Related Experiment Video
Updated: Jun 11, 2025
Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
Published on: August 9, 2024
Cost-effectiveness of a novel AI technology to quantify coronary inflammation and cardiovascular risk in patients
Apostolos Tsiachristas1, Kenneth Chan2, Elizabeth Wahome2
1Nuffield Department of Primary Care Health Sciences & Department of Psychiatry, University of Oxford, Oxford, OX2 6GG, UK.
Insights
Integrating artificial intelligence-enhanced risk (AI-Risk) assessment into coronary computed tomography angiography (CCTA) improves cardiac event prediction and is cost-effective. This AI tool refines risk stratification for better medical management in suspected coronary artery disease (CAD).
Area of Science:
- Cardiology
- Artificial Intelligence in Medicine
- Health Economics
Background:
- Coronary Computed Tomography Angiography (CCTA) is a primary tool for diagnosing obstructive coronary artery disease (CAD) in patients with chest pain.
- Acute cardiac events frequently occur even without obstructive CAD, highlighting limitations in current risk assessment.
- Novel methods are needed to improve risk stratification beyond traditional obstructive CAD detection.
Purpose of the Study:
- To assess the lifetime cost-effectiveness of an artificial intelligence-enhanced image analysis algorithm (AI-Risk) for stratifying cardiac event risk.
- To evaluate AI-Risk's ability to quantify coronary inflammation, plaque extent, and clinical risk factors using routine CCTA data.
- To determine if AI-Risk improves medical management and reduces adverse cardiac events.
Main Methods:
- A hybrid decision-tree with a population cohort Markov model was developed using data from 3,393 patients with a median follow-up of 7.7 years.
- Prospective evaluations involved 744 patients (chest pain investigation) and 1,214 patients (cardiovascular risk profiling) to assess AI-Risk's impact on treatment initiation.
- The model projected lifetime costs and outcomes, including major adverse cardiac events, under AI-Risk guidance.
Main Results:
- AI-Risk assessment led to treatment initiation or intensification in 45% of patients undergoing CCTA for chest pain.
- In a broader risk profiling cohort, AI-Risk led to treatment changes in 39% beyond current guidelines.
- Lifetime modeling predicted significant relative reductions in myocardial infarction (11%), ischaemic stroke (4%), heart failure (4%), and cardiac death (12%) with AI-Risk guided treatment.
- The Incremental Cost-Effectiveness Ratio (ICER) was favorable (£1,371-3,244), indicating cost-effectiveness.
Conclusions:
- The addition of AI-Risk assessment to routine CCTA interpretation is cost-effective.
- AI-Risk refines risk-guided medical management, improving outcomes compared to standard care.
- Implementing AI-Risk is beneficial both within current guidelines and for patients without obstructive CAD.
Aims:
Coronary computed tomography angiography (CCTA) is a first-line investigation for chest pain in patients with suspected obstructive coronary artery disease (CAD). However, many acute cardiac events occur in the absence of obstructive CAD. We assessed the lifetime cost-effectiveness of integrating a novel artificial intelligence-enhanced image analysis algorithm (AI-Risk) that stratifies the risk of cardiac events by quantifying coronary inflammation, combined with the extent of coronary artery plaque and clinical risk factors, by analysing images from routine CCTA.
Methods And Results:
A hybrid decision-tree with population cohort Markov model was developed from 3393 consecutive patients who underwent routine CCTA for suspected obstructive CAD and followed up for major adverse cardiac events over a median (interquartile range) of 7.7(6.4-9.1) years. In a prospective real-world evaluation survey of 744 consecutive patients undergoing CCTA for chest pain investigation, the availability of AI-Risk assessment led to treatment initiation or intensification in 45% of patients. In a further prospective study of 1214 consecutive patients with extensive guidelines recommended cardiovascular risk profiling, AI-Risk stratification led to treatment initiation or intensification in 39% of patients beyond the current clinical guideline recommendations. Treatment guided by AI-Risk modelled over a lifetime horizon could lead to fewer cardiac events (relative reductions of 11%, 4%, 4%, and 12% for myocardial infarction, ischaemic stroke, heart failure, and cardiac death, respectively). Implementing AI-Risk Classification in routine interpretation of CCTA is highly likely to be cost-effective (incremental cost-effectiveness ratio £1371-3244), both in scenarios of current guideline compliance, or when applied only to patients without obstructive CAD.
Conclusions:
Compared with standard care, the addition of AI-Risk assessment in routine CCTA interpretation is cost-effective, by refining risk-guided medical management.
More Related Videos
Related Concept Videos
01:28Imaging Studies for Cardiovascular System V: CT
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT
01:30Acute Coronary Syndrome III: Diagnostic studies
01:20Imaging Studies for Cardiovascular System III: X-Ray
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
01:27Coronary Artery Disease V: Surgical Management