Cost-effectiveness of a novel AI technology to quantify coronary inflammation and cardiovascular risk in patients undergoing routine coronary computed tomography angiography.
Authors
Affiliations (16)
Affiliations (16)
- Nuffield Department of Primary Care Health Sciences & Department of Psychiatry, University of Oxford, Oxford, OX2 6GG, UK.
- Acute Multidisciplinary Imaging & Interventional Centre, British Heart Foundation (BHF) Centre of Research Excellence, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.
- Lumanity, Sheffield, S1 2GQ, UK.
- Caristo Diagnostics, Oxford, OX2 0HP, UK.
- Sydney Medical School, University of Sydney, Sydney, Camperdown, NSW 2050, Australia.
- Departments of Cardiology and Radiology, Royal Brompton Hospital, London, SW3 6NP, UK.
- School of Biomedical Engineering and Imaging Sciences, King's College, London, SE1 7EH, UK.
- Department of Cardiovascular Sciences, University of Leicester, Leicester, LE1 7RH, UK.
- NIHR Leicester Biomedical Research Centre, Leicester, LE3 9QP, UK.
- Department of Cardiology, Translational Cardiovascular Research Group, Milton Keynes University Hospital NHS Foundation Trust, Milton, MK6 5LD, UK.
- Leeds Teaching Hospitals, Leeds, LS1 3EX, UK.
- Baker Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia.
- Royal Wolverhampton NHS Trust, Wolverhampton, WV10 0QP, UK.
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
- Department of Cardiovascular Medicine, Cleveland Clinic Heart Vascular and Thoracic Institute, Cleveland, OH, 44195, USA.
- Institute of Cardiovascular Science, University College London, London, WC1E 6DD, UK.
Abstract
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. 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. Compared with standard care, the addition of AI-Risk assessment in routine CCTA interpretation is cost-effective, by refining risk-guided medical management.