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AIMS: To investigate the location-specific prognostic significance of plaque burden, diameter stenosis and plaque morphology. METHODS AND RESULTS: Patients without a documented cardiac history who underwent coronary computed tomography angiography (CCTA) for suspected coronary artery disease were included. Percentage atheroma volume (PAV), maximum diameter stenosis, and plaque morphology were assessed and classified into proximal, mid, or distal segments of the coronary tree. Major adverse cardiac events (MACE) were defined as death or non-fatal myocardial infarction. Among 2819 patients 267 events (9.5%) occurred during a median follow-up of 6.9 years. When adjusted for traditional risk factors and presence of PAV on other locations, only proximal PAV was independently associated with MACE. However, PAV of the proximal segments was strongly correlated to PAV localized at the mid (R= 0.76) and distal segments (R=0.74, p<0.01 for both). When only adjusted for cardiovascular risk factors, the area under the curve (AUC) to predict MACE for proximal PAV was 0.73 (95%CI 0.69-0.76), which was similar compared to mid PAV (AUC 0.72, 95%CI 0.68-0.76) and distal PAV (AUC 0.72, 95%CI 0.68-0.76). Similar results were obtained using diameter stenosis instead of PAV. The presence of proximal low-attenuation plaque had borderline additional prognostic value. CONCLUSIONS: Proximal PAV was the strongest predictor of MACE when adjusted for cardiovascular risk factors and plaque at other locations. However, when presence of plaque was only adjusted for cardiovascular risk factors, proximal, mid, and distal plaque localization showed a similar predictive ability for MACE.
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BACKGROUND: The longitudinal relation between coronary artery disease (CAD) polygenic risk score (PRS) and long-term plaque progression and high-risk plaque (HRP) features is unknown. OBJECTIVES: The goal of this study was to investigate the impact of CAD PRS on long-term coronary plaque progression and HRP. METHODS: Patients underwent CAD PRS measurement and prospective serial coronary computed tomography angiography (CTA) imaging. Coronary CTA scans were analyzed with a previously validated artificial intelligence-based algorithm (atherosclerosis imaging-quantitative computed tomography imaging). The relationship between CAD PRS and change in percent atheroma volume (PAV), percent noncalcified plaque progression, and HRP prevalence was investigated in linear mixed-effect models adjusted for baseline plaque volume and conventional risk factors. RESULTS: A total of 288 subjects (mean age 58 ± 7 years; 60% male) were included in this study with a median scan interval of 10.2 years. At baseline, patients with a high CAD PRS had a more than 5-fold higher PAV than those with a low CAD PRS (10.4% vs 1.9%; P < 0.001). Per 10 years of follow-up, a 1 SD increase in CAD PRS was associated with a 0.69% increase in PAV progression in the multivariable adjusted model. CAD PRS provided additional discriminatory benefit for above-median noncalcified plaque progression during follow-up when added to a model with conventional risk factors (AUC: 0.73 vs 0.69; P = 0.039). Patients with high CAD PRS had an OR of 2.85 (95% CI: 1.14-7.14; P = 0.026) and 6.16 (95% CI: 2.55-14.91; P < 0.001) for having HRP at baseline and follow-up compared with those with low CAD PRS. CONCLUSIONS: Polygenic risk is strongly associated with future long-term plaque progression and HRP in patients suspected of having CAD.
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BACKGROUND AND AIMS: The aim of this study was to determine the prognostic value of coronary computed tomography angiography (CCTA)-derived atherosclerotic plaque analysis in ISCHEMIA. METHODS: Atherosclerosis imaging quantitative computed tomography (AI-QCT) was performed on all available baseline CCTAs to quantify plaque volume, composition, and distribution. Multivariable Cox regression was used to examine the association between baseline risk factors (age, sex, smoking, diabetes, hypertension, ejection fraction, prior coronary disease, estimated glomerular filtration rate, and statin use), number of diseased vessels, atherosclerotic plaque characteristics determined by AI-QCT, and a composite primary outcome of cardiovascular death or myocardial infarction over a median follow-up of 3.3 (interquartile range 2.2-4.4) years. The predictive value of plaque quantification over risk factors was compared in an area under the curve (AUC) analysis. RESULTS: Analysable CCTA data were available from 3711 participants (mean age 64 years, 21% female, 79% multivessel coronary artery disease). Amongst the AI-QCT variables, total plaque volume was most strongly associated with the primary outcome (adjusted hazard ratio 1.56, 95% confidence interval 1.25-1.97 per interquartile range increase [559 mm3]; P = .001). The addition of AI-QCT plaque quantification and characterization to baseline risk factors improved the model's predictive value for the primary outcome at 6 months (AUC 0.688 vs. 0.637; P = .006), at 2 years (AUC 0.660 vs. 0.617; P = .003), and at 4 years of follow-up (AUC 0.654 vs. 0.608; P = .002). The findings were similar for the other reported outcomes. CONCLUSIONS: In ISCHEMIA, total plaque volume was associated with cardiovascular death or myocardial infarction. In this highly diseased, high-risk population, enhanced assessment of atherosclerotic burden using AI-QCT-derived measures of plaque volume and composition modestly improved event prediction.
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Importance: Lipoprotein(a) (Lp[a]) is a causal risk factor for cardiovascular disease; however, long-term effects on coronary atherosclerotic plaque phenotype, high-risk plaque formation, and pericoronary adipose tissue inflammation remain unknown. Objective: To investigate the association of Lp(a) levels with long-term coronary artery plaque progression, high-risk plaque, and pericoronary adipose tissue inflammation. Design, Setting, and Participants: This single-center prospective cohort study included 299 patients with suspected coronary artery disease (CAD) who underwent per-protocol repeated coronary computed tomography angiography (CCTA) imaging with an interscan interval of 10 years. Thirty-two patients were excluded because of coronary artery bypass grafting, resulting in a study population of 267 patients. Data for this study were collected from October 2008 to October 2022 and analyzed from March 2023 to March 2024. Exposures: The median scan interval was 10.2 years. Lp(a) was measured at follow-up using an isoform-insensitive assay. CCTA scans were analyzed with a previously validated artificial intelligence-based algorithm (atherosclerosis imaging-quantitative computed tomography). Main Outcome and Measures: The association between Lp(a) and change in percent plaque volumes was investigated in linear mixed-effects models adjusted for clinical risk factors. Secondary outcomes were presence of low-density plaque and presence of increased pericoronary adipose tissue attenuation at baseline and follow-up CCTA imaging. Results: The 267 included patients had a mean age of 57.1 (SD, 7.3) years and 153 were male (57%). Patients with Lp(a) levels of 125 nmol/L or higher had twice as high percent atheroma volume (6.9% vs 3.0%; P = .01) compared with patients with Lp(a) levels less than 125 nmol/L. Adjusted for other risk factors, every doubling of Lp(a) resulted in an additional 0.32% (95% CI, 0.04-0.60) increment in percent atheroma volume during the 10 years of follow-up. Every doubling of Lp(a) resulted in an odds ratio of 1.23 (95% CI, 1.00-1.51) and 1.21 (95% CI, 1.01-1.45) for the presence of low-density plaque at baseline and follow-up, respectively. Patients with higher Lp(a) levels had increased pericoronary adipose tissue attenuation around both the right circumflex artery and left anterior descending at baseline and follow-up. Conclusions and Relevance: In this long-term prospective serial CCTA imaging study, higher Lp(a) levels were associated with increased progression of coronary plaque burden and increased presence of low-density noncalcified plaque and pericoronary adipose tissue inflammation. These data suggest an impact of elevated Lp(a) levels on coronary atherogenesis of high-risk, inflammatory, rupture-prone plaques over the long term.
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Background: Agatston coronary artery calcium (CAC) score is a strong predictor of mortality. However, the relationship between CAC and quantitative calcified plaque volume (CPV), which is measured on coronary computed tomography angiography (CCTA), is not well understood. Furthermore, there is limited evidence evaluating the difference between CAC versus CPV and CAC versus total plaque volume (TPV) in predicting obstructive coronary artery disease (CAD). Methods: This study included 147 subjects from the CLARIFY registry, a multicentered study of patients undergoing assessment using CCTA and CAC score as part of acute and stable chest pain evaluation. Automated software service (Cleerly.Inc, Denver, CO, USA) was used to evaluate the degree of vessel stenosis and plaque quantification on CCTA. CAC was measured using the standard Agatston method. Spearman correlation and receiver operating characteristic curve analysis was performed to evaluate the diagnostic ability of CAC, CPV and TPV in detecting obstructive CAD. Results: Results demonstrated a very strong positive correlation between CAC and CPV (r=0.76, p=0.0001) and strong correlation between CAC and TPV (r=0.72, p<0.001) at per-patient level analysis. At per-patient level analysis, the sensitivity of CAC (68%) is lower than CPV (77%) in predicting >50% stenosis, but negative predictive value is comparable. However, the sensitivity of TPV is higher compared with CAC in predicting >50% stenosis, and the negative predictive value of TPV is also higher. Conclusion: CPV and TPV are more sensitive in predicting the severity of obstructive CAD compared with the CAC score. However, the negative predictive value of CAC is comparable to CPV, but is lower than TPV. This study elucidates the relationship between CAC and quantitative plaque types, and especially emphasizes the differences between CAC and CPV which are two distinct plaque measurement techniques that are utilized in predicting obstructive CAD.
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This study assesses the agreement of Artificial Intelligence-Quantitative Computed Tomography (AI-QCT) with qualitative approaches to atherosclerotic disease burden codified in the multisociety 2022 CAD-RADS 2.0 Expert Consensus. 105 patients who underwent cardiac computed tomography angiography (CCTA) for chest pain were evaluated by a blinded core laboratory through FDA-cleared software (Cleerly, Denver, CO) that performs AI-QCT through artificial intelligence, analyzing factors such as % stenosis, plaque volume, and plaque composition. AI-QCT plaque volume was then staged by recently validated prognostic thresholds, and compared with CAD-RADS 2.0 clinical methods of plaque evaluation (segment involvement score (SIS), coronary artery calcium score (CACS), visual assessment, and CAD-RADS percent (%) stenosis) by expert consensus blinded to the AI-QCT core lab reads. Average age of subjects were 59 ± 11 years; 44% women, with 50% of patients at CAD-RADS 1-2 and 21% at CAD-RADS 3 and above by expert consensus. AI-QCT quantitative plaque burden staging had excellent agreement of 93% (k = 0.87 95% CI: 0.79-0.96) with SIS. There was moderate agreement between AI-QCT quantitative plaque volume and categories of visual assessment (64.4%; k = 0.488 [0.38-0.60]), and CACS (66.3%; k = 0.488 [0.36-0.61]). Agreement between AI-QCT plaque volume stage and CAD-RADS % stenosis category was also moderate. There was discordance at small plaque volumes. With ongoing validation, these results demonstrate a potential for AI-QCT as a rapid, reproducible approach to quantify total plaque burden.
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Inteligência Artificial , Angiografia por Tomografia Computadorizada , Angiografia Coronária , Doença da Artéria Coronariana , Estenose Coronária , Placa Aterosclerótica , Valor Preditivo dos Testes , Índice de Gravidade de Doença , Calcificação Vascular , Humanos , Feminino , Pessoa de Meia-Idade , Masculino , Idoso , Reprodutibilidade dos Testes , Calcificação Vascular/diagnóstico por imagem , Estenose Coronária/diagnóstico por imagem , Doença da Artéria Coronariana/diagnóstico por imagem , Vasos Coronários/diagnóstico por imagem , Interpretação de Imagem Radiográfica Assistida por Computador , Tomografia Computadorizada Multidetectores , Variações Dependentes do ObservadorRESUMO
BACKGROUND: Among patients with obstructive coronary artery disease (CAD) on coronary computed tomography angiography (CTA), downstream positron emission tomography (PET) perfusion imaging can be performed to assess the presence of myocardial ischemia. A novel artificial-intelligence-guided quantitative computed tomography ischemia algorithm (AI-QCTischemia) aims to predict ischemia directly from coronary CTA images. We aimed to study the prognostic value of AI-QCTischemia among patients with obstructive CAD on coronary CTA and normal or abnormal downstream PET perfusion. METHODS: AI-QCTischemia was calculated by blinded analysts among patients from the retrospective coronary CTA cohort at Turku University Hospital, Finland, with obstructive CAD on initial visual reading (diameter stenosis ≥50%) being referred for downstream 15O-H2O-PET adenosine stress perfusion imaging. All coronary arteries with their side branches were assessed by AI-QCTischemia. Absolute stress myocardial blood flow ≤2.3 âml/g/min in ≥2 adjacent segments was considered abnormal. The primary endpoint was death, myocardial infarction, or unstable angina pectoris. The median follow-up was 6.2 [IQR 4.4-8.3] years. RESULTS: 662 of 768 (86%) patients had conclusive AI-QCTischemia result. In patients with normal 15O-H2O-PET perfusion, an abnormal AI-QCTischemia result (n â= â147/331) vs. normal AI-QCTischemia result (n â= â184/331) was associated with a significantly higher crude and adjusted rates of the primary endpoint (adjusted HR 2.47, 95% CI 1.17-5.21, p â= â0.018). This did not pertain to patients with abnormal 15O-H2O-PET perfusion (abnormal AI-QCTischemia result (n â= â269/331) vs. normal AI-QCTischemia result (n â= â62/331); adjusted HR 1.09, 95% CI 0.58-2.02, p â= â0.794) (p-interaction â= â0.039). CONCLUSION: Among patients with obstructive CAD on coronary CTA referred for downstream 15O-H2O-PET perfusion imaging, AI-QCTischemia showed incremental prognostic value among patients with preserved perfusion by 15O-H2O-PET imaging, but not among those with reduced perfusion.
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Algoritmos , Inteligência Artificial , Angiografia por Tomografia Computadorizada , Angiografia Coronária , Doença da Artéria Coronariana , Circulação Coronária , Imagem de Perfusão do Miocárdio , Valor Preditivo dos Testes , Humanos , Feminino , Masculino , Imagem de Perfusão do Miocárdio/métodos , Estudos Retrospectivos , Pessoa de Meia-Idade , Idoso , Doença da Artéria Coronariana/diagnóstico por imagem , Doença da Artéria Coronariana/fisiopatologia , Doença da Artéria Coronariana/mortalidade , Prognóstico , Finlândia , Fatores de Tempo , Estenose Coronária/diagnóstico por imagem , Estenose Coronária/fisiopatologia , Estenose Coronária/mortalidade , Vasos Coronários/diagnóstico por imagem , Vasos Coronários/fisiopatologia , Reprodutibilidade dos Testes , Fatores de Risco , Índice de Gravidade de Doença , Tomografia por Emissão de Pósitrons , Adenosina/administração & dosagem , Vasodilatadores , Angina Instável/diagnóstico por imagem , Angina Instável/etiologia , Angina Instável/mortalidade , Angina Instável/fisiopatologiaRESUMO
Clinical risk scores based on traditional risk factors of atherosclerosis correlate imprecisely to an individual's complex pathophysiological predisposition to atherosclerosis and provide limited accuracy for predicting major adverse cardiovascular events (MACE). Over the past two decades, computed tomography scanners and techniques for coronary computed tomography angiography (CCTA) analysis have substantially improved, enabling more precise atherosclerotic plaque quantification and characterization. The accuracy of CCTA for quantifying stenosis and atherosclerosis has been validated in numerous multicentre studies and has shown consistent incremental prognostic value for MACE over the clinical risk spectrum in different populations. Serial CCTA studies have advanced our understanding of vascular biology and atherosclerotic disease progression. The direct disease visualization of CCTA has the potential to be used synergistically with indirect markers of risk to significantly improve prevention of MACE, pending large-scale randomized evaluation.
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Angiografia por Tomografia Computadorizada , Angiografia Coronária , Doença da Artéria Coronariana , Humanos , Angiografia por Tomografia Computadorizada/métodos , Doença da Artéria Coronariana/diagnóstico por imagem , Doença da Artéria Coronariana/diagnóstico , Medição de Risco/métodos , Angiografia Coronária/métodos , Placa Aterosclerótica/diagnóstico por imagem , Fatores de Risco de Doenças Cardíacas , Prognóstico , Estenose Coronária/diagnóstico por imagemRESUMO
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 aHR: 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.
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Angiografia por Tomografia Computadorizada , Angiografia Coronária , Doença da Artéria Coronariana , Vasos Coronários , Reserva Fracionada de Fluxo Miocárdico , Imagem de Perfusão do Miocárdio , Valor Preditivo dos Testes , Humanos , Masculino , Feminino , Pessoa de Meia-Idade , Idoso , Doença da Artéria Coronariana/diagnóstico por imagem , Doença da Artéria Coronariana/fisiopatologia , Reprodutibilidade dos Testes , Vasos Coronários/diagnóstico por imagem , Vasos Coronários/fisiopatologia , Imagem de Perfusão do Miocárdio/métodos , Prognóstico , Inteligência Artificial , Interpretação de Imagem Radiográfica Assistida por Computador , Tomografia Computadorizada de Emissão de Fóton Único , Isquemia Miocárdica/diagnóstico por imagem , Isquemia Miocárdica/fisiopatologiaRESUMO
AIMS: The incremental impact of atherosclerosis imaging-quantitative computed tomography (AI-QCT) on diagnostic certainty and downstream patient management is not yet known. The aim of this study was to compare the clinical utility of the routine implementation of AI-QCT versus conventional visual coronary CT angiography (CCTA) interpretation. METHODS AND RESULTS: In this multi-centre cross-over study in 5 expert CCTA sites, 750 consecutive adult patients referred for CCTA were prospectively recruited. Blinded to the AI-QCT analysis, site physicians established patient diagnoses and plans for downstream non-invasive testing, coronary intervention, and medication management based on the conventional site assessment. Next, physicians were asked to repeat their assessments based upon AI-QCT results. The included patients had an age of 63.8 ± 12.2 years; 433 (57.7%) were male. Compared with the conventional site CCTA evaluation, AI-QCT analysis improved physician's confidence two- to five-fold at every step of the care pathway and was associated with change in diagnosis or management in the majority of patients (428; 57.1%; P < 0.001), including for measures such as Coronary Artery Disease-Reporting and Data System (CAD-RADS) (295; 39.3%; P < 0.001) and plaque burden (197; 26.3%; P < 0.001). After AI-QCT including ischaemia assessment, the need for downstream non-invasive and invasive testing was reduced by 37.1% (P < 0.001), compared with the conventional site CCTA evaluation. Incremental to the site CCTA evaluation alone, AI-QCT resulted in statin initiation/increase an aspirin initiation in an additional 28.1% (P < 0.001) and 23.0% (P < 0.001) of patients, respectively. CONCLUSION: The use of AI-QCT improves diagnostic certainty and may result in reduced downstream need for non-invasive testing and increased rates of preventive medical therapy.
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Angiografia por Tomografia Computadorizada , Angiografia Coronária , Doença da Artéria Coronariana , Estudos Cross-Over , Humanos , Masculino , Feminino , Pessoa de Meia-Idade , Doença da Artéria Coronariana/diagnóstico por imagem , Angiografia por Tomografia Computadorizada/métodos , Angiografia Coronária/métodos , Estudos Prospectivos , Idoso , Revascularização Miocárdica , Tomografia Computadorizada por Raios X/métodosRESUMO
AIMS: Familial hypercholesterolaemia (FH) patients are subjected to a high lifetime exposure to low density lipoprotein cholesterol (LDL-C), despite use of lipid-lowering therapy (LLT). This study aimed to quantify the extent of subclinical atherosclerosis and to evaluate the association between lifetime cumulative LDL-C exposure and coronary atherosclerosis in young FH patients. METHODS AND RESULTS: Familial hypercholesterolaemia patients, divided into a subgroup of early treated (LLT initiated <25 years) and late treated (LLT initiated ≥25 years) patients, and an age- and sex-matched unaffected control group, underwent coronary CT angiography (CCTA) with artificial intelligence-guided analysis. Ninety genetically diagnosed FH patients and 45 unaffected volunteers (mean age 41 ± 3 years, 51 (38%) female) were included. Familial hypercholesterolaemia patients had higher cumulative LDL-C exposure (181 ± 54 vs. 105 ± 33â mmol/L ∗ years) and higher prevalence of coronary plaque compared with controls (46 [51%] vs. 10 [22%], OR 3.66 [95%CI 1.62-8.27]). Every 75â mmol/L ∗ years cumulative exposure to LDL-C was associated with a doubling in per cent atheroma volume (total plaque volume divided by total vessel volume). Early treated patients had a modestly lower cumulative LDL-C exposure compared with late treated FH patients (167 ± 41 vs. 194 ± 61â mmol/L ∗ years; P = 0.045), without significant difference in coronary atherosclerosis. Familial hypercholesterolaemia patients with above-median cumulative LDL-C exposure had significantly higher plaque prevalence (OR 3.62 [95%CI 1.62-8.27]; P = 0.001), compared with patients with below-median exposure. CONCLUSION: Lifetime exposure to LDL-C determines coronary plaque burden in FH, underlining the need of early as well as potent treatment initiation. Periodic CCTA may offer a unique opportunity to monitor coronary atherosclerosis and personalize treatment in FH.
This study reveals that young patients with familial hypercholesterolaemia (FH), as compared with individuals without FH, have a higher build-up of coronary artery plaque, linked directly to their increased lifetime exposure to LDL cholesterol. Genetically confirmed FH patients have a higher coronary plaque burden than those without FH, with every 75â mmol/L ∗ years increase in lifetime cumulative LDL cholesterol exposure resulting in a two-fold increase in total plaque volume. Early and potent LDL cholesterol lowering treatments are crucial for FH patients to prevent future cardiovascular diseases.
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LDL-Colesterol , Angiografia por Tomografia Computadorizada , Angiografia Coronária , Doença da Artéria Coronariana , Hiperlipoproteinemia Tipo II , Humanos , Hiperlipoproteinemia Tipo II/sangue , Hiperlipoproteinemia Tipo II/complicações , Hiperlipoproteinemia Tipo II/tratamento farmacológico , Feminino , Masculino , LDL-Colesterol/sangue , Doença da Artéria Coronariana/diagnóstico por imagem , Doença da Artéria Coronariana/prevenção & controle , Doença da Artéria Coronariana/epidemiologia , Doença da Artéria Coronariana/etiologia , Doença da Artéria Coronariana/sangue , Adulto , Biomarcadores/sangue , Fatores de Tempo , Prevalência , Pessoa de Meia-Idade , Placa Aterosclerótica , Fatores de Risco , Estudos de Casos e Controles , Resultado do Tratamento , Inibidores de Hidroximetilglutaril-CoA Redutases/uso terapêuticoRESUMO
BACKGROUND: The recent development of artificial intelligence-guided quantitative coronary computed tomography angiography analysis (AI-QCT) has enabled rapid analysis of atherosclerotic plaque burden and characteristics. OBJECTIVES: This study set out to investigate the 10-year prognostic value of atherosclerotic burden derived from AI-QCT and to compare the spectrum of plaque to manually assessed coronary computed tomography angiography (CCTA), coronary artery calcium scoring (CACS), and clinical risk characteristics. METHODS: This was a long-term follow-up study of 536 patients referred for suspected coronary artery disease. CCTA scans were analyzed with AI-QCT and plaque burden was classified with a plaque staging system (stage 0: 0% percentage atheroma volume [PAV]; stage 1: >0%-5% PAV; stage 2: >5%-15% PAV; stage 3: >15% PAV). The primary major adverse cardiac event (MACE) outcome was a composite of nonfatal myocardial infarction, nonfatal stroke, coronary revascularization, and all-cause mortality. RESULTS: The mean age at baseline was 58.6 years and 297 patients (55%) were male. During a median follow-up of 10.3 years (IQR: 8.6-11.5 years), 114 patients (21%) experienced the primary outcome. Compared to stages 0 and 1, patients with stage 3 PAV and percentage of noncalcified plaque volume of >7.5% had a more than 3-fold (adjusted HR: 3.57; 95% CI 2.12-6.00; P < 0.001) and 4-fold (adjusted HR: 4.37; 95% CI: 2.51-7.62; P < 0.001) increased risk of MACE, respectively. Addition of AI-QCT improved a model with clinical risk factors and CACS at different time points during follow-up (10-year AUC: 0.82 [95% CI: 0.78-0.87] vs 0.73 [95% CI: 0.68-0.79]; P < 0.001; net reclassification improvement: 0.21 [95% CI: 0.09-0.38]). Furthermore, AI-QCT achieved an improved area under the curve compared to Coronary Artery Disease Reporting and Data System 2.0 (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.023) and manual QCT (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.040), although net reclassification improvement was modest (0.09 [95% CI: -0.02 to 0.29] and 0.04 [95% CI: -0.05 to 0.27], respectively). CONCLUSIONS: Through 10-year follow-up, AI-QCT plaque staging showed important prognostic value for MACE and showed additional discriminatory value over clinical risk factors, CACS, and manual guideline-recommended CCTA assessment.
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Doença da Artéria Coronariana , Placa Aterosclerótica , Humanos , Masculino , Feminino , Doença da Artéria Coronariana/diagnóstico por imagem , Doença da Artéria Coronariana/terapia , Inteligência Artificial , Seguimentos , Valor Preditivo dos Testes , Artérias , Angiografia CoronáriaRESUMO
AIMS: Coronary computed tomography angiography (CTA) imaging is used to diagnose patients with suspected coronary artery disease (CAD). A novel artificial-intelligence-guided quantitative computed tomography ischemia algorithm (AI-QCTischemia) aims to identify myocardial ischemia directly from CTA images and may be helpful to improve risk stratification. The aims were 1) the prognostic value of AI-QCTischemia among symptomatic patients with suspected CAD entering diagnostic imaging with coronary CTA, and 2) the prognostic value of AI-QCTischemia separately among patients with no/non-obstructive CAD (≤50% visual diameter stenosis) and obstructive CAD (>50% visual diameter stenosis). METHODS AND RESULTS: For this cohort study, AI-QCTischemia was calculated by blinded analysts among patients with suspected CAD undergoing coronary CTA. The primary endpoint was the composite of death, myocardial infarction (MI), or unstable angina pectoris (uAP) (median follow-up 6.9 years). 1880/2271 (83%) patients were analyzable by AI-QCTischemia. Patients with an abnormal AI-QCTischemia result (n = 509/1880) vs. patients with a normal AI-QCTischemia result (n = 1371/1880) had significantly higher crude and adjusted rates of the primary endpoint (HRadj 1.96,95% CI 1.46-2.63, p < 0.001; covariates: age/sex/hypertension/diabetes/smoking/typical angina). An abnormal AI-QCTischemia result was associated with significantly higher crude and adjusted rates of the primary endpoint among patients with no/non-obstructive CAD (n = 1373/1847) (HRadj 1.81,95% CI 1.09-3.00, p = 0.022), but not among those with obstructive CAD (n = 474/1847) (HRadj 1.26,95% CI 0.75-2.12, p = 0.386) (p-interaction = 0.032). CONCLUSION: Among patients with suspected CAD, an abnormal AI-QCTischemia result was associated with a 2-fold increased adjusted rate of long-term death, MI, or uAP. AI-QCTischemia may be useful to improve risk stratification, especially among patients with no/non-obstructive CAD on coronary CTA.
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BACKGROUND AND AIMS: Artificial intelligence quantitative CT (AI-QCT) determines coronary plaque morphology with high efficiency and accuracy. Yet, its performance to quantify lipid-rich plaque remains unclear. This study investigated the performance of AI-QCT for the detection of low-density noncalcified plaque (LD-NCP) using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS). METHODS: The INVICTUS Registry is a multi-center registry enrolling patients undergoing clinically indicated coronary CT angiography and IVUS, NIRS-IVUS, or optical coherence tomography. We assessed the performance of various Hounsfield unit (HU) and volume thresholds of LD-NCP using maxLCBI4mm ≥ 400 as the reference standard and the correlation of the vessel area, lumen area, plaque burden, and lesion length between AI-QCT and IVUS. RESULTS: This study included 133 atherosclerotic plaques from 47 patients who underwent coronary CT angiography and NIRS-IVUS The area under the curve of LD-NCP<30HU was 0.97 (95% confidence interval [CI]: 0.93-1.00] with an optimal volume threshold of 2.30 mm3. Accuracy, sensitivity, and specificity were 94% (95% CI: 88-96%], 93% (95% CI: 76-98%), and 94% (95% CI: 88-98%), respectively, using <30 HU and 2.3 mm3, versus 42%, 100%, and 27% using <30 HU and >0 mm3 volume of LD-NCP (p < 0.001 for accuracy and specificity). AI-QCT strongly correlated with IVUS measurements; vessel area (r2 = 0.87), lumen area (r2 = 0.87), plaque burden (r2 = 0.78) and lesion length (r2 = 0.88), respectively. CONCLUSIONS: AI-QCT demonstrated excellent diagnostic performance in detecting significant LD-NCP using maxLCBI4mm ≥ 400 as the reference standard. Additionally, vessel area, lumen area, plaque burden, and lesion length derived from AI-QCT strongly correlated with respective IVUS measurements.
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Doença da Artéria Coronariana , Placa Aterosclerótica , Humanos , Placa Aterosclerótica/diagnóstico , Doença da Artéria Coronariana/diagnóstico , Inteligência Artificial , Espectroscopia de Luz Próxima ao Infravermelho , Ultrassonografia de Intervenção/métodos , Tomografia Computadorizada por Raios X/métodos , Angiografia Coronária/métodos , Angiografia por Tomografia Computadorizada , Vasos Coronários/diagnóstico por imagem , Vasos Coronários/patologia , Lipídeos , Valor Preditivo dos TestesRESUMO
BACKGROUND: Coronary CT angiography (CCTA) is a first-line noninvasive imaging modality for evaluating coronary artery disease (CAD). Recent advances in CCTA technology enabled semi-automated detection of coronary arteries and atherosclerosis. However, there have been to date no large-scale validation studies of automated assessment of coronary atherosclerosis phenotype and coronary artery dimensions by artificial intelligence (AI) compared to current standard invasive imaging. METHODS: INVICTUS registry is a multicenter, retrospective, and prospective study designed to evaluate the dimensions of coronary arteries, as well as the characteristic, volume, and phenotype of coronary atherosclerosis by CCTA, compared with the invasive imaging modalities including intravascular ultrasound (IVUS), near-infrared spectroscopy (NIRS)-IVUS and optical coherence tomography (OCT). All patients clinically underwent both CCTA and invasive imaging modalities within three months. RESULTS: Patients data are sent to the core-laboratories to analyze for stenosis severity, plaque characteristics and volume. The variables for CCTA are measured using an AI-based automated software and assessed independently with the variables measured at the imaging core laboratories for IVUS, NIRS-IVUS, and OCT in a blind fashion. CONCLUSION: The INVICTUS registry will provide new insights into the diagnostic value of CCTA for determining coronary atherosclerosis phenotype and coronary artery dimensions compared to IVUS, NIRS-IVUS, and OCT. Our findings will potentially shed new light on precision medicine informed by an AI-based coronary CTA assessment of coronary atherosclerosis burden, composition, and severity. (ClinicalTrials.gov: NCT04066062).
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Doença da Artéria Coronariana , Placa Aterosclerótica , Humanos , Doença da Artéria Coronariana/diagnóstico por imagem , Angiografia por Tomografia Computadorizada , Tomografia de Coerência Óptica , Inteligência Artificial , Estudos Prospectivos , Estudos Retrospectivos , Ultrassonografia de Intervenção/métodos , Valor Preditivo dos Testes , Angiografia Coronária/métodos , Vasos Coronários/diagnóstico por imagemRESUMO
OBJECTIVE: Obesity is associated with all-cause mortality and cardiovascular disease (CVD). Visceral fat (VF) is an important CVD risk metric given its independent correlation with myocardial infarction and stroke. This study aims to clarify the relationship between the presence and severity of VF with the presence and severity of coronary artery plaque. METHODS: In 145 consecutive asymptomatic patients, atherosclerosis imaging-quantitative computed tomography was performed for total plaque volume (TPV) and percentage atheroma volume, as well as the volume of noncalcified plaque (NCP), calcified plaque, and low-density NCP (LD-NCP), diameter stenosis, and vascular remodeling. This study also included VF analysis and subcutaneous fat analysis, recording of outer waist circumference, and percentage body fat analysis. RESULTS: The mean age of the patients was 56.1 [SD 8.5] years, and 84.0% were male. Measures of visceral adiposity (mean [SD, Q1-Q3 thresholds]) included estimated body fat, 28.7% (9.0%, 24.1%-33.0%); VF, 169.8 cm2 (92.3, 102.0-219.0 cm2 ); and subcutaneous fat, 223.6 mm2 (114.2, 142.5-288.0 mm2 ). The Spearman correlation coefficients of VF and plaque volume included TPV 0.22 (p = 0.0074), calcified plaque 0.12 (p = 0.62), NCP 0.25 (p = 0.0023), and LD-NCP 0.37 (p < 0.0001). There was a progression of the median coronary plaque volume for each quartile of VF including TPV (Q1: 19.8, Q2: 48.1, Q3: 86.4, and Q4: 136.6 mm3 [p = 0.0098]), NCP (Q1: 15.7, Q2: 35.4, Q3: 86.4, and Q4: 136.6 mm3 [p = 0.0032]), and LD-NCP (Q1: 0.6, Q2: 0.81, Q3: 2.0, and Q4: 5.0 mm3 [p < 0.0001]). CONCLUSIONS: These findings demonstrate progression with regard to VF and TPV, NCP volume, and LD-NCP volume. Notably, there was a progression of VF and amount of LD-NCP, which is known to be high risk for future cardiovascular events. A consistent progression may indicate the future utility of VF in CVD risk stratification.
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Doença da Artéria Coronariana , Estenose Coronária , Placa Aterosclerótica , Humanos , Masculino , Pessoa de Meia-Idade , Feminino , Doença da Artéria Coronariana/diagnóstico por imagem , Angiografia por Tomografia Computadorizada/métodos , Gordura Intra-Abdominal/diagnóstico por imagem , Angiografia Coronária/métodos , Placa Aterosclerótica/diagnóstico por imagem , Vasos Coronários/diagnóstico por imagemRESUMO
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.
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Doença da Artéria Coronariana , Estenose Coronária , Placa Aterosclerótica , Humanos , Feminino , Masculino , Pessoa de Meia-Idade , Idoso , Placa Aterosclerótica/diagnóstico por imagem , Constrição Patológica , Inteligência Artificial , Angiografia Coronária/métodos , Angiografia por Tomografia Computadorizada/métodos , Valor Preditivo dos Testes , Índice de Gravidade de DoençaRESUMO
AIMS: We compared diagnostic performance, costs, and association with major adverse cardiovascular events (MACE) of clinical coronary computed tomography angiography (CCTA) interpretation versus semiautomated approach that use artificial intelligence and machine learning for atherosclerosis imaging-quantitative computed tomography (AI-QCT) for patients being referred for nonemergent invasive coronary angiography (ICA). METHODS: CCTA data from individuals enrolled into the randomized controlled Computed Tomographic Angiography for Selective Cardiac Catheterization trial for an American College of Cardiology (ACC)/American Heart Association (AHA) guideline indication for ICA were analyzed. Site interpretation of CCTAs were compared to those analyzed by a cloud-based software (Cleerly, Inc.) that performs AI-QCT for stenosis determination, coronary vascular measurements and quantification and characterization of atherosclerotic plaque. CCTA interpretation and AI-QCT guided findings were related to MACE at 1-year follow-up. RESULTS: Seven hundred forty-seven stable patients (60 ± 12.2 years, 49% women) were included. Using AI-QCT, 9% of patients had no CAD compared with 34% for clinical CCTA interpretation. Application of AI-QCT to identify obstructive coronary stenosis at the ≥50% and ≥70% threshold would have reduced ICA by 87% and 95%, respectively. Clinical outcomes for patients without AI-QCT-identified obstructive stenosis was excellent; for 78% of patients with maximum stenosis < 50%, no cardiovascular death or acute myocardial infarction occurred. When applying an AI-QCT referral management approach to avoid ICA in patients with <50% or <70% stenosis, overall costs were reduced by 26% and 34%, respectively. CONCLUSIONS: In stable patients referred for ACC/AHA guideline-indicated nonemergent ICA, application of artificial intelligence and machine learning for AI-QCT can significantly reduce ICA rates and costs with no change in 1-year MACE.