Why Plaque Burden is Critical to Assessing Cardiovascular Risk: An Interview with Ibrahim Danad, MD, PhD – diagnosticimaging.com

Report on AI-Powered Cardiovascular Risk Assessment and its Contribution to Sustainable Development Goals

Introduction: Limitations of Conventional Risk Assessment

Traditional methods for predicting a patient’s cardiovascular risk have historically relied on factors such as cholesterol levels, diabetes, and hypertension. According to Dr. Ibrahim Danad, a cardiologist at Radboud University Medical Center, this approach inadequately addresses the primary disease process of coronary atherosclerosis. While Coronary Computed Tomography Angiography (CCTA) allows for non-invasive visualization of the disease, its full potential has been underutilized due to a clinical focus on identifying obstructive disease as the primary cause of patient symptoms.

The Importance of Plaque Burden and Technological Innovation (SDG 9)

Research, including initial findings from the CONFIRM1 trial, indicates that the overall plaque burden, rather than the presence of obstructive lesions, is the most significant long-term predictor of cardiovascular events. This represents a paradigm shift in cardiovascular diagnostics. However, traditional risk scores and visual CCTA assessments fail to capture the true extent of plaque burden. This gap highlights the need for technological innovation in healthcare, a key component of Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure).

• Accurate assessment requires precise quantification of plaque.
• Visual inspection of CT scans is insufficient for determining a patient’s comprehensive plaque burden.
• The development of AI-powered software addresses this technological need, enhancing scientific and diagnostic capabilities in the medical sector.

The CONFIRM2 Trial: Validating AI-Powered Plaque Quantification

The recent international, multicenter CONFIRM2 trial provided significant evidence supporting the use of advanced technology for this purpose. The study involved over 1,900 patients undergoing clinically indicated CCTA exams.

1. Researchers assessed the adjunctive use of the AI-powered AI-QCT software (Cleerly) for coronary plaque quantification.
2. The software was found to provide improved prognostic assessment for major adverse cardiovascular events (MACE) and mortality.
3. Its predictive power surpassed that of established metrics, including CAD-RADS 2.0 and the coronary artery calcium score (CACS).
4. For patients without severe stenosis, the addition of AI-QCT to clinical predictive factors improved the Area Under the Curve (AUC) from 72 percent to 77 percent.

Contribution to SDG 3: Good Health and Well-being

The implementation of AI-QCT technology directly supports the achievement of Sustainable Development Goal 3 (Good Health and Well-being) by enhancing the prevention and treatment of non-communicable diseases (NCDs).

• Alignment with Target 3.4: By identifying significant plaque burden in patients previously classified as low-risk, the technology enables earlier preventive interventions, such as statin therapy. This proactive approach is crucial for reducing premature mortality from cardiovascular disease, a primary NCD.
• Alignment with Target 3.d: The AI software functions as an advanced early warning system for cardiovascular risk. It strengthens the capacity for risk reduction and management of major health risks, improving patient outcomes on a global scale.

Conclusion: A Paradigm Shift in Preventive Cardiology

The integration of AI-powered plaque quantification into CCTA analysis marks a significant advancement in preventive cardiology. By shifting the focus from obstructive lesions to overall plaque burden, clinicians can more accurately identify at-risk individuals and initiate treatment sooner. This innovation not only improves patient care but also aligns directly with the global objectives of SDG 3 and SDG 9, promoting better health outcomes through technological advancement and a focus on proactive, preventive healthcare.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

• SDG 3: Good Health and Well-being

  The article’s primary focus is on improving the diagnosis and prevention of cardiovascular disease, a leading non-communicable disease (NCD). It discusses a new AI-powered technology that enhances the prediction of cardiovascular risk, aiming to reduce adverse health events and mortality. This directly aligns with the goal of ensuring healthy lives and promoting well-being for all at all ages.

• SDG 9: Industry, Innovation, and Infrastructure

  The article highlights a significant technological advancement in medical diagnostics. The development and application of “AI-powered AI-QCT software” represents innovation in the healthcare industry. The discussion of the “international multicenter CONFIRM2 trial” underscores the role of scientific research in creating and validating these new technologies, which is central to SDG 9’s aim to foster innovation and upgrade technological capabilities.

2. What specific targets under those SDGs can be identified based on the article’s content?

• Target 3.4: Reduce premature mortality from non-communicable diseases

  The article directly addresses this target. It explains that the new AI technology provides an “improved prognostic assessment for major adverse cardiovascular events (MACE) and mortality.” By identifying plaque burden more accurately, even in patients previously considered low-risk, the technology allows for earlier preventive interventions, such as statin therapy. This proactive approach is aimed at preventing the progression of coronary atherosclerosis and thereby reducing premature deaths from cardiovascular disease.

• Target 9.5: Enhance scientific research and upgrade technological capabilities

  The article is a clear example of this target in action. It describes the development and successful trial of an innovative “AI-powered AI-QCT software” for medical imaging. The reference to the “CONFIRM2 trial” demonstrates the process of scientific research being used to validate and enhance technological capabilities within the healthcare sector. The entire discussion revolves around leveraging advanced technology to solve a long-standing clinical challenge.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

• Indicator 3.4.1: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease

  The article explicitly mentions that the AI software provides improved assessment for “MACE and mortality.” The effectiveness of this new diagnostic approach is measured by its ability to better predict and, through subsequent treatment, help reduce these specific outcomes. Therefore, the reduction in mortality and major adverse cardiovascular events serves as a direct indicator of progress towards Target 3.4.

• Implied Indicator for Target 9.5: The development and application of new technologies

  While the article does not provide quantitative data on research and development spending or personnel (as specified in official SDG indicators like 9.5.1 and 9.5.2), it provides qualitative evidence of progress. The existence and successful clinical trial of the “AI-powered AI-QCT software” is a tangible output of enhanced scientific research and technological upgrading. The article itself, reporting on this innovation, serves as an indicator of advancements in the field.

4. Table of SDGs, Targets, and Indicators

Source: diagnosticimaging.com