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Percorrer por autor "Marques, Hugo"

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  • Corrigendum to ‘Relation of Gender to Atherosclerotic Plaque Characteristics by Differing Angiographic Stenosis Severity’ The American Journal of Cardiology, Volume 204, 1 October 2023, Pages 276-283 (The American Journal of Cardiology (2023) 204 (276–283), (S0002914923005325), (10.1016/j.amjcard.2023.07.004))
    Publication . Jonas, Rebecca; Patel, Toral; Crabtree, Tami R.; Jennings, Robert S.; Heo, Ran; Park, Hyung Bok; Marques, Hugo; Chang, Hyuk Jae; Stuijfzand, Wijnand J.; Rosendael, Alexander R.van; 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; Driessen, Roel S.; Bom, Michiel J.; Thompson, Randall C.; Jang, James J.; Ridner, Michael; Rowan, Chris; Avelar, Erick; Généreux, Philippe; Knaapen, Paul; de Waard, Guus A.; Pontone, Gianluca; Andreini, Daniele; Bax, Jeroen J.; Choi, Andrew D.; Earls, James P.; Hoffmann, Udo; Min, James K.; Villines, Todd C.
    The authors regret that the original version was published with the wrong ac affiliation. The affiliation has now been corrected. The authors regret any inconvenience caused.
    2023Artigo científico Acesso aberto Ver mais
  • 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
  • Prognostic value of aI-based quantitative coronary CTA vs human reader-based visual assessment: results from the CONFIRM2 registry
    Publication . Rosendael, Alexander van; Nakanishi, Rine; Bax, Jeroen J.; Pontone, Gianluca; Mushtaq, Saima; Buechel, Ronny R.; Gräni, Christoph; Feuchtner, Gudrun; Lacaita, Pietro G.; Patel, Amit R.; Singulane, Cristiane C.; Choi, Andrew D.; Al-Mallah, Mouaz; Andreini, Daniele; Karlsberg, Ronald P.; Cho, Geoffrey W.; Rochitte, Carlos E.; Alasnag, Mirvat; Hamdan, Ashraf; Cademartiri, Filippo; Maffei, Erica; Marques, Hugo; Gonçalves, Pedro de Araújo; Gupta, Himanshu; Hadamitzky, Martin; Khalique, Omar; Kalra, Dinesh; Mills, James D.; Nurmohamed, Nick S.; Knaapen, Paul; Budoff, Matthew; Shaikh, Kashif; Martin, Enrico; German, David M.; Ferencik, Maros; Oehler, Andrew C.; Deaño, Roderick; Nagpal, Prashant; Assen, Marly van; Cecco, Carlo N. de; Kamperidis, Vasileios; Foldyna, Borek; Brendel, Jan M.; Cheng, Victor Y.; Branch, Kelley R.; Bittencourt, Marcio; Bhatti, Sabha; Polsani, Venkateshwar; Wesbey, George; Cardoso, Rhanderson; Blankstein, Ron; Delago, Augustin; Pursnani, Amit; Alsaid, Amro; Singh, Vasvi; Aquino, Melissa; Park, Jisuk; Danad, Ibrahim
    Background: The severity and extent of whole heart coronary plaque volume and stenosis can be reliably measured by artificial intelligence–guided quantitative coronary computed tomography angiography (AI-QCT). Limited data are available on the potential incremental prognostic value compared with currently recommended qualitative coronary computed tomography angiography (CTA) reads and the coronary artery calcium score (CACS). Objectives: The aim of this study was to evaluate the prognostic value of AI-QCT compared with human coronary CTA reads, including the CAD-RADS (Coronary Artery Disease–Reporting and Data System), CACS, and the modified Duke Index. Methods: CONFIRM2 (Quantitative COroNary CT Angiography Evaluation For Evaluation of Clinical Outcomes: An InteRnational, Multicenter Registry) is a multicenter, international, observational cohort study of patients undergoing clinically indicated coronary CTA with follow-up for major adverse cardiac events (MACE). Asymptomatic patients and those with cardiac history were excluded. Coronary artery disease presence, extent, and composition were quantified by AI-QCT across the coronary tree, yielding 24 patient-, vessel-, and plaque-level variables. On the basis of prior analyses, noncalcified plaque burden and diameter stenosis were identified as the strongest predictors and combined for statistical modeling as “AI-QCT.” Comparator computed tomography scores included CAD-RADS, CACS, and the modified Duke Index, whereas clinical predictors were summarized in the risk factor–weighted clinical likelihood score. Area under the curve (AUC) and continuous net reclassification index (NRI) were calculated to assess the incremental value. The primary endpoint was MACE (death, myocardial infarction [MI], stroke, heart failure, late revascularization, or hospital stay for unstable angina), and the secondary endpoint was death or MI. Results: In 1,916 patients with all risk scores available, 87 (4.5%) MACE and 27 (1.4%) death/MI events occurred during 3 years of follow-up. There was a stepwise risk increase with higher coronary artery disease classifications with CAD-RADS and CACS. The addition of AI-QCT significantly improved risk stratification for MACE compared with CAD-RADS (AUC: 0.81 vs 0.79; P < 0.001 and NRI: 0.47; P < 0.001), CACS (AUC: 0.79 vs 0.70; P < 0.001 and NRI 0.61; P < 0.001), the modified Duke Index (AUC: 0.81 vs 0.76; P < 0.001 and NRI: 0.52; P < 0.001), and CAD-RADS + CACS model (AUC: 0.81 vs 0.79; P = 0.004 and NRI: 0.54; P < 0.001). AI-QCT also improved discrimination when results were adjusted for the risk factor–weighted clinical likelihood and for the prediction of death/MI. Excluding 195 patients with severe stenosis (?70%), in a multivariable model of CAD-RADS and AI-QCT, only AI-QCT was significantly associated with MACE and death/MI, and AI-QCT significantly improved risk stratification compared with CAD-RADS for MACE (AUC: 0.77 vs 0.72; P < 0.001 and NRI: 0.54; P < 0.001) and death/MI (AUC: 0.81 vs 0.73; P = 0.011 and NRI: 0.69; P = 0.001). Conclusions: AI-QCT provided incremental prognostic information compared with CAD-RADS 2.0, CACS, and the modified Duke Index for the prediction of MACE as well as the secondary endpoint of death or nonfatal MI.
    2026-01-01Artigo de investigação Acesso aberto Ver mais
  • Relation of gender to atherosclerotic plaque characteristics by differing angiographic stenosis severity
    Publication . Jonas, Rebecca; Patel, Toral; Crabtree, Tami R.; Jennings, Robert S.; Heo, Ran; Park, Hyung Bok; Marques, Hugo; Chang, Hyuk Jae; Stuijfzand, Wijnand J.; van Rosendael, Alexander R.; 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; Driessen, Roel S.; Bom, Michiel J.; Thompson, Randall C.; Jang, James J.; Ridner, Michael; Rowan, Chris; Avelar, Erick; Généreux, Philippe; Knaapen, Paul; de Waard, Guus A.; Pontone, Gianluca; Andreini, Daniele; Bax, Jeroen J.; Choi, Andrew D.; Earls, James P.; Hoffmann, Udo; Min, James K.; Villines, Todd C.
    It is unknown whether gender influences the atherosclerotic plaque characteristics (APCs) of lesions of varying angiographic stenosis severity. This study evaluated the imaging data of 303 symptomatic patients from the derivation arm of the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial, all of whom underwent coronary computed tomographic angiography and clinically indicated nonemergent invasive coronary angiography upon study enrollment. Index tests were interpreted by 2 blinded core laboratories, one of which performed quantitative coronary computed tomographic angiography using an artificial intelligence application to characterize and quantify APCs, including percent atheroma volume (PAV), low-density noncalcified plaque (LD-NCP), noncalcified plaque (NCP), calcified plaque (CP), lesion length, positive arterial remodeling, and high-risk plaque (a combination of LD-NCP and positive remodeling ≥1.10); the other classified lesions as obstructive (≥50% diameter stenosis) or nonobstructive (<50% diameter stenosis) based on quantitative invasive coronary angiography. The relation between APCs and angiographic stenosis was further examined by gender. The mean age of the study cohort was 64.4 ± 10.2 years (29.0% female). In patients with obstructive disease, men had more LD-NCP PAV (0.5 ± 0.4 vs 0.3 ± 0.8, p = 0.03) and women had more CP PAV (11.7 ± 1.6 vs 8.0 ± 0.8, p = 0.04). Obstructive lesions had more NCP PAV compared with their nonobstructive lesions in both genders, however, obstructive lesions in women also demonstrated greater LD-NCP PAV (0.4 ± 0.5 vs 1.0 ± 1.8, p = 0.03), and CP PAV (17.4 ± 16.5 vs 25.9 ± 18.7, p = 0.03) than nonobstructive lesions. Comparing the composition of obstructive lesions by gender, women had more CP PAV (26.3 ± 3.4 vs 15.8 ± 1.5, p = 0.005) whereas men had more NCP PAV (33.0 ± 1.6 vs 26.7 ± 2.5, p = 0.04). Men had more LD-NCP PAV in nonobstructive lesions compared with women (1.2 ± 0.2 vs 0.6 ± 0.2, p = 0.02). In conclusion, there are gender-specific differences in plaque composition based on stenosis severity.
    2023-10-01Artigo científico Acesso aberto Ver mais
  • Risk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method: results from the PARADIGM registry
    Publication . Park, Hyung Bok; Lee, Jina; Hong, Yongtaek; Byungchang, So; Kim, Wonse; Lee, Byoung K.; Lin, Fay Y.; Hadamitzky, Martin; Kim, Yong Jin; Conte, Edoardo; Andreini, Daniele; Pontone, Gianluca; Budoff, Matthew J.; Gottlieb, Ilan; Chun, Eun Ju; Cademartiri, Filippo; Maffei, Erica; Marques, Hugo; Gonçalves, Pedro de A.; Leipsic, Jonathon A.; Shin, Sanghoon; Choi, Jung H.; Virmani, Renu; Samady, Habib; Chinnaiyan, Kavitha; Stone, Peter H.; Berman, Daniel S.; Narula, Jagat; Shaw, Leslee J.; Bax, Jeroen J.; Min, James K.; Kook, Woong; Chang, Hyuk Jae
    Background and Hypothesis: The recently introduced Bayesian quantile regression (BQR) machine-learning method enables comprehensive analyzing the relationship among complex clinical variables. We analyzed the relationship between multiple cardiovascular (CV) risk factors and different stages of coronary artery disease (CAD) using the BQR model in a vessel-specific manner. Methods: From the data of 1,463 patients obtained from the PARADIGM (NCT02803411) registry, we analyzed the lumen diameter stenosis (DS) of the three vessels: left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA). Two models for predicting DS and DS changes were developed. Baseline CV risk factors, symptoms, and laboratory test results were used as the inputs. The conditional 10%, 25%, 50%, 75%, and 90% quantile functions of the maximum DS and DS change of the three vessels were estimated using the BQR model. Results: The 90th percentiles of the DS of the three vessels and their maximum DS change were 41%–50% and 5.6%–7.3%, respectively. Typical anginal symptoms were associated with the highest quantile (90%) of DS in the LAD; diabetes with higher quantiles (75% and 90%) of DS in the LCx; dyslipidemia with the highest quantile (90%) of DS in the RCA; and shortness of breath showed some association with the LCx and RCA. Interestingly, High-density lipoprotein cholesterol showed a dynamic association along DS change in the per-patient analysis. Conclusions: This study demonstrates the clinical utility of the BQR model for evaluating the comprehensive relationship between risk factors and baseline-grade CAD and its progression.
    2023-03Artigo científico Acesso aberto Ver mais
  • Sex and age-specific interactions of coronary atherosclerotic plaque onset and prognosis from coronary computed tomography
    Publication . Rosendael, Sophie E. Van; Bax, A. Maxim; Lin, Fay Y.; Achenbach, Stephan; Andreini, Daniele; Budoff, Matthew J.; Cademartiri, Filippo; Callister, Tracy Q.; Chinnaiyan, Kavitha; Chow, Benjamin J. W.; Cury, Ricardo C.; DeLago, Augustin J.; Feuchtner, Gudrun; Hadamitzky, Martin; Hausleiter, Joerg; Kaufmann, Philipp A.; Kim, Yong Jin; Leipsic, Jonathon A.; Maffei, Erica; Marques, Hugo; Gonçalves, Pedro de Araújo; Pontone, Gianluca; Raff, Gilbert L.; Rubinshtein, Ronen; Villines, Todd C.; Chang, Hyuk Jae; Berman, Daniel S.; Min, James K.; Bax, Jeroen J.; Shaw, Leslee J.; Rosendael, Alexander R. Van
    Aims: The totality of atherosclerotic plaque derived from coronary computed tomography angiography (CCTA) emerges as a comprehensive measure to assess the intensity of medical treatment that patients need. This study examines the differences in age onset and prognostic significance of atherosclerotic plaque burden between sexes. Methods and results: From a large multi-center CCTA registry the Leiden CCTA score was calculated in 24 950 individuals. A total of 11 678 women (58.5 ± 12.4 years) and 13 272 men (55.6 ± 12.5 years) were followed for 3.7 years for major adverse cardiovascular events (MACE) (death or myocardial infarction). The age where the median risk score was above zero was 12 years higher in women vs. men (64-68 years vs. 52-56 years, respectively, P < 0.001). The Leiden CCTA risk score was independently associated with MACE: score 6-20: HR 2.29 (1.69-3.10); score > 20: HR 6.71 (4.36-10.32) in women, and score 6-20: HR 1.64 (1.29-2.08); score > 20: HR 2.38 (1.73-3.29) in men. The risk was significantly higher for women within the highest score group (adjusted P-interaction = 0.003). In pre-menopausal women, the risk score was equally predictive and comparable with men. In post-menopausal women, the prognostic value was higher for women [score 6-20: HR 2.21 (1.57-3.11); score > 20: HR 6.11 (3.84-9.70) in women; score 6-20: HR 1.57 (1.19-2.09); score > 20: HR 2.25 (1.58-3.22) in men], with a significant interaction for the highest risk group (adjusted P-interaction = 0.004). Conclusion: Women developed coronary atherosclerosis approximately 12 years later than men. Post-menopausal women within the highest atherosclerotic burden group were at significantly higher risk for MACE than their male counterparts, which may have implications for the medical treatment intensity.
    2023-09-01Artigo científico Acesso aberto Ver mais