Cost-effectiveness of a novel AI technology to quantify coronary inflammation and cardiovascular risk in patients undergoing routine Coronary Computed Tomography Angiography

医学 胸痛 冠状动脉疾病 心脏病学 内科学 指南 心脏成像 前瞻性队列研究 放射科 人口 队列 心房颤动 计算机断层血管造影 心血管事件 血管造影 风险评估 试验预测值 心肌梗塞 试验前后概率 急性冠脉综合征 计算机断层摄影术 队列研究 动脉 危险分层 狭窄
作者
Tsiachristas, A,Chan, K,Wahome, E,Kearns, B,Patel, P,Lyasheva, M,Syed, N,Fry, S,Halborg, T,West, H,Nicol, E,Adlam, D,Modi, B,Kardos, A,Greenwood, JP,Sabharwal, N,De Maria, GL,Munir, S,McAlindon, E,Sohan, Y
出处
期刊:University of Oxford - Oxford University Research Archive (ORA)
摘要

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.

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