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Percorrer por autor "Jukema, Ruurt A."

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  • Development and validation of a quantitative coronary CT Angiography model for diagnosis of vessel-specific coronary ischemia
    Publication . CREDENCE and PACIFIC-1 Investigators; Nurmohamed, Nick S.; Danad, Ibrahim; Jukema, Ruurt A.; Winter, Ruben W. de; Groot, Robin J. de; Driessen, Roel S.; Bom, Michiel J.; Diemen, Pepijn van; Pontone, Gianluca; Andreini, Daniele; Chang, Hyuk Jae; Katz, Richard J.; Stroes, Erik S. G.; Wang, Hao; Chan, Chung; Crabtree, Tami; Aquino, Melissa; Min, James K.; Earls, James P.; Bax, Jeroen J.; Choi, Andrew D.; Knaapen, Paul; Rosendael, Alexander R. van; Heo, Ran; Park, Hyung Bok; Marques, Hugo; Stuijfzand, Wijnand J.; Choi, Jung Hyun; Doh, Joon Hyung; Her, Ae Young; Koo, Bon Kwon; Nam, Chang Wook; Shin, Sang Hoon; Cole, Jason; Gimelli, Alessia; Khan, Muhammad Akram; Lu, Bin; Gao, Yang; Nabi, Faisal; Al-Mallah, Mouaz H.; Nakazato, Ryo; Schoepf, U. Joseph; Thompson, Randall C.; Jang, James J.; Ridner, Michael; Rowan, Chris; Avelar, Erick; Généreux, Philippe; Waard, Guus A. de; Sprengers, Ralf W.
    Background: Noninvasive stress testing is commonly used for detection of coronary ischemia but possesses variable accuracy and may result in excessive health care costs. Objectives: This study aimed to derive and validate an artificial intelligence-guided quantitative coronary computed tomography angiography (AI-QCT) model for the diagnosis of coronary ischemia that integrates atherosclerosis and vascular morphology measures (AI-QCTISCHEMIA) and to evaluate its prognostic utility for major adverse cardiovascular events (MACE). Methods: A post hoc analysis of the CREDENCE (Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia) and PACIFIC-1 (Comparison of Coronary Computed Tomography Angiography, Single Photon Emission Computed Tomography [SPECT], Positron Emission Tomography [PET], and Hybrid Imaging for Diagnosis of Ischemic Heart Disease Determined by Fractional Flow Reserve) studies was performed. In both studies, symptomatic patients with suspected stable coronary artery disease had prospectively undergone coronary computed tomography angiography (CTA), myocardial perfusion imaging (MPI), SPECT, or PET, fractional flow reserve by CT (FFRCT), and invasive coronary angiography in conjunction with invasive FFR measurements. The AI-QCTISCHEMIA model was developed in the derivation cohort of the CREDENCE study, and its diagnostic performance for coronary ischemia (FFR ≤0.80) was evaluated in the CREDENCE validation cohort and PACIFIC-1. Its prognostic value was investigated in PACIFIC-1. Results: In CREDENCE validation (n = 305, age 64.4 ± 9.8 years, 210 [69%] male), the diagnostic performance by area under the receiver-operating characteristics curve (AUC) on per-patient level was 0.80 (95% CI: 0.75-0.85) for AI-QCTISCHEMIA, 0.69 (95% CI: 0.63-0.74; P < 0.001) for FFRCT, and 0.65 (95% CI: 0.59-0.71; P < 0.001) for MPI. In PACIFIC-1 (n = 208, age 58.1 ± 8.7 years, 132 [63%] male), the AUCs were 0.85 (95% CI: 0.79-0.91) for AI-QCTISCHEMIA, 0.78 (95% CI: 0.72-0.84; P = 0.037) for FFRCT, 0.89 (95% CI: 0.84-0.93; P = 0.262) for PET, and 0.72 (95% CI: 0.67-0.78; P < 0.001) for SPECT. Adjusted for clinical risk factors and coronary CTA-determined obstructive stenosis, a positive AI-QCTISCHEMIA test was associated with an HR of 7.6 (95% CI: 1.2-47.0; P = 0.030) for MACE. Conclusions: This newly developed coronary CTA-based ischemia model using coronary atherosclerosis and vascular morphology characteristics accurately diagnoses coronary ischemia by invasive FFR and provides robust prognostic utility for MACE beyond presence of stenosis.
    2024Artigo científico Acesso aberto Ver mais
  • Diagnostic performance of a novel AI-guided coronary computed tomography algorithm for predicting myocardial ischemia (AI-QCTISCHEMIA) across sex and age subgroups
    Publication . Kamila, Putri Annisa; Hojjati, Tara; Nurmohamed, Nick S.; Danad, Ibrahim; Ding, Yipu; Jukema, Ruurt A.; Raijmakers, Pieter G.; Driessen, Roel S.; Bom, Michiel J.; van Diemen, Pepijn; Pontone, Gianluca; Andreini, Daniele; Chang, Hyuk Jae; Katz, Richard J.; Choi, Andrew D.; Knaapen, Paul; Bax, Jeroen J.; van Rosendael, Alexander; Heo, Ran; Park, Hyung Bok; Marques, Hugo; Stuijfzand, Wijnand J.; Choi, Jung Hyun; Doh, Joon Hyung; Her, Ae Young; Koo, Bon Kwon; Nam, Chang Wook; Shin, Sang Hoon; Cole, Jason; Gimelli, Alessia; Khan, Muhammad Akram; Lu, Bin; Gao, Yang; Nabi, Faisal; Al-Mallah, Mouaz H.; Nakazato, Ryo; Schoepf, U. Joseph; Thompson, Randall C.; Jang, James J.; Ridner, Michael; Rowan, Chris; Avelar, Erick; Généreux, Philippe; de Waard, Guus A.
    Background AI-QCTISCHEMIA is a novel artificial intelligence algorithm that predicts myocardial ischemia using quantitative features from coronary computed tomography angiography, providing a noninvasive alternative to functional imaging. However, its diagnostic performance across key demographic subgroups, particularly by sex and age, remains underexplored. We aimed to evaluate the diagnostic performance of AI-QCTISCHEMIA for predicting myocardial ischemia across these subgroups. Methods This post-hoc analysis included symptomatic patients with suspected coronary artery disease from the CREDENCE (Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia) (n = 305; 868 vessels) and PACIFIC-1 (Comparison of Coronary Computed Tomography Angiography, Single Photon Emission Computed Tomography [SPECT], Positron Emission Tomography [PET], and Hybrid Imaging for Diagnosis of Ischemic Heart Disease Determined by Fractional Flow Reserve) (n = 208; 612 vessels) studies. All patients underwent coronary computed tomography angiography, myocardial perfusion imaging (SPECT and/or PET), and invasive coronary angiography with 3-vessel fractional flow reserve as the reference standard. Diagnostic performance was evaluated at the vessel level using receiver operating characteristic analysis and under the curve (AUC), stratified by sex and age groups. Results In computed tomographic evaluation of atherosclerotic determinants of myocardial ischemia, AI-QCTISCHEMIA demonstrated higher diagnostic performance than myocardial perfusion imaging, with AUCs of 0.87 vs 0.63 in men and 0.85 vs 0.71 in women ( P < .001 for both). Similarly, in older (?65 years) and younger (<65 years) patients, AUCs were 0.85 vs 0.67 and 0.87 vs 0.63 ( P < .001 for both). In PACIFIC-1, AI-QCTISCHEMIA outperformed SPECT in men (AUC = 0.86 vs 0.67; P < .001) and women (0.81 vs 0.65; P < .001) while performing comparably with PET (0.86 vs 0.82; P = .140; 0.81 vs 0.72; P = .214). In older patients, AI-QCTISCHEMIA showed higher performance than SPECT (0.85 vs 0.73; P < .001) and was similar to PET (0.85 vs 0.86; P = .816). In younger patients, it also outperformed SPECT (0.87 vs 0.66; P < .001) with comparable performance with PET (0.87 vs 0.84; P = .338). Conclusions AI-QCTISCHEMIA demonstrated consistently high diagnostic performance to detect myocardial ischemia across sex and age groups, significantly outperforming SPECT and showing comparable performance with PET, supporting its role as a noninvasive alternative for ischemia assessment.
    2025-12-30Artigo de investigação Acesso aberto Ver mais