Cost-effectiveness of a novel AI technology to quantify coronary inflammation and cardiovascular risk in patients undergoing routine coronary computed tomography angiography

Abstract

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.

Keywords: Coronary CT angiography; Coronary artery disease; Cost-effectiveness analysis; Inflammation.

Graphical Abstract

Figure 1

Decision tree for patient undergoing coronary computed tomography angiogram for suspected coronary artery disease. (A) Proposed clinical pathway of additional AI-Risk Classification to standard care. (B) Decision tree for standard care with CCTA assessment of presence/absence of obstructive CAD and clinical risk factor stratification. (C) Decision tree after addition of AI-Risk assessment. CAD, coronary artery disease; CABG, coronary artery bypass grafting; CCTA, coronary computed tomography angiogram; and PCI, percutaneous coronary intervention.

Figure 2

Markov model illustrating transition of health state every 3 months cycle.

Figure 3

Estimated major adverse cardiac events in a cohort of 5000 simulated patients over a lifetime horizon. Data points indicate the total number of events, and bars represent 95% confidence intervals. The differences between standard care and AI-Risk guided management are expressed in absolute reduction of events (% reduction).

Figure 4

(A) Cost-effectiveness plane demonstrating the uncertainty around the incremental cost of £693 and QALY of 0.21 over 1000 sets of probabilistic sensitivity analysis, and (B) cost-effectiveness acceptability curve of AI-Risk assessment in addition to standard care compared with standard care alone assuming AI-Risk model price of £700 intercepting at an ICER of £3244. ICER, incremental cost effectiveness ratio; QALY, quality adjusted life years.