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Cost-effectiveness of a novel AI technology to quantify coronary inflammation and cardiovascular risk in patients undergoing routine Coronary Computed Tomography Angiography.
Tsiachristas, Apostolos; Chan, Kenneth; Wahome, Elizabeth; Kearns, Ben; Patel, Parijat; Lyasheva, Maria; Syed, Nigar; Fry, Sam; Halborg, Thomas; West, Henry; Nicol, Ed; Adlam, David; Modi, Bhavik; Kardos, Attila; Greenwood, John P; Sabharwal, Nikant; De Maria, Giovanni Luigi; Munir, Shahzad; McAlindon, Elisa; Sohan, Yogesh; Tomlins, Pete; Siddique, Muhammad; Shirodaria, Cheerag; Blankstein, Ron; Desai, Milind; Neubauer, Stefan; Channon, Keith M; Deanfield, John; Akehurst, Ron; Antoniades, Charalambos.
Afiliação
  • Tsiachristas A; Nuffield Department of Primary Care Health Sciences & Department of Psychiatry, University of Oxford, Oxford, United Kingdom.
  • Chan K; 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, United Kingdom.
  • Wahome E; 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, United Kingdom.
  • Kearns B; Lumanity, Sheffield, United Kingdom.
  • Patel P; 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, United Kingdom.
  • Lyasheva M; 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, United Kingdom.
  • Syed N; Caristo Diagnostics, Oxford, United Kingdom.
  • Fry S; Caristo Diagnostics, Oxford, United Kingdom.
  • Halborg T; 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, United Kingdom.
  • West H; 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, United Kingdom.
  • Nicol E; Sydney Medical Schol, University of Sydney, Sydney, Australia.
  • Adlam D; Royal Brompton and Harefield Hospitals, London, United Kingdom.
  • Modi B; School of Biomedical Engineering and Imaging Sciences, King's College, London, London, United Kingdom.
  • Kardos A; Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Leicester, United Kingdom.
  • Greenwood JP; Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Leicester, United Kingdom.
  • Sabharwal N; Department of Cardiology, Translational Cardiovascular Research Group, Milton Keynes University Hospital NHS Foundation Trust, Milton Keynes, United Kingdom.
  • De Maria GL; Leeds University UK & Baker Heart and Diabetes Institute, Melbourne, Australia.
  • Munir S; 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, United Kingdom.
  • McAlindon E; 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, United Kingdom.
  • Sohan Y; Royal Wolverhampton NHS Trust, Wolverhampton, United Kingdom.
  • Tomlins P; Royal Wolverhampton NHS Trust, Wolverhampton, United Kingdom.
  • Siddique M; Caristo Diagnostics, Oxford, United Kingdom.
  • Shirodaria C; Caristo Diagnostics, Oxford, United Kingdom.
  • Blankstein R; Caristo Diagnostics, Oxford, United Kingdom.
  • Desai M; 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, United Kingdom.
  • Neubauer S; Caristo Diagnostics, Oxford, United Kingdom.
  • Channon KM; Cleveland Clinic Heart and Vascular Institute, Cleveland, OH, USA.
  • Deanfield J; Cleveland Clinic Heart and Vascular Institute, Cleveland, OH, USA.
  • Akehurst R; 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, United Kingdom.
  • Antoniades C; 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, United Kingdom.
Article em En | MEDLINE | ID: mdl-39341792
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 3,393 consecutive patients who underwent routine CCTA for suspected obstructive CAD and followed up for major adverse cardiac events over a median(IQR) 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 1,214 consecutive patients with extensive guideline 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 4%, 4%, 11%, 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 £1,371-3,244), 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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Eur Heart J Qual Care Clin Outcomes Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Eur Heart J Qual Care Clin Outcomes Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Reino Unido