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<p>Coronary artery disease (CAD) is a major cause of ill health and death worldwide. Coronary computed tomographic angiography (CCTA) is the first-line investigation to detect CAD in symptomatic patients. This diagnostic approach risks greater second-line heart tests and treatments at a cost to the patient and health system. The National Health Service funded use of an artificial intelligence (AI) diagnostic tool, computed tomography (CT)-derived fractional flow reserve (FFR-CT), in patients with chest pain to improve physician decision-making and reduce downstream tests. This observational cohort study assessed the impact of FFR-CT on cardiovascular outcomes by including all patients investigated with CCTA during the national AI implementation program at 27 hospitals (CCTA <i>n</i> = 90,553 and FFR-CT <i>n</i> = 7,863). FFR-CT was safe, with no difference in all-cause (<i>n</i> = 1,134 (3.2%) versus 1,612 (2.9%), adjusted-hazard ratio (aHR) 1.00 (0.93–1.08), <i>P</i> = 0.97) or cardiovascular mortality (<i>n</i> = 465 (1.3%) versus 617 (1.1%), aHR 0.96 (0.85–1.08), <i>P</i> = 0.48), while reducing invasive coronary angiograms (<i>n</i> = 5,720 (16%) versus 8,183 (14.9%), aHR 0.93 (0.90–0.97), <i>P</i> &lt; 0.001) and noninvasive cardiac tests (189/1,000 patients versus 167/1,000), <i>P</i> &lt; 0.001). Implementation of an AI-diagnostic tool as part of a health intervention program was safe and beneficial to the patient pathway and health system with fewer cardiac tests at 2 years.</p>

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Implementation of a national AI technology program on cardiovascular outcomes and the health system

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月4日

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  • Timothy A. Fairbairn,
  • Liam Mullen,
  • Edward Nicol,
  • Gregory Y. H. Lip,
  • Matthias Schmitt,
  • Matthew Shaw,
  • Laurence Tidbury,
  • Ian Kemp,
  • Jennifer Crooks,
  • Girvan Burnside,
  • Sumeet Sharma,
  • Anoop Chauhan,
  • Chee Liew,
  • Sawan Waidyanatha,
  • Sri Iyenger,
  • Andrew Beale,
  • Imran Sunderji,
  • John P. Greenwood,
  • Manish Motwani,
  • Anna Reid,
  • Anna Beattie,
  • Justin Carter,
  • Peter Haworth,
  • Nicholas Bellenger,
  • Benjamin Hudson,
  • Jonathan Rodrigues,
  • Oliver Watson,
  • Vinod Venugopal,
  • Russell Bull,
  • Peter O’Kane,
  • Aparna Deshpande,
  • Gerald P. McCann,
  • Simon Duckett,
  • Hatef Mansoubi,
  • Victoria Parish,
  • Joban Sehmi,
  • Campbell Rogers,
  • Sarah Mullen,
  • Jonathan Weir-McCalL

摘要

Coronary artery disease (CAD) is a major cause of ill health and death worldwide. Coronary computed tomographic angiography (CCTA) is the first-line investigation to detect CAD in symptomatic patients. This diagnostic approach risks greater second-line heart tests and treatments at a cost to the patient and health system. The National Health Service funded use of an artificial intelligence (AI) diagnostic tool, computed tomography (CT)-derived fractional flow reserve (FFR-CT), in patients with chest pain to improve physician decision-making and reduce downstream tests. This observational cohort study assessed the impact of FFR-CT on cardiovascular outcomes by including all patients investigated with CCTA during the national AI implementation program at 27 hospitals (CCTA n = 90,553 and FFR-CT n = 7,863). FFR-CT was safe, with no difference in all-cause (n = 1,134 (3.2%) versus 1,612 (2.9%), adjusted-hazard ratio (aHR) 1.00 (0.93–1.08), P = 0.97) or cardiovascular mortality (n = 465 (1.3%) versus 617 (1.1%), aHR 0.96 (0.85–1.08), P = 0.48), while reducing invasive coronary angiograms (n = 5,720 (16%) versus 8,183 (14.9%), aHR 0.93 (0.90–0.97), P < 0.001) and noninvasive cardiac tests (189/1,000 patients versus 167/1,000), P < 0.001). Implementation of an AI-diagnostic tool as part of a health intervention program was safe and beneficial to the patient pathway and health system with fewer cardiac tests at 2 years.