vessel volume), cardiovascular risk score, CACS or obstructive coronary artery stenoses. Quantitative assessment of atherosclerotic plaque subtypes was performed using standardized semiautomatic software (Autoplaque, Version 2.5, Cedars-Sinai Medical Center). The found LAP burden, CACS, obstructive coronary artery disease were all higher in the individual who experienced MI. In patients (n=1769) followed for median of 4.7 years with low-attenuation plaque burden greater than 4% were nearly 5 times more likely to have subsequent MI than those below this threshold (hazard ratio, 4.65; 95% CI, 2.06–10.5; P<0.001).7 They conclude that low-attenuation plaque can provide incremental prediction of MI to standard assessment cardiovascular risk scores, computed tomography calcium scoring or luminal stenosis severity. The strength of this study was it was multicentered, RCT with long-term follow-up and without industry funding reducing potential bias. Limitations include use of single technique to analyze plaque volume, potential influence on decision making for participants in the trial from the original CCTA results, however this may have reduced incidence of MACE so would not diminish the value of the findings. Authors state further investigation is needed to see if this can be used for clinical decision making leading to improved patient outcomes. This study is pertinent as identifies the potential role of plaque analysis as a predictor of future MACE.

A sub analysis of the Computed Tomographic Angiography for Selective Cardiac Catheterization trial (CONSERVE)40 trial assessed 747 patients who underwent CCTA prior to non-emergent ICA with AI-QCT/AI-CPA software by Cleerly, Inc.41 The AI-QCT reported on stenosis determination, coronary vascular measurements and quantification and characterization of atherosclerotic plaque. CCTA interpretation and AI-QCT/AI-CPA guided findings were evaluated for MACE at 1-year follow-up.

AI-QCT/AI-CPA reported no CAD in 9% compared to 34% with CCTA alone. For intermediate stenosis (50-69%) AI-QCT/AI-CPA identified 8% of patients (60/747) as compared to Level II/III readers in which 27% (205/747) were reported to have ≥50% stenosis, 16% (117/747) with ≥70% stenosis, and 12% (88/787) with intermediate stenosis resulting in referral for invasive intervention. The rates of safe ICA deferral from AI-QCT were significantly higher than those based upon Level II/III reader interpretation of CCTA. They reported that no patient experienced MACE for 1 year following who had been quantified as having non-severe stenosis by AI-QCT. When categorizing stenosis severity as 0%, 1%−24%, 26%−49%, 50%−69%, >70%, stenosis severity to predict MACE events was similar between AI-QCT (AUC of 0.61; 95% CI 0.52−0.70) and Level II/III CCTA interpretation (AUC of 0.63; 95% CI 0.53−0.73; p =.64). The authors conclude that for patients meeting criteria for non-emergent ICA based on ACC/ AHA Guideline that adoption of an AI-QCT approach could reduce unnecessary ICA by 87%−95% based upon stenosis severity thresholds.

For CPA there was a linear correlation between the quantification of atherosclerotic plaque and absolute plaque volume and MACE. Plaque volumes were categorized using Hounsfield unit (HU) ranges with noncalcified plaque (NCP) defined as HU between −30 and +350; low density-NCP (LD-NCP) defined as plaques < 30 HU; and calcified plaque (CP) defined as >350 HU.26

As the hazard ratio for each plaque volume increased so did the observed rate of MACE. For patients with plaque volume between 0 and 300 mm3 (n = 509), 301−750 mm3 (n = 174) and ≥750 mm3 (n =64), there was an observed MACE rate of 2.6%, 7.0%, and 9.4%, respectively, (p = .001).

The authors conclude that AI-QCT/AI-CPA referral management can avoid unnecessary ICA in patients with stable CAD with <50% or <70% stenosis. Limitations of the study include indirectness as mean age was 60 ± 12.2 which is younger than Medicare population, the population was 86% Asian and not reflective of US population, post hoc analysis, risk of bias due to lack of blinding for CCTA core laboratory and short-term follow-up.

A long-term follow-up included 536 patients enrolled in studies evaluating the long-term prognostic value of high-risk plaques.42-44 All patients underwent CCTA for suspected stable CAD between 2008-2014. AI-QCT/AI-CPA analysis was conducted using Cleerly, Inc platform to analyze the CCTA images. This study population was in Amsterdam where national registry database is maintained, was utilized to determine prognostic data from follow-up visits between 2021 and 2022 providing follow-up data for 508 patients. They were evaluated for MACE and outcomes based on plaque staging27. Coronary plaque volume was normalized to the total per–patient vessel volume to account for variation in coronary artery volume by calculating as plaque volume /vessel volume x100%. These normalized volumes were reported as percentage atheroma volume (PAV), percentage noncalcified plaque volume (NCPV), and percentage calcified plaque volume (CPV).

Artificial-intelligence-QCT analysis showed 343 patients (64%) had nonobstructive CAD (<50% stenosis), 88 (16%) had moderate obstructive CAD (≥50%-69% stenosis) and 105 (20%) had a severe obstructive stenosis of ≥70%. Plaque volume was stratified with 15 patients without plaque (CAD stage 0), 257 patients with a PAV between 0% and 5% (CAD stage 1), 149 patients with a PAV between 5% and 15% (CAD stage 2), and 115 patients with ≥15% PAV (CAD stage 3). They were also stratified based on their patients were stratified based on their NCPV: 15 patients without NCPV (NCPV stage 0), 212 patients with a NCPV volume between 0% and 2.5%, 160 patients with NCPV between 2.5% and 7.5%, and 149 patients with >7.5% NCPV. Higher PAV stages correlated with worse survival for both MACE and secondary outcomes (p<0.001). Higher NCPV stages also showed worse survival. Those with no plaque volume did not experience any events during follow-up. The addition of AI-QCT to a risk model with clinical risk factors and CACS improved risk discrimination for MACE at 2, 5, and 10 years of follow-up (10-year AUC: 0.82 [95% CI: 0.780.87] vs 0.73 [95% CI: 0.68-0.79]; p< 0.001; NRI: 0.21 [95% CI: 0.09-0.38]). The authors reported AI-QCT/AI-CPA outperformed manual stenosis grading and segment involvement score in the prediction of MACE at 10 years (AUC:0.82 [95% CI: 0.78-0.87] vs 0.78 [95% CI: 0.73-0.83]; P ¼ 0.040; NRI: 0.04 [95% CI: 0.05 to 0.27]). Additionally, they were able to calculate predictive risk for 10-year survival across subgroups based on plaque burden by stenosis grade.

The authors conclude that quantitative coronary plaque staging with AI-QCT may improve risk stratification for long term MACE. Previous studies (ICONIC) demonstrated >75% of culprit lesions prior to MI were nonobstructive. This study found plaque volumes were a greater determinant of 10-year ASCVD risk in patients with low CAD-RADS stenosis score compared to those with high scores suggesting that high-plaque burden, even in absence of obstructive lesions, are independent prognostic risk factors. This may have implications for treatment as these higher risk individuals may benefit from an intensified therapy. With the emergence of additional therapeutic options, the technology may be able to improve identification to apply treatments to those who need them the most and avoid in those who may not benefit.

This study benefits from a 10-year follow-up offering valuable long term outcome data. Limitations include indirectness as the population was younger than Medicare population (mean age 58 ± 9.2) population in non-US population, risk of bias from missing outcome data inherent to retrospective study design, imprecision related to relatively small sample size, and concerns related to study quality and follow-up of the cohort over time.

A large observational, retrospective, consecutive, international, multicenter cohort study included 11,808 patients who underwent clinically indicated CCTA. Analysis for total plaque volume and composition was performed with AI-CPA using Heartflow, Inc platform. Mean age was 62.7 ± 12.2 years with 45.9% women. The authors report median total plaque volume was 223 mm3 (IQR: 29-614 mm3) and was significantly higher in male participants (360 mm3; IQR: 78-805 mm3) compared with female participants (108 mm3; IQR: 10-388 mm3) (P < 0.0001).35

The DECODE study explores the role of AI-QCT/AI-CPA in clinical decision making. A cohort of 100 patients with suspected CAD who underwent clinically indicated CCTA were included. Three Level 3 CCTA readers reviewed the CCTA report, clinical and laboratory data and management plans using a recently published management hierarchy. The cardiologists were then provided QI-QCT/AI-CPA data and had the opportunity to alter management plan with this additional information. Management Plan Reclassification Rate (RR) following AI-QCT/AI-CPA review was 66% (66/100) (95% CI 56.72%-75.28%). The authors report that when AI-QCT/AI-CPA information was added most management plans were intensified with reclassification rates ranging from 47% in patients with CAC=0 to 96% in patients with CAC>400, and in 89.5% in patients with coronary stenosis >50%. Broken down by CAD-RADs score the RR rare was 89.5% for CAD-RADS ≥ 3 (>50% stenosis) and 51.6% with <50% stenosis.45 Limitations of the study include small sample size, potential variability among readers, lack of control group or randomization and potential risk of bias, and lack of knowledge of the changes in management impact patient outcomes.

Societal Guidance

Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR) and the North American Society of Cardiovascular Imaging (NASCI)2

Practice Guidelines in the form of a consensus document was published by SCCT, ACC, ACR and NASCI. This document reports on the standardized reporting system for CCTA called CAD-RADS. Additionally, they proposed management considerations includes further cardiac investigation and escalating clinical management, for the different levels of disease based on the CAD-RADS classification. They acknowledge emerging technologies for performing quantitative and reproducible assessments for total plaque burden and type but are not widely available and lack consensus thresholds for patient management.

The 2021 Chest Pain Guidelines from the American Heart Association (AHA) and AAC1 state:

  • Intermediate-risk patients with acute chest pain and no known CAD eligible for initial diagnostic testing after a negative or inconclusive evaluation for ACS, CCTA is useful for exclusion of atherosclerotic plaque and obstructive CAD (strong recommendation(1A) based on high quality evidence).
  • Sequential or add-one diagnostic test for intermediate-risk patients with acute chest pain and no known CAD, as well as an inconclusive prior stress test, CCTA can be useful for exclusion of atherosclerotic plaque and obstructive CAD (moderate recommendation (2a) based on expert opinion (C-EO).
  • Intermediate-risk patients with acute chest pain and known nonobstructive CAD, CCTA can be useful for exclusion of atherosclerotic plaque and obstructive CAD (moderate recommendation (2a) based on expert opinion (C-EO).

The American College of Cardiology46

The ACC Innovations in Prevention Working Group published Atherosclerosis Treatment Algorithms with the aim to personize medical intervention based on findings from CCTA and risk factors. The “Atherosclerosis Treatment Algorithms” includes recommended treatments based on plaque staging.

Atherosclerosis stages were categorized as: 25,27

  • Stage 0 = 0 mm3 (0% percent atheroma volume).
  • Stage 1 = >0-250 mm3 (>0-5.0% percent atheroma volume).
  • Stage 2 = >250-750 mm3 (>5%-15% percent atheroma volume).
  • Stage 3 = >750 mm3 (>15% percent atheroma volume).

 

Multiple interventions are reviewed with DASH diet and physical activity and Icosapent ethyl supported by RCT, statins supported by observational cohort study, colchicine supposed by a prospective study and Evolocumab by a retrospective study. The treatment algorithms also provide recommends for treatment and serial CCTA to monitor disease progression based on stage of disease.

These guidelines are limited as they have not been validated as to impact on patient outcomes, and some of the recommended treatments are supported by low quality literature. The ACC Foundation has an investment in the software utilized in the algorithms.

American Society of Preventive Cardiology (ASPC)5

A clinical practice statement was developed by an expert panel on CCTA and emerging applications od CCTA. Within this report the authors discuss the role of CCTA in calculation of plaque volume and that when plaque volumes decrease, as seen on serial CCTA with CPA, improved clinical outcomes are demonstrated. They conclude that ability to distinguish plaque composition and identify vulnerable plaques are clinically applicable for the evaluation and management of CAD. AI-QCT/AI-CPA has been shown to improve personalize preventive therapies, reduce time, and improve accuracy for CPA calculation. They state there is a high correlation demonstrated with 98% agreement with expert readers for CAD-RADS category on per patient and 99% of per vessel basis with >95% sensitivity for detection of obstructive stenosis.4 They state confidence for future clinical trials to utilize AI-QCT/AI-CPA for plaque quantification and evaluation of response to therapies. They also state: “AI solutions should be validated in multicenter clinical trials against appropriate ground truth standards in order to ensure accuracy, precision, and generalizability” and “the effect of individualized preventive therapies that is guided by the identification of AI-identified CCTA adverse plaque characteristics require study in future prospective randomized trials”.

Health Care Disparities

The demographic of CAD varies between genders, race, and age. Women have been found to have a higher rate of non-obstructive CAD which is highly prognostic (2-fold increased risk) in women of future MACE. Women were also found to carry an increased risk associated with non-obstructive left main disease, presence of HRP compared to men.5 Studies are beginning to show a difference in plaque composition between men and women which may impact disease progression. The SCCT has developed an expert consensus statement about the role of CCTA in women and future investigations are needed to further delineate these differences. Additionally, most studies did not include a population the represents the Medicare population on age and co-morbidities. Differences in the diagnostic and management related to ethnicity needs further investigation as these demographics have not been well represented in the studies.

Analysis of Evidence (Rationale for Determination)

It is well established that CCTA is an important tool for cardiac evaluation and can provide clinically relevant information for the care of patients with existing or suspected CAD. CCTA has been found beneficial to clarify the diagnosis, guide intervention and potentially reduce the risk of future MI. 15 Investigators have sought to further expand the information gathered from CCTA to offer non-invasive means to evaluate the coronary arteries with the potential to reduce the need for invasive angiography or provide further diagnostic information that may impact management. This is pertinent as the many patients with lower-risk findings may not receive as aggressive interventions and go on to experience MACE demonstrating a need for further technologies that can aid in evaluating additional risk factors.

Emerging evidence has established that the quantity and composition of plaque can play a role in identifying high risk cases. Multiple studies have identified that CCTA plaque burden is associated with cardiovascular outcomes including the ICONIC, CONFIRM and PARADIGM studies. 19,38,39 Plaque composition has emerging evidence to provide information that can aid in identification of this at-risk group and offer additional management options to reduce risk. The addition of plaque quantification was demonstrated to improve the diagnosis and prognostic risk stratification, beyond the capability of CCTA alone.42 While this information can be calculated by expert readers it is very time consuming and variability among readers has limited its utility. The ability to calculate this accurately with enhanced technologies offers the potential to incorporate into the decision making more practically. Studies have demonstrated that statins are associated with slower progression of overall coronary atherosclerosis volume, with increased plaque calcification and reduction of high-risk plaque features demonstrating there are interventions that may improve outcomes based on these findings. 19 Symptomatic patients with intermediate risk and no known CAD and negative evaluation have been identified as a group at potential elevated risk. The Chest Pain Guidelines support the role of plaque assessment in this group to further delineate risk and offer escalating intervention if indicated. This is supported by a strong recommendation and high-quality evidence (1A). Cardiac ischemia is decreased blood flow and oxygen to the heart muscle and the Chest Pain Guidelines recommend non-invasive testing for cardiac ischemia with a functional stress test (exercise or pharmacologic echocardiography or MPI)-Class I recommendation. The use of AI-QCT/AI-CPA does not extend to evaluation for cardiac ischemia defined within the limited coverage criteria of this LCD as there is currently insufficient evidence to support this role.

While other populations may potentially benefit this role has not been established by evidence so further investigation is necessary in these populations. Therefore, the policy is limited coverage to ensure access to this sub-set where benefit has been established while further investigation defines the role of this emerging technology beyond this population. The role of this technology for surveillance or monitoring response to treatment has not been established in the literature, and therefore is limited to initial diagnosis.

 

Proposed Process Information

Synopsis of Changes
Changes Fields Changed
Not Applicable N/A
Associated Information

Related LCDs

L33559 Cardiac Computed Tomography (CCT) and Coronary Computed Tomography Angiography (CCTA)

L39075 Non-Invasive Fractional Flow Reserve

 

Sources of Information
N/A
Bibliography
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  12. Nakanishi R, Motoyama S, Leipsic J, Budoff MJ. How accurate is atherosclerosis imaging by coronary computed tomography angiography? Journal of cardiovascular computed tomography. 2019;13(5):254-260.
  13. ECRI. Cleerly Coronary Data Visualization Platform (Cleerly Health) for Aiding Diagnosis of Coronary Artery Disease. https://www.ecri.org/components/ProductBriefs/Pages/210682.aspx. Published 2021. Updated 7/1/2021. Accessed October 28, 2022.
  14. Newby DE, Adamson PD, Berry C, et al. Coronary CT Angiography and 5-Year Risk of Myocardial Infarction. N Engl J Med. 2018;379(10):924-933.
  15. SCOT-HEART. CT coronary angiography in patients with suspected angina due to coronary heart disease (SCOT-HEART): an open-label, parallel-group, multicentre trial. The Lancet. 2015;385(9985):2383-2391.
  16. Douglas PS, Hoffmann U, Patel MR, et al. Outcomes of Anatomical versus Functional Testing for Coronary Artery Disease. New England Journal of Medicine. 2015;372(14):1291-1300.
  17. Hoffmann U, Ferencik M, Udelson JE, et al. Prognostic Value of Noninvasive Cardiovascular Testing in Patients With Stable Chest Pain: Insights From the PROMISE Trial (Prospective Multicenter Imaging Study for Evaluation of Chest Pain). Circulation. 2017;135(24):2320-2332.
  18. Ferencik M, Mayrhofer T, Bittner D, et al. Use of High-Risk Coronary Atherosclerotic Plaque Detection for Risk Stratification of Patients With Stable Chest Pain: A Secondary Analysis of the PROMISE Randomized Clinical Trial. JAMA cardiol 2018.
  19. Lee SE, Chang HJ, Sung JM, et al. Effects of Statins on Coronary Atherosclerotic Plaques: The PARADIGM Study. JACC Cardiovasc Imaging. 2018;11(10):1475-1484.
  20. Perez de Isla L, Diaz-Diaz JL, Romero MJ, et al. Alirocumab and Coronary Atherosclerosis in Asymptomatic Patients with Familial Hypercholesterolemia: The ARCHITECT Study. Circulation. 2023;147(19):1436-1443.
  21. Greenwood JP, Maredia N, Younger JF, et al. Cardiovascular magnetic resonance and single-photon emission computed tomography for diagnosis of coronary heart disease (CE-MARC): a prospective trial. Lancet. 2012;379(9814):453-460.
  22. Maron DJ, Hochman JS, Reynolds HR, et al. Initial Invasive or Conservative Strategy for Stable Coronary Disease. N Engl J Med. 2020;382(15):1395-1407.
  23. Lipkin I, Telluri A, Kim Y, et al. Coronary CTA With AI-QCT Interpretation: Comparison With Myocardial Perfusion Imaging for Detection of Obstructive Stenosis Using Invasive Angiography as Reference Standard. AJR Am J Roentgenol. 2022;219(3):407-419.
  24. Lu MT, Meyersohn NM, Mayrhofer T, et al. Central Core Laboratory versus Site Interpretation of Coronary CT Angiography: Agreement and Association with Cardiovascular Events in the PROMISE Trial. Radiology. 2018;287(1):87-95.
  25. Stuijfzand WJ, Van Rosendael AR, Lin FY, et al. Stress myocardial perfusion imaging vs coronary computed tomographic angiography for diagnosis of invasive vessel-specific coronary physiology: predictive modeling results from the computed tomographic evaluation of atherosclerotic determinants of myocardial ischemia (CREDENCE) trial. JAMA cardiology. 2020;5(12):1338-1348.
  26. Griffin WF, Choi AD, Riess JS, et al. AI Evaluation of Stenosis on Coronary CTA, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve: A CREDENCE Trial Substudy. JACC Cardiovasc Imaging. 2023;16(2):193-205.
  27. Min JK, Chang HJ, Andreini D, et al. Coronary CTA plaque volume severity stages according to invasive coronary angiography and FFR. J Cardiovasc Comput Tomogr. 2022;16(5):415-422.
  28. Deseive S, Kupke M, Straub R, et al. Quantified coronary total plaque volume from computed tomography angiography provides superior 10-year risk stratification. European Heart Journal-Cardiovascular Imaging. 2021;22(3):314-321.
  29. Williams MC, Kwiecinski J, Doris M, et al. Sex-specific computed tomography coronary plaque characterization and risk of myocardial infarction. Cardiovascular Imaging. 2021;14(9):1804-1814.
  30. Lin A, Manral N, McElhinney P, et al. Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study. Lancet Digit Health. 2022;4(4):e256-e265.
  31. Budoff MJ, Dowe D, Jollis JG, et al. Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial. Journal of the American College of Cardiology. 2008;52(21):1724-1732.
  32. Jonas RA, Barkovich E, Choi AD, et al. The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography. Clin Imaging. 2022;84:149-158.
  33. Cho GW, Ghanem AK, Quesada CG, et al. Quantitative plaque analysis with AI-augmented CCTA in end-stage renal disease and complex CAD. Clinical imaging. 2022;89:155-161.
  34. Jonas R, Earls J, Marques H, et al. Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence. Open Heart. 2021;8(2).
  35. Tzimas G, Gulsin GS, Everett RJ, et al. Age-and sex-specific nomographic CT quantitative plaque data from a large international cohort. JACC: Cardiovascular Imaging. 2023.
  36. Omori H, Matsuo H, Fujimoto S, et al. Determination of lipid-rich plaques by artificial intelligence-enabled quantitative computed tomography using near-infrared spectroscopy as reference. Atherosclerosis. 2023;386:117363.
  37. Hakim D, Coskun AU, Maynard C, et al. Endothelial shear stress computed from coronary computed tomography angiography: A direct comparison to intravascular ultrasound. Journal of Cardiovascular Computed Tomography. 2023;17(3):201-210.
  38. Min JK, Dunning A, Lin FY, et al. Age-and gender-related differences in all-cause mortality risk based on coronary computed tomography angiography findings: results from the International Multicenter Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter Registry of 23,854 patients without known coronary artery disease. Journal of the American College of Cardiology. 2011; 58(8):849-60.
  39. Chang HJ, Lin FY, Lee SE, et al. Coronary Atherosclerotic Precursors of Acute Coronary Syndromes. J Am Coll Cardiol. 2018;71(22):2511-2522.
  40. Chang H-J, Lin FY, Gebow D, et al. Selective referral using CCTA versus direct referral for individuals referred to invasive coronary angiography for suspected CAD: a randomized, controlled, open-label trial. JACC: Cardiovascular Imaging. 2019;12(7 Part 2):1303-1312.
  41. Kim Y, Choi AD, Telluri A, et al. Atherosclerosis Imaging Quantitative Computed Tomography (AI-QCT) to guide referral to invasive coronary angiography in the randomized controlled CONSERVE trial. Clin Cardiol. 2023;46(5):477-483.
  42. Nurmohamed NS, Bom MJ, Jukema RA, et al. AI-Guided Quantitative Plaque Staging Predicts Long-Term Cardiovascular Outcomes in Patients at Risk for Atherosclerotic CVD. JACC: Cardiovascular Imaging. 2023.
  43. van Diemen PA, Bom MJ, Driessen RS, et al. Prognostic Value of RCA Pericoronary Adipose Tissue CT-Attenuation Beyond High-Risk Plaques, Plaque Volume, and Ischemia. JACC Cardiovasc Imaging. 2021;14(8):1598-1610.
  44. Driessen RS, Bom MJ, van Diemen PA, et al. Incremental prognostic value of hybrid [15O] H2O positron emission tomography–computed tomography: Combining myocardial blood flow, coronary stenosis severity, and high-risk plaque morphology. European Heart Journal-Cardiovascular Imaging. 2020;21(10):1105-1113.
  45. Rinehart S, Raible S, Ng N, Mullen S, Huey W, Pursnani A. Utility Of AI Plaque Quantification: Results Of The Decisions For Treating Coronary Disease Are Changed In Patients Evaluated With Quantified Plaque Analysis (DECODE) Study. Journal of Cardiovascular Computed Tomography. 2023;17(4):S33.
  46. Freeman AM, Raman SV, Aggarwal M, et al. Integrating Coronary Atherosclerosis Burden and Progression with Coronary Artery Disease Risk Factors to Guide Therapeutic Decision Making. Am J Med. 2023;136(3):260-269 e267.
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Non-Invasive Technology for Coronary Artery Plaque Analysis - Multijurisdictional CAC
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05/30/2024
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  • Creation of Uniform LCDs With Other MAC Jurisdiction
  • Provider Education/Guidance
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Mr. Lance Thrash- Cleerly Labs View Letter
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National Government Services Medical Policy Unit
P.O. Box 7108
Indianapolis, IN 46207-7108
NGSDraftLCDComments@anthem.com

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Related LCDs

L33559 Cardiac Computed Tomography (CCT) and Coronary Computed Tomography Angiography (CCTA)

L39075 Non-Invasive Fractional Flow Reserve

 

Sources of Information
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Bibliography
  1. Gulati M, Levy PD, Mukherjee D, et al. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2021;144(22):e368-e454.
  2. Cury RC, Leipsic J, Abbara S, et al. CAD-RADS™ 2.0–2022 coronary artery disease-reporting and data system: an expert consensus document of the society of cardiovascular computed tomography (SCCT), the American college of cardiology (ACC), the American college of radiology (ACR), and the North America society of cardiovascular imaging (NASCI). Cardiovascular Imaging. 2022;15(11):1974-2001.
  3. Correction to: 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2021;144(22):e455.
  4. Choi AD, Marques H, Kumar V, et al. CT Evaluation by Artificial Intelligence for Atherosclerosis, Stenosis and Vascular Morphology (CLARIFY): A Multi-center, international study. J Cardiovasc Comput Tomogr. 2021;15(6):470-476.
  5. Budoff MJ, Lakshmanan S, Toth PP, et al. Cardiac CT angiography in current practice: An American society for preventive cardiology clinical practice statement(). Am J Prev Cardiol. 2022;9:100318.
  6. Nerlekar N, Ha FJ, Cheshire C, et al. Computed Tomographic Coronary Angiography-Derived Plaque Characteristics Predict Major Adverse Cardiovascular Events: A Systematic Review and Meta-Analysis. Circ Cardiovasc Imaging. 2018;11(1):e006973.
  7. Williams MC, Kwiecinski J, Doris M, et al. Low-Attenuation Noncalcified Plaque on Coronary Computed Tomography Angiography Predicts Myocardial Infarction: Results From the Multicenter SCOT-HEART Trial (Scottish Computed Tomography of the HEART). Circulation. 2020;141(18):1452-1462.
  8. Adminstration UFaD. 2020.
  9. Budoff MJ, Bhatt DL, Kinninger A, et al. Effect of icosapent ethyl on progression of coronary atherosclerosis in patients with elevated triglycerides on statin therapy: final results of the EVAPORATE trial. Eur Heart J. 2020;41(40):3925-3932.
  10. Nielsen LH, Ortner N, Nørgaard BL, Achenbach S, Leipsic J, Abdulla J. The diagnostic accuracy and outcomes after coronary computed tomography angiography vs. conventional functional testing in patients with stable angina pectoris: a systematic review and meta-analysis. European Heart Journal - Cardiovascular Imaging. 2014;15(9):961-971.
  11. Narula J, Chandrashekhar Y, Ahmadi A, et al. SCCT 2021 expert consensus document on coronary computed tomographic angiography: a report of the Society of Cardiovascular Computed Tomography. Journal of cardiovascular computed tomography. 2021;15(3):192-217.
  12. Nakanishi R, Motoyama S, Leipsic J, Budoff MJ. How accurate is atherosclerosis imaging by coronary computed tomography angiography? Journal of cardiovascular computed tomography. 2019;13(5):254-260.
  13. ECRI. Cleerly Coronary Data Visualization Platform (Cleerly Health) for Aiding Diagnosis of Coronary Artery Disease. https://www.ecri.org/components/ProductBriefs/Pages/210682.aspx. Published 2021. Updated 7/1/2021. Accessed October 28, 2022.
  14. Newby DE, Adamson PD, Berry C, et al. Coronary CT Angiography and 5-Year Risk of Myocardial Infarction. N Engl J Med. 2018;379(10):924-933.
  15. SCOT-HEART. CT coronary angiography in patients with suspected angina due to coronary heart disease (SCOT-HEART): an open-label, parallel-group, multicentre trial. The Lancet. 2015;385(9985):2383-2391.
  16. Douglas PS, Hoffmann U, Patel MR, et al. Outcomes of Anatomical versus Functional Testing for Coronary Artery Disease. New England Journal of Medicine. 2015;372(14):1291-1300.
  17. Hoffmann U, Ferencik M, Udelson JE, et al. Prognostic Value of Noninvasive Cardiovascular Testing in Patients With Stable Chest Pain: Insights From the PROMISE Trial (Prospective Multicenter Imaging Study for Evaluation of Chest Pain). Circulation. 2017;135(24):2320-2332.
  18. Ferencik M, Mayrhofer T, Bittner D, et al. Use of High-Risk Coronary Atherosclerotic Plaque Detection for Risk Stratification of Patients With Stable Chest Pain: A Secondary Analysis of the PROMISE Randomized Clinical Trial. JAMA cardiol 2018.
  19. Lee SE, Chang HJ, Sung JM, et al. Effects of Statins on Coronary Atherosclerotic Plaques: The PARADIGM Study. JACC Cardiovasc Imaging. 2018;11(10):1475-1484.
  20. Perez de Isla L, Diaz-Diaz JL, Romero MJ, et al. Alirocumab and Coronary Atherosclerosis in Asymptomatic Patients with Familial Hypercholesterolemia: The ARCHITECT Study. Circulation. 2023;147(19):1436-1443.
  21. Greenwood JP, Maredia N, Younger JF, et al. Cardiovascular magnetic resonance and single-photon emission computed tomography for diagnosis of coronary heart disease (CE-MARC): a prospective trial. Lancet. 2012;379(9814):453-460.
  22. Maron DJ, Hochman JS, Reynolds HR, et al. Initial Invasive or Conservative Strategy for Stable Coronary Disease. N Engl J Med. 2020;382(15):1395-1407.
  23. Lipkin I, Telluri A, Kim Y, et al. Coronary CTA With AI-QCT Interpretation: Comparison With Myocardial Perfusion Imaging for Detection of Obstructive Stenosis Using Invasive Angiography as Reference Standard. AJR Am J Roentgenol. 2022;219(3):407-419.
  24. Lu MT, Meyersohn NM, Mayrhofer T, et al. Central Core Laboratory versus Site Interpretation of Coronary CT Angiography: Agreement and Association with Cardiovascular Events in the PROMISE Trial. Radiology. 2018;287(1):87-95.
  25. Stuijfzand WJ, Van Rosendael AR, Lin FY, et al. Stress myocardial perfusion imaging vs coronary computed tomographic angiography for diagnosis of invasive vessel-specific coronary physiology: predictive modeling results from the computed tomographic evaluation of atherosclerotic determinants of myocardial ischemia (CREDENCE) trial. JAMA cardiology. 2020;5(12):1338-1348.
  26. Griffin WF, Choi AD, Riess JS, et al. AI Evaluation of Stenosis on Coronary CTA, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve: A CREDENCE Trial Substudy. JACC Cardiovasc Imaging. 2023;16(2):193-205.
  27. Min JK, Chang HJ, Andreini D, et al. Coronary CTA plaque volume severity stages according to invasive coronary angiography and FFR. J Cardiovasc Comput Tomogr. 2022;16(5):415-422.
  28. Deseive S, Kupke M, Straub R, et al. Quantified coronary total plaque volume from computed tomography angiography provides superior 10-year risk stratification. European Heart Journal-Cardiovascular Imaging. 2021;22(3):314-321.
  29. Williams MC, Kwiecinski J, Doris M, et al. Sex-specific computed tomography coronary plaque characterization and risk of myocardial infarction. Cardiovascular Imaging. 2021;14(9):1804-1814.
  30. Lin A, Manral N, McElhinney P, et al. Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study. Lancet Digit Health. 2022;4(4):e256-e265.
  31. Budoff MJ, Dowe D, Jollis JG, et al. Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial. Journal of the American College of Cardiology. 2008;52(21):1724-1732.
  32. Jonas RA, Barkovich E, Choi AD, et al. The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography. Clin Imaging. 2022;84:149-158.
  33. Cho GW, Ghanem AK, Quesada CG, et al. Quantitative plaque analysis with AI-augmented CCTA in end-stage renal disease and complex CAD. Clinical imaging. 2022;89:155-161.
  34. Jonas R, Earls J, Marques H, et al. Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence. Open Heart. 2021;8(2).
  35. Tzimas G, Gulsin GS, Everett RJ, et al. Age-and sex-specific nomographic CT quantitative plaque data from a large international cohort. JACC: Cardiovascular Imaging. 2023.
  36. Omori H, Matsuo H, Fujimoto S, et al. Determination of lipid-rich plaques by artificial intelligence-enabled quantitative computed tomography using near-infrared spectroscopy as reference. Atherosclerosis. 2023;386:117363.
  37. Hakim D, Coskun AU, Maynard C, et al. Endothelial shear stress computed from coronary computed tomography angiography: A direct comparison to intravascular ultrasound. Journal of Cardiovascular Computed Tomography. 2023;17(3):201-210.
  38. Min JK, Dunning A, Lin FY, et al. Age-and gender-related differences in all-cause mortality risk based on coronary computed tomography angiography findings: results from the International Multicenter Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter Registry of 23,854 patients without known coronary artery disease. Journal of the American College of Cardiology. 2011; 58(8):849-60.
  39. Chang HJ, Lin FY, Lee SE, et al. Coronary Atherosclerotic Precursors of Acute Coronary Syndromes. J Am Coll Cardiol. 2018;71(22):2511-2522.
  40. Chang H-J, Lin FY, Gebow D, et al. Selective referral using CCTA versus direct referral for individuals referred to invasive coronary angiography for suspected CAD: a randomized, controlled, open-label trial. JACC: Cardiovascular Imaging. 2019;12(7 Part 2):1303-1312.
  41. Kim Y, Choi AD, Telluri A, et al. Atherosclerosis Imaging Quantitative Computed Tomography (AI-QCT) to guide referral to invasive coronary angiography in the randomized controlled CONSERVE trial. Clin Cardiol. 2023;46(5):477-483.
  42. Nurmohamed NS, Bom MJ, Jukema RA, et al. AI-Guided Quantitative Plaque Staging Predicts Long-Term Cardiovascular Outcomes in Patients at Risk for Atherosclerotic CVD. JACC: Cardiovascular Imaging. 2023.
  43. van Diemen PA, Bom MJ, Driessen RS, et al. Prognostic Value of RCA Pericoronary Adipose Tissue CT-Attenuation Beyond High-Risk Plaques, Plaque Volume, and Ischemia. JACC Cardiovasc Imaging. 2021;14(8):1598-1610.
  44. Driessen RS, Bom MJ, van Diemen PA, et al. Incremental prognostic value of hybrid [15O] H2O positron emission tomography–computed tomography: Combining myocardial blood flow, coronary stenosis severity, and high-risk plaque morphology. European Heart Journal-Cardiovascular Imaging. 2020;21(10):1105-1113.
  45. Rinehart S, Raible S, Ng N, Mullen S, Huey W, Pursnani A. Utility Of AI Plaque Quantification: Results Of The Decisions For Treating Coronary Disease Are Changed In Patients Evaluated With Quantified Plaque Analysis (DECODE) Study. Journal of Cardiovascular Computed Tomography. 2023;17(4):S33.
  46. Freeman AM, Raman SV, Aggarwal M, et al. Integrating Coronary Atherosclerosis Burden and Progression with Coronary Artery Disease Risk Factors to Guide Therapeutic Decision Making. Am J Med. 2023;136(3):260-269 e267.

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