Association of Plaque Location and Vessel Geometry Determined by Coronary Computed Tomographic Angiography With Future Acute Coronary Syndrome–Causing Culprit Lesions

Donghee Han; Andrew Lin; Keiichiro Kuronuma; Evangelos Tzolos; Alan C Kwan; Eyal Klein; Daniele Andreini; Jeroen J Bax; Filippo Cademartiri; Kavitha Chinnaiyan; Benjamin J W Chow; Edoardo Conte; Ricardo C Cury; Gudrun Feuchtner; Martin Hadamitzky; Yong-Jin Kim; Jonathon A Leipsic; Erica Maffei; Hugo Marques; Fabian Plank; Gianluca Pontone; Todd C Villines; Mouaz H Al-Mallah; Pedro de Araújo Gonçalves; Ibrahim Danad; Heidi Gransar; Yao Lu; Ji-Hyun Lee; Sang-Eun Lee; Lohendran Baskaran; Subhi J Al’Aref; Yeonyee E Yoon; Alexander Van Rosendael; Matthew J Budoff; Habib Samady; Peter H Stone; Renu Virmani; Stephan Achenbach; Jagat Narula; Hyuk-Jae Chang; James K Min; Fay Y Lin; Leslee J Shaw; Piotr J Slomka; Damini Dey; Daniel S Berman

doi:10.1001/jamacardio.2021.5705

. 2022 Jan 26;7(3):309–319. doi: 10.1001/jamacardio.2021.5705

Association of Plaque Location and Vessel Geometry Determined by Coronary Computed Tomographic Angiography With Future Acute Coronary Syndrome–Causing Culprit Lesions

Donghee Han ¹, Andrew Lin ¹, Keiichiro Kuronuma ¹, Evangelos Tzolos ², Alan C Kwan ¹, Eyal Klein ¹, Daniele Andreini ³, Jeroen J Bax ⁴, Filippo Cademartiri ⁵, Kavitha Chinnaiyan ⁶, Benjamin J W Chow ⁷, Edoardo Conte ³, Ricardo C Cury ⁸, Gudrun Feuchtner ⁹, Martin Hadamitzky ¹⁰, Yong-Jin Kim ¹¹, Jonathon A Leipsic ¹², Erica Maffei ¹³, Hugo Marques ¹⁴, Fabian Plank ⁹, Gianluca Pontone ³, Todd C Villines ¹⁵, Mouaz H Al-Mallah ¹⁶, Pedro de Araújo Gonçalves ¹⁴, Ibrahim Danad ¹⁷, Heidi Gransar ¹, Yao Lu ¹⁸, Ji-Hyun Lee ¹⁹, Sang-Eun Lee ²⁰, Lohendran Baskaran ²¹, Subhi J Al’Aref ²², Yeonyee E Yoon ¹⁸, Alexander Van Rosendael ¹⁸, Matthew J Budoff ²³, Habib Samady ²⁴, Peter H Stone ²⁵, Renu Virmani ²⁶, Stephan Achenbach ²⁷, Jagat Narula ²⁸, Hyuk-Jae Chang ²⁹, James K Min ³⁰, Fay Y Lin ¹⁸, Leslee J Shaw ¹⁸, Piotr J Slomka ¹, Damini Dey ¹, Daniel S Berman ^1,^✉

¹Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California

²BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom

³Department of Clinical Sciences and Community Health, University of Milan, Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy

⁴Department of Cardiology, Heart Lung Center, Leiden University Medical Center, Leiden, the Netherlands

⁵Cardiovascular Imaging Center, SDN IRCCS, Naples, Italy

⁶Department of Cardiology, William Beaumont Hospital, Royal Oaks, Michigan

⁷Department of Medicine and Radiology, University of Ottawa, Ottawa, Ontario, Canada

⁸Baptist Cardiac and Vascular Institute, Miami, Florida

⁹Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria

¹⁰Department of Radiology and Nuclear Medicine, German Heart Center, Munich, Germany

¹¹Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea

¹²Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada

¹³Department of Radiology, Marche, Urbino, Italy

¹⁴UNICA, Unit of Cardiovascular Imaging, Hospital da Luz, Lisboa, Portugal

¹⁵Cardiology Service, Walter Reed National Military Center, Bethesda, Maryland

¹⁶Houston Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, Houston, Texas

¹⁷Department of Cardiology, VU University Medical Center, Amsterdam, the Netherlands

¹⁸Dalio Institute of Cardiovascular Imaging, Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York

¹⁹Division of Cardiology, Department of Internal Medicine, Myongji Hospital, Hanyang University College of Medicine, Goyang, Republic of Korea

²⁰Department of Cardiology, Ewha Womans University Seoul Hospital, Seoul, South Korea

²¹Department of Cardiovascular Medicine, National Heart Centre, Singapore

²²Division of Cardiology, Department of Medicine, University of Arkansas for Medical Sciences, Little Rock

²³Department of Medicine, Lundquist Institute at Harbor-UCLA (University of California, Los Angeles), Torrance, California

²⁴Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia

²⁵Cardiovascular Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts

²⁶Department of Pathology, CVPath Institute, Gaithersburg, Maryland

²⁷Department of Cardiology, University of Erlangen, Erlangen, Germany

²⁸Department of Cardiology, Icahn School of Medicine at Mt Sinai Hospital, New York, New York

²⁹Division of Cardiology, Severance Cardiovascular Hospital, Integrative Cardiovascular Imaging Center, Yonsei University College of Medicine, Seoul, South Korea

³⁰Cleerly Inc, New York, New York

Accepted for Publication: November 30, 2021.

Published Online: January 26, 2022. doi:10.1001/jamacardio.2021.5705

^✉

Corresponding Author: Daniel S. Berman, MD, Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048 (daniel.berman@cshs.org).

Author Contributions: Drs Han and Berman had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Han and Lin served as co–first authors. Drs Dey and Berman served as co–senior authors.

Concept and design: Han, A. Lin, Kwan, Andreini, Bax, Cademartiri, Chinnaiyan, Kim, Maffei, Marques, Pontone, Villines, Danad, Lu, J.-H. Lee, Al’Aref, Van Rosendael, Achenbach, Narula, F. Y. Lin, Dey.

Acquisition, analysis, or interpretation of data: Han, A. Lin, Kuronuma, Tzolos, Kwan, Klein, Andreini, Cademartiri, Chinnaiyan, Chow, Conte, Cury, Feuchtner, Hadamitzky, Kim, Leipsic, Maffei, Marques, Plank, Pontone, Villines, Al-Mallah, de Araújo Gonçalves, Danad, Gransar, Lu, S.-E. Lee, Baskaran, Yoon, Budoff, Samady, Stone, Virmani, Chang, Min, F. Y. Lin, Shaw, Slomka, Dey, Berman.

Drafting of the manuscript: Han, A. Lin, Tzolos, Bax, Cury, Chang, Slomka.

Critical revision of the manuscript for important intellectual content: A. Lin, Kuronuma, Tzolos, Kwan, Klein, Andreini, Bax, Cademartiri, Chinnaiyan, Chow, Conte, Cury, Feuchtner, Hadamitzky, Kim, Leipsic, Maffei, Marques, Plank, Pontone, Villines, Al-Mallah, de Araújo Gonçalves, Danad, Gransar, Lu, J.-H. Lee, S.E. Lee, Baskaran, Al’Aref, Yoon, Van Rosendael, Budoff, Samady, Stone, Virmani, Achenbach, Narula, Min, F. Y. Lin, Shaw, Slomka, Dey, Berman.

Statistical analysis: Han, A. Lin, Klein, Cademartiri, Maffei, Lu, Chang, Min, F. Y. Lin, Shaw, Dey.

Obtained funding: Berman.

Administrative, technical, or material support: A. Lin, Kuronuma, Tzolos, Andreini, Chow, Conte, Hadamitzky, Plank, Villines, de Araújo Gonçalves, Danad, Virmani, Min, F. Y. Lin, Slomka, Dey, Berman.

Supervision: Kwan, Andreini, Bax, Cademartiri, Cury, Feuchtner, Maffei, Pontone, Villines, Al-Mallah, Yoon, Van Rosendael, Budoff, Achenbach, Dey, Berman.

Conflict of Interest Disclosures: Dr Kwan reported receiving grants from the Doris Duke Charitable Foundation during the conduct of the study. Dr Bax reported receiving speaker fees from Abbott Laboratories, Edwards Lifesciences, research grants from Abbott Laboratories, Edwards Lifesciences, Medtronic plc, Boston Scientific Corporation, and Biotronik outside the submitted work. Dr Chinnaiyan reported receiving institutional grants from HeartfFlow Inc during the conduct of the study. Dr Chow reported research collaboration and support from Artrya Ltd, grants from AusculSciences and TD Bank, nonfinancial research support from Siemens AG, and equity interest from GE Healthcare during the conduct of the study. Dr Cury reported consulting for Covera Health, GE Healthcare, and Cleerly Inc outside the submitted work. Dr Leipsic reported consulting for and having stock options from HeartFlow Inc, speaking fees from Philips, and institutional grants from GE Healthcare outside the submitted work. Dr Al-Mallah reported receiving grants from Siemens AG outside the submitted work. Dr Al’Aref reported receiving grants from the National Institutes of Health (NIH) during the conduct of the study and textbook royalties from Elsevier outside the submitted work. Dr Samady reported receiving personal fees from Philips, Abbott Vascular, and Medtronic plc during the conduct of the study, being a cofounder and equity holder of Covanos Inc, and equity holder of SIG outside the submitted work. Dr Virmani reported receiving grants from the NIH, Leducq Foundation, 4C Medical, 4Tech, Abbott Vascular, Ablative Solutions, Absorption Systems, Advanced NanoTherapies, Aerwave Medical Inc, Alivas, Amgen Inc, Asahi Medical, Aurios Medical, Avantec Vascular, BD Bioscences, Biosensors, Biotronik, Biotyx Medical, Bolt Medical Inc, Boston Scientific, Canon Inc, Cardiac Implants LLC, Cardiawave, CardioMech, Cardionomic, CeloNova BioSciences Inc, Cerus Endovascular Inc, Chansu Vascular Technologies LLC, Children’s National Medical Center, Concept Medical, Cook Medical, Cooper Health, Cormaze Technologies GmbH, CRL/AccelLab, CroíValve, CSI, DexCom Inc, Edwards Lifesciences, Elucid Bioimaging, eLum Technologies Inc, Emboline Inc, Endotronix, Envision, Filterlex, Imperative Care Inc, Innovative Cardiovascular Solutions LLC, Intact Vascular Inc, Interface Biolgics, InterShunt Technologies Inc, Invatin Technologies, Lahav CRO, LimFlow, L & J Biosciences, Lutonix, Lyra Therapeutics Inc, Mayo Clinic, Maywell, MD Start, MedAlliance, Medanex Clinic, Medtronic plc, Mercator Limited, MicroPort, MicroVention, Neovasc Inc, Nephronyx Ltd, Nova Vascular, Nyra Medical, Occultech, Olympus Therapeutics, OhioHealth, OrbusNeich, Ossio, phenox Inc, Pi-Cardia, Polares Medical, PolyVascular, Profusa Inc, ProKidney LLC, Protembis, Pulse Biosciences Inc, Qool Therapeutics, Recombinetics, ReCor Medical Inc, Regencor Inc, Renata Medical, Restore Medical Ltd, Ripple Therapeutics, Rush University, Sanofi SA, Shockwave Medical, Sahajanand Medical Technologies Limited, SoundPipe, Spartan Micro, SpectraWAVE Inc, Surmodics Inc, Terumo Corporation, Jacobs Institute, Transmural Systems LLC, Transverse Medical Inc, TruLeaf Medical, University of California, San Francisco, University of Pennsylvania Medical Center, Vascudyne Inc, Vesper, Vetex Medical, WhiteSwell, WL Gore & Associates Inc, and Xeltis and personal fees from Abbott Vascular, Boston Scientific, CeloNova BioSciences Inc, OrbusNeich, Terumo Corporation, W. L. Gore & Associates Inc, Edwards Lifesciences, Cook Medical, CSI, ReCor Medical, Sino Medical Sciences Technology Inc, Surmodics Inc, Bard BD, Medtronic plc, and Xeltis outside the submitted work. Dr Min reported serving on the medical advisory board of Arineta during the conduct of the study, having an equity interest and serving on the medical advisory board of Arineta, and having an equity interest and being an employee of Cleerly Inc outside the submitted work. Dr F. Y. Lin reported receiving grants from GE Healthcare outside the submitted work. Dr Slomka reported software royalties from Cedars-Sinai Medical Center during the conduct of the study; grants from the NIH and Siemens AG outside the submitted work; and patents for US8885905B2 and WO2011069120A1 issued. Dr Dey reported receiving software royalties from Cedars-Sinai Medical Center and a patent outside the submitted work. Dr Berman reported a patent for software with royalties paid from Cedars-Sinai Medical Center. No other disclosures were reported.

Funding/Support: This trial was supported by grant HL115150 from the NIH and the Leading Foreign Research Institute Recruitment Program of the National Research Foundation of Korea, Ministry of Science, ICT and Future Planning (Seoul, Korea); by grants 1R01HL148787-01A1 and 1R01HL151266-01 from the NIH; and by the Dalio Institute of Cardiovascular Imaging and Miriam and Sheldon G. Adelson Medical Research Foundation.

Role of the Funder/Sponsor: The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

^✉

Corresponding author.

PMCID: PMC8792800 PMID: 35080587

Key Points

Question

Are plaque location and vessel geometry determined by coronary computed tomographic angiography (CCTA) associated with future acute coronary syndrome (ACS)–causing culprit lesions?

Findings

Of 548 coronary lesions in 116 patients with incident ACS after CCTA in this nested case-control study, lesions with a short distance from the ostium or located within vessel bifurcations or tortuous segments were more likely to develop into culprit lesions. A greater number of these adverse geometric characteristics were exhibited by culprit lesion precursors compared with the highest-grade stenosis lesions in 116 matched controls.

Meaning

These findings suggest that CCTA-derived adverse geometric characteristics of coronary plaques are associated with progression to ACS-causing culprit lesions.

This cohort study assesses the association of adverse geometric characteristics of coronary lesions with location and vessel geometry on coronary computed tomographic angiography.

Abstract

Importance

Distinct plaque locations and vessel geometric features predispose to altered coronary flow hemodynamics. The association between these lesion-level characteristics assessed by coronary computed tomographic angiography (CCTA) and risk of future acute coronary syndrome (ACS) is unknown.

Objective

To examine whether CCTA-derived adverse geometric characteristics (AGCs) of coronary lesions describing location and vessel geometry add to plaque morphology and burden for identifying culprit lesion precursors associated with future ACS.

Design, Setting, and Participants

This substudy of ICONIC (Incident Coronary Syndromes Identified by Computed Tomography), a multicenter nested case-control cohort study, included patients with ACS and a culprit lesion precursor identified on baseline CCTA (n = 116) and propensity score–matched non-ACS controls (n = 116). Data were collected from July 20, 2012, to April 30, 2017, and analyzed from October 1, 2020, to October 31, 2021.

Exposures

Coronary lesions were evaluated for the following 3 AGCs: (1) distance from the coronary ostium to lesion; (2) location at vessel bifurcations; and (3) vessel tortuosity, defined as the presence of 1 bend of greater than 90° or 3 curves of 45° to 90° using a 3-point angle within the lesion.

Main Outcomes and Measures

Association between lesion-level AGCs and risk of future ACS-causing culprit lesions.

Results

Of 548 lesions, 116 culprit lesion precursors were identified in 116 patients (80 [69.0%] men; mean [SD], age 62.7 [11.5] years). Compared with nonculprit lesions, culprit lesion precursors had a shorter distance from the ostium (median, 35.1 [IQR, 23.6-48.4] mm vs 44.5 [IQR, 28.2-70.8] mm), more frequently localized to bifurcations (85 [73.3%] vs 168 [38.9%]), and had more tortuous vessel segments (5 [4.3%] vs 6 [1.4%]; all P < .05). In multivariable Cox regression analysis, an increasing number of AGCs was associated with a greater risk of future culprit lesions (hazard ratio [HR] for 1 AGC, 2.90 [95% CI, 1.38-6.08]; P = .005; HR for ≥2 AGCs, 6.84 [95% CI, 3.33-14.04]; P < .001). Adverse geometric characteristics provided incremental discriminatory value for culprit lesion precursors when added to a model containing stenosis severity, adverse morphological plaque characteristics, and quantitative plaque characteristics (area under the curve, 0.766 [95% CI, 0.718-0.814] vs 0.733 [95% CI, 0.685-0.782]). In per-patient comparison, patients with ACS had a higher frequency of lesions with adverse plaque characteristics, AGCs, or both compared with control patients (≥2 adverse plaque characteristics, 70 [60.3%] vs 50 [43.1%]; ≥2 AGCs, 92 [79.3%] vs 60 [51.7%]; ≥2 of both, 37 [31.9%] vs 20 [17.2%]; all P < .05).

Conclusions and Relevance

These findings support the concept that CCTA-derived AGCs capturing lesion location and vessel geometry are associated with risk of future ACS-causing culprit lesions. Adverse geometric characteristics may provide additive prognostic information beyond plaque assessment in CCTA.

Introduction

Histopathologic and intracoronary imaging studies show acute coronary syndrome (ACS) to result from atherosclerotic plaque erosion or rupture, most frequently in the setting of thin-cap fibroatheromas.^1,2,3 Coronary computed tomographic angiography (CCTA) enables the noninvasive assessment of coronary atherosclerosis, with disruption-prone vulnerable plaques exhibiting qualitative morphological adverse plaque characteristics such as positive remodeling, low attenuation plaque, spotty calcification, and napkin-ring sign.^4,5,6,7 Furthermore, the burden of noncalcified plaque quantified from CCTA has been shown to be associated with patient-level and plaque-specific risk of future ACS.^8,9

The risk of plaque disruption is a function of both intrinsic plaque vulnerability and extrinsic hemodynamic forces at the plaque surface,¹⁰ and evidence suggests that distinct plaque locations and vessel geometric features predispose to altered coronary flow hemodynamics and endothelial shear stress.^{11,12,13,14,15} In addition to evaluation of plaque morphology and burden, CCTA permits the accurate determination of plaque distribution and vessel curvature. In this subanalysis of patients with incident ACS from the ICONIC (Incident Coronary Syndromes Identified by Computed Tomography) study, we sought to characterize adverse geometric characteristics (AGCs) of coronary lesions capturing location and vessel geometry on baseline CCTA. We hypothesized that AGCs are independently associated with future ACS-causing culprit lesions beyond current qualitative and quantitative plaque assessment in CCTA.

Methods

Study Population

This nested case-control study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. The ICONIC study¹⁶ was a nested case-control study within the prospective CONFIRM (Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter) registry of 23 251 consecutive patients undergoing baseline CCTA for suspected coronary artery disease. The ICONIC study design has been previously described,⁹ and each participating institution received local review board or ethics board approval. Written or oral patient informed consent or a waiver of consent was obtained according to site-specific regulations. Among 23 251 patients in the CONFIRM registry, 234 with ACS events were propensity matched 1:1 with controls with no events. Standardized demographical and clinical patient information were collected prospectively at each study site; race and ethnicity data were collected because coronary artery plaque characteristics and cardiovascular disease-associated morbidity and mortality vary widely across different ethnic groups. Among 234 patients with ACS, we excluded 118 in whom culprit lesion adjudication was not feasible for the following reasons: invasive coronary angiography was not performed at the time of ACS (n = 32); invasive coronary angiography images were not available (n = 26); no culprit lesion could be determined on invasive coronary angiography (n = 14); culprit lesions on invasive coronary angiography aligned with normal CCTA segments (n = 21); culprit lesions on CCTA could not be measured owing to artifact or small vessel size (n = 12); culprit lesions aligned to total occluded segments (n = 5); and CCTA images were not available for secondary review (n = 8) (eFigure 1 in the Supplement). The final study population included 116 patients with subsequent ACS and 116 matched non-ACS controls. Data were collected from July 20, 2012, to April 30, 2017.

ACS and Culprit Lesion Adjudication

Acute coronary syndrome events were adjudicated by the clinical and data coordinating center at the Dalio Institute of Cardiovascular Imaging, blinded to CCTA findings. Cardiac biomarker measurements and electrocardiograms were used to adjudicate ACS events.¹⁷ Invasive coronary angiography images at the time of ACS event were transmitted to independently masked readers at the core laboratory. The Society of Cardiovascular Computed Tomography 18-segment model¹⁸ was applied to invasive coronary angiography for the evaluation of coronary vessels to align with the same model used for CCTA analysis. Using the ROMICAT (Rule Out Myocardial Infarction by Computer Assisted Tomography) definition,¹⁹ a single lesion with significant stenosis was considered the culprit lesion. In patients with ACS with a single significant stenosis, this lesion was considered the culprit lesion. In patients with ACS with 2 or more significant lesions, the culprit lesion was defined using invasive coronary angiography and electrocardiographic findings.^19,20 All other lesions were deemed nonculprits.

CCTA Analysis

All CCTA images were acquired using at least 64-detector row scanners in accordance with Society of Cardiovascular Computed Tomography guidelines.⁹ Using CCTA analysis software Autoplaque, version 2.5 (Cedars-Sinai Medical Center), all coronary segments of 2 mm or more according to the 18-segment Society of Cardiovascular Computed Tomography model were evaluated by 2 level III experienced readers (D.H. and A.L.) blinded to clinical and invasive coronary angiography data. Diameter stenosis was visually graded according to the guidelines.²¹ Positive remodeling was defined by a vessel remodeling index of greater than 1.1.⁴ Low attenuation plaque was defined by the presence of any voxel of less than 30 Hounsfield units.²² Spotty calcification was defined as calcification of less than 3 mm in any direction within a plaque.⁷ Napkin-ring sign was defined as noncalcified plaque with a central area of hypoattenuation surrounded by a peripheral rim of hyperattenuation.⁶

Using the same software, all coronary lesions were evaluated for the following 3 AGCs: location at bifurcation, distance from ostium to lesion, and vessel tortuosity (eFigure 2 in the Supplement). Bifurcation was defined as the presence of a side branch origin within the lesion. Vessel tortuosity was defined as the presence of 1 bend of greater than 90° or 3 curves of 45° to 90° using a 3-point angle within the lesion (eFigure 3 in the Supplement).^9,23 Vessel angle measurements were repeated in 30 randomly selected lesions by 2 independent readers (D.H. and K.K.) to evaluate interobserver reproducibility. The intraclass correlation coefficient for lesion curve measurement was 0.91 (95% CI, 0.82-0.96; P < .001). The distance (in mm) from the coronary ostium to lesion (site of maximal diameter stenosis) was measured for each individual plaque. Using the median distance from coronary ostium to lesion as a cutoff, lesions were categorized as having a short (<39 mm for left coronary artery and <60 mm for right coronary artery) or long distance from the ostium for binary assessment.

The volumes of total plaque, calcified plaque (≥350 Hounsfield units), fibrous plaque (131-350 Hounsfield units), fibrofatty plaque (31-130 Hounsfield units), and necrotic core (−30 to 30 Hounsfield units) were quantified semiautomatically (MEDIS Qangio CT, version 2.1.9.1 [Medis Medical Imaging Systems]) as described previously.⁹ The respective plaque burdens were calculated as plaque volume divided by vessel volume of the lesion multiplied by 100 (in percentage).

Statistical Analysis

Data were analyzed from October 1, 2020, to October 31, 2021. At the patient level, 116 patients with ACS were compared with 116 matched controls. The distribution and mean propensity score were compared between patients and matched controls to ensure the matching remained balanced after exclusion of 118 pairs for the culprit lesion assessment. At the lesion level, 116 culprit lesion precursors were compared with (1) 432 nonculprit lesions in patients with ACS and (2) 116 highest-grade stenosis in control patients. Continuous variables are reported as mean (SD) or median (IQR), as appropriate. Categorical variables are reported as number (percentage). Comparisons of continuous variables and categorical variables between culprit lesion precursors and nonculprit lesions were performed using the paired or unpaired t test, Mann-Whitney test, or χ² test as appropriate. The number of adverse plaque characteristics was categorized as 0, 1, or 2 or more,²² and the number of AGCs was categorized as 0, 1, or 2 or more. Kaplan-Meier analysis with log rank test was used to assess the risk of future culprit lesions. Cox proportional hazards regression analysis was used to determine the risk of future culprit lesions, with adjustment for diameter stenosis, number of adverse plaque characteristics, and quantitative plaque characteristics, which included plaque area at the maximal stenosis section, minimum lumen area, total plaque volume, noncalcified plaque volume, and necrotic core volume. Shared frailty models were used in the Cox proportional hazards regression analysis to adjust for within-group dependence. For comparisons between patients with ACS and controls, clustered sandwich estimator models were used to account for the clustering within matched sets. Receiver operating characteristic curve analysis was applied to assess the incremental value of CCTA-determined plaque features for discriminating culprit lesion, using 3 nested models: (1) diameter stenosis plus adverse plaque characteristics; (2) diameter stenosis plus adverse plaque characteristics plus quantitative plaque characteristics; and (3) diameter stenosis plus adverse plaque characteristics plus quantitative plaque characteristics plus AGCs. Internal validation was performed by correcting for optimism using 1000 bootstrap resampling method.²⁴ The optimism-adjusted area under the receiver operating characteristic curve (AUC) was used to measure the discriminatory ability of the models for identifying culprit lesion precursors. Statistical analyses were performed using Stata, version 14 (StataCorp LLC). Two-sided P < .05 indicated statistical significance.

Results

Baseline Clinical Characteristics of Patients With ACS vs Controls

Baseline clinical and imaging characteristics between patients with ACS and controls are shown in Table 1 and eTable 1 in the Supplement. Of the 116 patients with ACS, mean (SD) age was 62.7 (11.5) years; 80 patients (69.0%) were men and 36 (31.0%) were women. The median time from CCTA to the first ACS event was 29 (IQR, 5-386) days. Twenty-one patients (18.1%) had ST-elevation myocardial infarction, 50 (43.1%) had non-ST elevation myocardial infarction, and 42 (36.2%) had unstable angina.

Table 1. Baseline Clinical and Imaging Characteristics Between Patients With ACS and Controls.

Characteristic	Patient group^a		P value
Characteristic	ACS (n = 116)	Controls (n = 116)	P value
Baseline clinical characteristics included in propensity score
Age, mean (SD), y	62.7 (11.5)	64.6 (9.7)	.19
Sex
Men	80 (69.0)	70 (68.3)	.87
Women	36 (31.0)	46 (39.7)	.87
Risk factors
Hypertension	72 (62.1)	66 (56.9)	.42
Diabetes	26 (22.4)	35 (30.2)	.18
Dyslipidemia	67 (57.8)	61 (52.6)	.39
Family history of premature CAD	49 (42.2)	38 (32.8)	.11
Current smoker	29 (25.0)	23 (19.8)	.34
CAD severity (site read)
Normal	5 (4.3)	9 (7.7)	.31
Nonobstructive	44 (37.9)	49 (42.2)
1-Vessel obstructive	19 (16.4)	24 (20.7)
2-Vessel obstructive	29 (25.0)	23 (19.8)
3-Vessel/left main	19 (16.4)	11 (9.5)
Propensity score, mean (SD)	0.08 (0.04)	0.08 (0.04)	.99
CCTA characteristics per patient
Stenosis, mean (SD), %
Diameter	45.4 (15.9)	35.6 (19.1)	<.001
Area	67.6 (18.0)	54.9 (25.7)	<.001
Minimum luminal, mean (SD)
Area, mm²	2.1 (1.5)	2.4 (1.7)	.07
Diameter, mm	1.3 (0.4)	1.4 (0.5)	.06
Total plaque volume, median (IQR), mm³	296.1 (118.3-480.7)	226.8 (85.4-407.1)	.13
Calcified	73.5 (21.4-181.0)	59.2 (11.3-155.6)	.91
Fibrous	131.8 (57.2-200.6)	92.8 (31.8-158.5)	.01
Fibrofatty	35.7 (12.2-82.7)	28.2 (4.2-58.6)	.02
Necrotic core	2.0 (0.1-7.3)	1.2 (0-5.1)	.12
Noncalcified	180.4 (73.3-306.9)	145.8 (57.2-231.6)	.008
Total plaque burden, median (IQR), %	11.1 (5.7-19.6)	10.4 (3.3-18.1)	.13
Calcified	3.7 (0.8-7.0)	2.7 (0.6-6.7)	.63
Fibrous	5.2 (2.5-9.1)	4.0 (1.2-7.2)	.02
Fibrofatty	1.5 (0.5-3.4)	1.2 (0.1-2.3)	.09
Necrotic core	0.1 (0-0.3)	0.1 (0-0.2)	.36
Noncalcified plaque	7.4 (3.3-15.6)	5.9 (2.1-9.7)	.03
Maximum cross-sectional plaque burden, mean (SD), %	70.8 (16.6)	59.7 (26.9)	<.001
Diffuseness, %	28.6 (15.7-42.9)	22.1 (9.8-35.5)	.003
No. of total lesions, mean (SD)	4.7 (2.4)	4.1 (2.5)	.06
Adverse plaque characteristics
Any positive remodeling present	109 (94.0)	95 (81.9)	.005
Any low attenuation plaque present	63 (54.3)	38 (32.7)	.001
Any spotty calcification present	40 (34.5)	24 (20.7)	.02
Any napkin-ring sign present	19 (16.4)	5 (4.3)	.003
Lesion with ≥2 APCs	70 (60.3)	50 (43.1)	.009
No. of lesions, mean (SD)
With APCs	3.8 (2.2)	3.1 (2.3)	.02
With ≥2 APCs	1.1 (1.2)	0.7 (0.1)	.009
Adverse geometric characteristics
Lesion with short distance from ostium	110 (94.8)	100 (86.2)	.03
Lesion at bifurcation	112 (96.5)	77 (66.4)	<.001
Lesion at tortuous segment	8 (6.9)	4 (3.4)	.24
Lesion with ≥2 AGCs	92 (79.3)	60 (51.7)	.04
No. of lesion, mean (SD)
With AGCs	3.4 (1.6)	2.9 (2.0)	.02
With ≥2 AGCs	1.2 (0.9)	0.8 (0.9)	.001
APCs and AGCs
Lesion with both APCs and AGCs	109 (94.0)	90 (77.6)	<.001
No. of lesions with both APCs and AGCs, mean (SD)	2.9 (1.5)	2.2 (1.7)	.003
Lesions with both ≥2 APCs and ≥2 AGCs	37 (31.9)	20 (17.2)	<.001
No. of lesions with both ≥2 APCs and ≥2 AGCs, mean (SD)	0.4 (0.6)	0.2 (0.5)	.03
CCTA characteristics per lesion^b
Diameter stenosis, %
1-24	18 (15.5)	32 (27.6)	.07
25-49	37 (31.9)	40 (34.5)
50-69	36 (31.0)	24 (20.7)
≥70	25 (21.5)	20 (17.2)
APCs
Positive remodeling	98 (84.5)	54 (46.5)	<.001
Low attenuation plaque	41 (35.3)	21 (18.1)	.003
Spotty calcification	21 (18.1)	8 (6.9)	.01
Napkin-ring sign	13 (11.2)	6 (5.2)	.09
AGCs
Lesion with short distance from ostium	74 (63.8)	48 (41.4)	.001
Lesion at bifurcation	85 (73.3)	40 (34.5)	<.001
Lesion at tortuous segment	5 (4.3)	3 (2.6)	.47
Quantitative plaque volume and burden, median (IQR)
Total plaque volume, mm³	94.9 (29.1-205.0)	21.5 (6.1-3.8)	<.001
Calcified volume	19.3 (2.2-78.6)	3.8 (0-24.8)	.02
Fibrous volume	35.2 (13.0-93.3)	7.0 (1.7-31.7)	<.001
Fibrofatty volume	9.5 (1.3-32.6)	1.7 (0-9.9)	.001
Necrotic core volume	0.1 (0-2.7)	0 (0-0.6)	.06
Noncalcified volume	42.4 (21.0-131.9)	10.6 (2.2-45.2)	<.001
Total plaque burden, %	31.8 (20.1-42.2)	24.5 (11.6-37.3)	.002
Calcified burden	8.1 (1.6-17.1)	6.8 (0-13.3)	.31
Fibrous burden	13.8 (7.9-19.2)	9.5 (2.8-15.5)	<.001
Fibrofatty burden	3.6 (0.6-9.2)	1.4 (0-6.7)	.008
Necrotic core burden	0.1 (0-0.6)	0 (0-0.4)	.02
Noncalcified plaque burden	20.3 (10.5-28.3)	13.9 (3.6-24.0)	.003

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Abbreviations: ACS, acute coronary syndrome; AGC, adverse geometric characteristic; APC, adverse plaque characteristic; CAD, coronary artery disease; CCTA, coronary computed tomography angiography.

^{^a}

Unless otherwise indicated, data are expressed as number (%) of patients.

^{^b}

Culprit lesions in patients with ACS vs maximum stenosed lesion in controls.

Baseline clinical characteristics used for propensity score matching did not differ significantly between the 116 patients with ACS and 116 matched controls (Table 1). The AUC of the propensity score model was 0.506 (95% CI, 0.432-0.581). There was no significant difference in mean propensity scores between included cases and controls (0.08 [0.04] vs 0.08 [0.04]; P = .91) (eFigure 4 in the Supplement).

CCTA-Based Plaque Characteristics in Culprit vs Nonculprit Lesions

Of the total 548 lesions in 116 patients, we identified 116 lesions as culprit lesion precursors associated with future ACS. Compared with 432 nonculprit lesions, culprit lesion precursors had a greater-diameter stenosis (eg, ≥70%, 25 [21.5%] vs 30 [6.9%]; P < .001 for trend) and a higher prevalence of all 4 adverse plaque characteristics (eg, any adverse plaque characteristic, 108 [93.1%] vs 331 [76.6%]; P < .001) (Table 2). Culprit lesion precursors exhibited higher median volumes and burdens of total plaque (94.9 [IQR, 29.1-205.0] vs 23.9 [IQR, 9.4-65.9] mm³ and 31.8% [IQR, 20.1%-42.2%] vs 19.9% [IQR, 11.9%-30.9%], respectively), calcified plaque (19.3 [IQR, 2.2-78.6] vs 5.8 [IQR, 1.3-19.7] mm³ and 8.1% [IQR, 1.6%-17.1%] vs 5.3% [IQR, 1.5%-12.0%], respectively), fibrous plaque (35.2 [IQR, 13.0-93.3] vs 11.1 [IQR, 4.6-29.5] mm³ and 13.8% [IQR, 7.9%-19.2%] vs 9.2% [IQR, 5.5%-14.3%], respectively), fibrofatty plaque (9.5 [IRQ, 1.3-32.6] vs 1.7 [IQR, 0.1-8.3] mm³ and 3.6% [IQR, 0.6%-9.2%] vs 1.2% [IQR, 0.1%-4.2%], respectively), and necrotic core (0.1 [IQR, 0-2.7] vs 0 [IQR, 0-0.3] mm³ and 0.1% [IQR, 0-0.6%] vs 0 [IQR, 0-0.1%], respectively) when compared with nonculprit lesions (all P < .05). With respect to AGCs, culprit lesion precursors had a shorter medium ostium to lesion distance (overall, 35.1 [IQR, 23.6-48.4] mm vs 44.5 [IQR, 28.2-70.8] mm [P < .001]; left coronary artery, 34.9 [IQR, 24.5-47.7] vs 39.9 [IQR, 26.1-54.5] mm [P = .02]; right coronary artery, 36.2 [IQR, 20-54.8] vs 65.9 [IQR, 30.4-103.8] mm [P < .001]) and more frequently localized to bifurcations (85 [73.3%] vs 168 [38.9%]) or tortuous vessel segments (5 [4.3%] vs 6 [1.4%]) (both P < .05) compared with nonculprit lesions.

Table 2. CCTA Characteristics of Culprit and Nonculprit Lesions in Patients With ACS.

Characteristic	Lesion^a		P value
Characteristic	Culprit (n = 116)	Nonculprit (n = 432)	P value
Stenosis grade
Diameter stenosis, %
1-24	18 (15.5)	164 (38.0)	<.001
25-49	37 (31.9)	179 (41.4)
50-69	36 (31.0)	59 (13.7)
≥70	25 (21.5)	30 (6.9)
Coronary vessel distribution
Left anterior descending	64 (55.2)	161 (37.3)	.002
Left circumflex	19 (16.4)	114 (26.4)
Right coronary artery	33 (28.4)	157 (36.3)
Adverse plaque characteristics
Positive remodeling	98 (84.5)	317 (73.4)	.01
Low attenuation plaque	41 (35.3)	56 (13.0)	<.001
Spotty calcification	21 (18.1)	46 (10.6)	.03
Napkin-ring sign	13 (11.2)	7 (1.6)	<.001
Any APC	108 (93.1)	331 (76.6)	<.001
≥2 APCs	45 (38.8)	78 (18.1)	<.001
AGCs
Distance from ostium, median (IQR), mm	35.1 (23.6-48.4)	44.5 (28.2-70.8)	<.001
Short distance	74 (63.8)	198 (45.8)	.001
Bifurcation	85 (73.3)	168 (38.9)	<.001
Tortuosity	5 (4.3)	6 (1.4)	.046
Any AGC	106 (91.4)	283 (65.5)	<.001
≥2 AGCs	59 (50.9)	90 (20.8)	<.001
Both APCs and AGCs	98 (84.5)	222 (51.4)	<.001
≥2 APCs and ≥2 AGCs	27 (23.3)	21 (4.9)	<.001
Quantitative plaque volume and burden, median (IQR)
Total plaque volume, mm³	94.9 (29.1-205.0)	23.9 (9.4-65.9)	<.001
Calcified	19.3 (2.2-78.6)	5.8 (1.3-19.7)	<.001
Fibrous	35.2 (13.0-93.3)	11.1 (4.6-29.5)	<.001
Fibrofatty	9.5 (1.3-32.6)	1.7 (0.1-8.3)	<.001
Necrotic core	0.1 (0-2.7)	0 (0-0.3)	<.001
Noncalcified	42.4 (21.0-131.9)	14.7 (5.5-40.5)	<.001
Total plaque burden, %	31.8 (20.1-42.2)	19.9 (11.9-30.9)	<.001
Calcified	8.1 (1.6-17.1)	5.3 (1.5-12.0)	.04
Fibrous	13.8 (7.9-19.2)	9.2 (5.5-14.3)	<.001
Fibrofatty	3.6 (0.6-9.2)	1.2 (0.1-4.2)	<.001
Necrotic core	0.1 (0-0.6)	0 (0-0.1)	<.001
Noncalcified plaque	20.3 (10.5-28.3)	11.7 (6.4-19.6)	<.001

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Abbreviations: ACS, acute coronary syndrome; AGC, adverse geometric characteristics; APC, adverse plaque characteristics; CCTA, coronary computed tomography angiography.

^{^a}

Unless otherwise indicated, data are expressed as number (%) of lesions.

Association of AGCs With Future Culprit Lesions

An increasing number of AGCs was associated a higher prevalence of low attenuation plaque (0 AGCs, 13 of 151 [8.6%]; 1 AGC, 46 of 262 [17.5%]; ≥2 AGCs, 29 of 135 [21.5%]; P = .008) and napkin-ring sign (0 AGCs, 0; 1 AGC, 9 of 262 [3.4%]; ≥2 AGCs, 11 of 135 [8.1%]; P = .001) as well as a trend toward a greater-diameter stenosis (eg, ≥70%: 0 AGCs, 12 of 151 [7.9%]; 1 AGC, 25 of 262 [9.5%]; ≥2 AGCs, 18 of 135 [13.3%]; P = .07) (eTable 2 in the Supplement). Lesions without AGCs had a lower prevalence of positive remodeling (117 of 151 [77.5%]) and spotty calcification (16 of 151 [10.6%]) compared with lesions with 1 (215 of 262 [82.1%] and 43 of 262 [16.0%], respectively) or at least 2 AGCs (109 of 135 [80.7%] and 20 of 135 [14.8%], respectively). There was a graded increase in the quantitative volumes and burdens of all plaque components with an increasing number of AGCs (eg, total plaque volume, 15.5 [IQR, 6.9-30.8] vs 88.3 [IQR, 26.2-154.9] mm³; P < .001).

The prevalence of culprit lesion precursors increased with the number of AGCs, a trend that was more pronounced with an increasing frequency of adverse plaque characteristics (Figure 1). Among 40 lesions containing neither adverse plaque characteristics nor AGCs, none progressed to future culprit lesions. By contrast, more than half (27 of 48 [56.3%]) of lesions with at least 2 adverse plaque characteristics and at least 2 AGCs were culprit lesion precursors. On Kaplan-Meier analysis, the cumulative risk of plaques developing into culprit lesions increased with the number of AGCs (log-rank P < .001) (Figure 2A). Plaques exhibiting both adverse plaque characteristics and AGCs had the greatest probability of becoming culprit lesions compared to plaques with either adverse plaque characteristics or AGCs or plaques with no adverse plaque characteristics or AGCs (log-rank P < .001) (Figure 2B).

Figure 1. — The prevalence of culprit lesion precursors increased with an increasing number of APCs and AGCs. Lesions containing neither APCs nor AGCs did not develop into culprit lesions.

^aP < .001 compared with 0 AGCs.

Figure 2. — A, The risk of plaques developing into culprit lesions increased with the number of AGCs. B, Plaques with both APCs and AGCs had the greatest probability of becoming culprit lesions compared to plaques with either APCs or AGCs alone, or plaques with neither APCs nor AGCs.

In multivariable Cox proportional hazards regression analysis, AGCs were independently associated with increased risk of future culprit lesions after adjustment for diameter stenosis, number of adverse plaque characteristics, and quantitative plaque characteristics (eTable 3 in the Supplement). Compared with nonculprit lesions, culprit lesion precursors had an elevated hazard for a short distance from the ostium (hazard ratio [HR], 2.71 [95% CI, 1.73-4.24]; P < .001), bifurcation location (HR, 2.14 [95% CI, 1.39-3.29]; P < .001), and tortuosity (HR, 2.31 [95% CI, 1.01-5.27]; P = .04). An increasing number of AGCs was associated with greater risk of future culprit lesions, with 1 and at least 2 AGCs conferring a 2.9-fold (HR, 2.90 [95% CI, 1.38-6.08]; P = .005) and 7-fold (HR, 6.84 [95% CI, 3.33-14.04]; P < .001) risk, respectively.

On receiver operating characteristic curve analysis, a model containing diameter stenosis, adverse plaque characteristics, and quantitative plaque characteristics (model 2) had a higher AUC for discriminating culprit lesion precursors compared with a model with diameter stenosis and adverse plaque characteristics (model 1) (AUC, 0.733 [95% CI, 0.685-0.782] vs 0.712 [95% CI, 0.662-0.766]). The addition of AGCs to model 2 provided further incremental discriminatory value (AUC, 0.766 [95% CI, 0.718-0.814]) (Figure 3).

Figure 3. — Receiver operator characteristic curves show the discriminatory value of plaque characteristics for identifying future culprit lesions. The addition of adverse geometric characteristics (AGCs; including positive remodeling, low attenuation plaque, spotty calcification, and napkin-ring sign) to a model containing diameter stenosis (DS), adverse plaque characteristics (APCs; including ostium to lesion distance, bifurcation, and tortuosity), and quantitative plaque characteristics (QPCs; including plaque area at maximal stenosis section, minimum lumen area, total plaque, noncalcified plaque, and necrotic core volume) yielded a higher area under the curve (AUC).

Adverse Geometric Characteristics in Patients With ACS vs Controls

Compared with controls, patients with ACS had a higher diameter and area of stenosis and noncalcified plaque volume and burden (Table 1). Total plaque volume and calcified plaque volume did not differ between groups. Lesions in patients with ACS more frequently exhibited adverse plaque characteristics (eg, positive remodeling, 109 [94.0%] vs 95 [81.9%]; P = .005), a short distance from ostium (110 [94.8%] vs 100 [86.2%]; P = .03), bifurcation location (112 [96.5%] vs 77 [66.4%]; P < .001), or a combination of both adverse plaque characteristics and AGCs (109 [94.0%] vs 90 [77.6%]; P < .001) compared with lesions in controls. The prevalence of lesions in tortuous segments did not differ significantly between patients with ACS and controls (8 [6.9%] vs 4 [3.4%]; P = .24). The total mean number of lesions with any AGC (3.4 [1.6] vs 2.9 [2.0]; P = .02) or at least 2 AGCs (1.2 [0.9] vs 0.8 [0.9]; P = .001) was also significantly higher in patients with ACS than in controls. Patients with ACS had a higher frequency of lesions with adverse plaque characteristics, AGCs, or both compared with control patients (≥2 adverse plaque characteristics, 70 [60.3%] vs 50 [43.1%]; ≥2 AGCs, 92 [79.3%] vs 60 [51.7%]; ≥2 of both, 37 [31.9%] vs 20 [17.2%]; all P < .05).

Compared with the highest-grade stenosis lesion in controls, culprit lesion precursors exhibited a higher prevalence of adverse plaque characteristics (eg, positive remodeling, 98 [84.5%] vs 317 [73.4%]; P = .01) and greater total and noncalcified plaque volumes and burdens (total plaque volume and burden, 94.9 [IQR, 29.1-205.0] vs 21.5 [IQR, 6.1-3.8] mm³ and 31.8% [IQR, 20.1%- 42.2%] vs 24.5% [IQR, 11.6%-37.3%], respectively; noncalcified plaque volume and burden, 42.4 [IQR, 21.0-131.9] vs 10.6 [IQR, 2.2-45.2] mm³ and 20.3% [IQR, 10.5%-28.3%] vs 13.9% [IQR, 3.6%-24.0%], respectively; all P < .01) (Table 1). Culprit precursor lesions more frequently had a short distance from the ostium (74 [63.8%] vs 48 [41.4%]; P = .001) and localized to bifurcations (85 [73.3%] vs 40 [34.5%]; P < .001). The prevalence of vessel tortuosity did not differ significantly between culprit lesion precursors and the highest-grade stenosis control lesions (5 [4.3%] vs 3 [2.6%]; P = .47). In multivariable Cox proportional hazards regression, a short distance from the ostium (HR, 1.55 [95% CI, 1.02-2.33]; P = .03) and bifurcation location (HR, 2.63 [95% CI, 1.77-3.92]; P < .001) conferred an increased risk of future culprit lesions (eTable 4 in the Supplement). An increasing number of AGCs was associated with greater risk of future culprit lesions, with 1 and at least 2 AGCs conferring a 2.5-fold (HR, 2.53 [95% CI, 1.24-5.15]; P = .01) and 4.4-fold (HR, 4.43 [95% CI, 2.17-9.05]; P < .001) risk, respectively.

Discussion

This study examined the association of CCTA-derived AGCs (plaque location and vessel geometry) with culprit lesion precursors. Our primary findings included the following: (1) plaques located at bifurcations, tortuous vessel segments, or closer to the coronary ostium had a greater risk of developing into culprit lesions, independently of diameter stenosis, adverse plaque characteristics, and quantitative plaque characteristics; (2) among plaques without adverse plaque characteristics and AGCs (n = 40), none developed into culprit lesions; and (3) AGCs provided incremental discriminatory value for identifying culprit lesion precursors when added to a model containing diameter stenosis, adverse plaque characteristics, and quantitative plaque characteristics.

Prior reports demonstrating the predictive value of CCTA-based coronary plaque assessment for subsequent ACS have focused on morphological plaque characteristics.^5,7,8,9,25 It is well established that CCTA-verified qualitative adverse plaque characteristics confer an increased risk of future coronary events.^5,7,26 Beyond this, quantitative plaque measurements have independent predictive value for subsequent ACS in patients with stable coronary artery disease.^8,9 The recent ICONIC study showed quantitative plaque burden and composition to be associated with future ACS at both the per-patient and per-lesion level^9,27; however, the potential influence of plaque location and vessel geometry on outcomes was not examined. Coronary computed tomographic angiography enables precise 3-dimensional reconstruction of the entire coronary tree and hence the accurate and reproducible assessment of vessel angulation, tortuosity, and bifurcation points.^28,29 Although patient-based prognostic CCTA indexes such as the computed tomography–Leaman score³⁰ and modified Duke coronary artery disease index³¹ assign greater weighting to lesions in proximal coronary segments, they do not incorporate geometric vessel features. We demonstrate that assessment of plaque location and vessel geometry provides incremental value for identifying future ACS-causing culprit lesions over and above plaque morphological features and quantitative plaque characteristics. Although prior reports have examined AGCs at the time of ACS^31,32,33 or identified adverse plaque characteristics from antecedent CCTA scans in patients with ACS, our study is the first, to our knowledge, to demonstrate the prognostic significance of both AGCs and adverse plaque characteristics in patients with stable disease who experience future ACS.

Mechanistically, thrombotic occlusions leading to ACS have a propensity to occur in the proximal third of the coronary arteries as demonstrated in invasive coronary imaging studies.^32,34 Plaque distance from the coronary ostium has been shown to be independently associated with plaque lipid content^33,35 and plaque rupture.³⁶ Moreover, the presence of angulation on a lesion and bifurcations occurring before or after a lesion confer an increased risk of plaque rupture.^34,36 Coronary plaques undergo constant evolution, including subclinical plaque rupture and healing, and the surrounding arterial wall also undergoes constant remodeling.¹ These AGCs affect local hemodynamic factors such as wall shear stress (WSS) that play a pivotal role in coronary plaque progression and subsequent plaque disruption.^37,38,39,40 Several experimental and histopathologic studies have shown that coronary atherogenesis preferentially involves the outer walls of vessel bifurcations and inner walls of regions of high curvature in the coronary arterial tree.^41,42 In these geometrically predisposed locations, WSS is significantly lower and exhibits directional changes and flow separation, features absent from regions of the coronary tree that are generally spared from atherosclerosis.^43,44 Furthermore, increased WSS and axial stress induced by the pressure gradient across a stenosis act directly on plaques to increase their risk of rupture.⁴⁵ Thus, biomechanical stresses are closely associated with plaque location and vessel geometry. Coronary computed tomographic angiography–derived AGCs as proposed by the present study represent simple anatomical parameters that may capture the effect of local hemodynamic forces in noninvasive plaque-based risk assessment.

Although recent advances in CCTA permit the modeling of computational fluid dynamics and calculation of hemodynamic parameters such as WSS,^46,47 such techniques require specialized software and are computationally expensive, limiting their clinical application. Herein, we present 3 AGCs that can be readily obtained from CCTA images using a combination of visual assessment and simple distance and angle measurements. We show these AGCs to provide incremental and complementary prognostic information to standard clinical CCTA evaluation of stenosis severity and adverse plaque characteristics. Hence, comprehensive plaque assessment, including lesion location and vessel geometry, may enhance the ability of CCTA to identify future ACS-causing culprit lesions and risk stratify patients with stable coronary artery disease.

Limitations

This study has several limitations. The post hoc nested case-control design of the ICONIC study may be susceptible to selection bias as well as unmeasured confounding factors, which may limit the generalizability of our findings. Future studies will need to externally validate these findings in larger, unselected cohorts. We did not assess emerging software-based hemodynamic parameters such as WSS, and hence were unable to directly explore the association between biomechanical stresses and AGCs in the present analysis. The low prevalence of lesions located in tortuous vessel segments (<5%) suggests that our study may have been underpowered to detect any significant between-patient differences for this AGC. Patients who died without a confirmatory finding of ACS were excluded from the ICONIC study, and thus our findings are limited to culprit lesions associated with nonfatal ACS. The extent of missing data in this analysis should be noted. Among 234 patients with ACS and 234 controls in the ICONIC study, we only included 116 patients with ACS with identifiable culprit lesion precursors and their 116 paired controls. This may have resulted in potential selection bias and unbalanced propensity matching in the remaining case-control pairs. Although clinical characteristics did not differ significantly between included vs excluded patients, included patients had significantly higher CCTA-derived volumes and burdens of all plaque components and a higher degree of stenosis compared to excluded patients (eTable 5 in the Supplement). Thus, the present study findings might be limited to patients with high cardiovascular risk and extensive coronary atherosclerosis at baseline. Further studies are required to confirm the association of AGCs with future ACS risk in low- and intermediate-risk populations.

Conclusions

The findings of this study support the concept that CCTA-derived AGCs capturing lesion location and vessel geometry are associated with the risk of future ACS-causing culprit lesions. Adverse geometric characteristics can be obtained from standard CCTA images and may provide additive prognostic information beyond plaque assessment in CCTA.

Supplement.

eTable 1. Additional Patient Characteristics Between ACS vs Control

eTable 2. CCTA Lesion Characteristics According to the Number of AGCs in Patients With ACS

eTable 3. Univariable and Multivariable Cox Proportional Hazards Regression Analysis of AGCs for Risk of Culprit Lesions

eTable 4. Multivariable Cox Proportional Hazards Regression Analysis of AGCs for Risk of Culprit Lesions (Patients With ACS vs Controls)

eTable 5. Clinical and CCTA Characteristics in Analyzed (n = 116) and Excluded (n=118) Patients With ACS From the ICONIC Study

eFigure 1. Patient Selection and Study Design

eFigure 2. Illustrations of Adverse Geometric Characteristics

eFigure 3. Lesion Curve Assessment Using Semiautomated Software

eFigure 4. Propensity Score Distribution of the 116 Case and 116 Control Pairs

Click here for additional data file.^{(525.7KB, pdf)}

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24.Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361-387. doi: [DOI] [PubMed] [Google Scholar]
25.Motoyama S, Ito H, Sarai M, et al. Plaque characterization by coronary computed tomography angiography and the likelihood of acute coronary events in mid-term follow-up. J Am Coll Cardiol. 2015;66(4):337-346. doi: 10.1016/j.jacc.2015.05.069 [DOI] [PubMed] [Google Scholar]
26.Williams MC, Moss AJ, Dweck M, et al. Coronary artery plaque characteristics associated with adverse outcomes in the SCOT-HEART Study. J Am Coll Cardiol. 2019;73(3):291-301. doi: 10.1016/j.jacc.2018.10.066 [DOI] [PMC free article] [PubMed] [Google Scholar]
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46.Han D, Starikov A, Ó Hartaigh B, et al. Relationship between endothelial wall shear stress and high-risk atherosclerotic plaque characteristics for identification of coronary lesions that cause ischemia: a direct comparison with fractional flow reserve. J Am Heart Assoc. 2016;5(12):e004186. doi: 10.1161/JAHA.116.004186 [DOI] [PMC free article] [PubMed] [Google Scholar]
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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement.

eTable 1. Additional Patient Characteristics Between ACS vs Control

eTable 2. CCTA Lesion Characteristics According to the Number of AGCs in Patients With ACS

eTable 3. Univariable and Multivariable Cox Proportional Hazards Regression Analysis of AGCs for Risk of Culprit Lesions

eTable 4. Multivariable Cox Proportional Hazards Regression Analysis of AGCs for Risk of Culprit Lesions (Patients With ACS vs Controls)

eTable 5. Clinical and CCTA Characteristics in Analyzed (n = 116) and Excluded (n=118) Patients With ACS From the ICONIC Study

eFigure 1. Patient Selection and Study Design

eFigure 2. Illustrations of Adverse Geometric Characteristics

eFigure 3. Lesion Curve Assessment Using Semiautomated Software

eFigure 4. Propensity Score Distribution of the 116 Case and 116 Control Pairs

Click here for additional data file.^{(525.7KB, pdf)}

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[hoi210090r46] 46.Han D, Starikov A, Ó Hartaigh B, et al. Relationship between endothelial wall shear stress and high-risk atherosclerotic plaque characteristics for identification of coronary lesions that cause ischemia: a direct comparison with fractional flow reserve. J Am Heart Assoc. 2016;5(12):e004186. doi: 10.1161/JAHA.116.004186 [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Association of Plaque Location and Vessel Geometry Determined by Coronary Computed Tomographic Angiography With Future Acute Coronary Syndrome–Causing Culprit Lesions

Donghee Han, MD

Andrew Lin, MBBS, PhD

Keiichiro Kuronuma, MD

Evangelos Tzolos, MD

Alan C Kwan, MD

Eyal Klein, MD

Daniele Andreini, MD, PhD

Jeroen J Bax, MD, PhD

Filippo Cademartiri, MD, PhD

Kavitha Chinnaiyan, MD

Benjamin J W Chow, MD

Edoardo Conte, MD

Ricardo C Cury, MD

Gudrun Feuchtner, MD

Martin Hadamitzky, MD

Yong-Jin Kim, MD

Jonathon A Leipsic, MD

Erica Maffei, MD

Hugo Marques, MD

Fabian Plank, MD

Gianluca Pontone, MD, PhD

Todd C Villines, MD

Mouaz H Al-Mallah, MD

Pedro de Araújo Gonçalves, MD, PhD

Ibrahim Danad, MD

Heidi Gransar, MSc

Yao Lu, MSc

Ji-Hyun Lee, MD

Sang-Eun Lee, MD, PhD

Lohendran Baskaran, MD

Subhi J Al’Aref, MD

Yeonyee E Yoon, MD, PhD

Alexander Van Rosendael, MD

Matthew J Budoff, MD

Habib Samady, MD

Peter H Stone, MD

Renu Virmani, MD

Stephan Achenbach, MD

Jagat Narula, MD, PhD

Hyuk-Jae Chang, MD, PhD

James K Min, MD

Fay Y Lin, MD

Leslee J Shaw, PhD

Piotr J Slomka, PhD

Damini Dey, PhD

Daniel S Berman, MD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Exposures

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Study Population

ACS and Culprit Lesion Adjudication

CCTA Analysis

Statistical Analysis

Results

Baseline Clinical Characteristics of Patients With ACS vs Controls

Table 1. Baseline Clinical and Imaging Characteristics Between Patients With ACS and Controls.

CCTA-Based Plaque Characteristics in Culprit vs Nonculprit Lesions

Table 2. CCTA Characteristics of Culprit and Nonculprit Lesions in Patients With ACS.

Association of AGCs With Future Culprit Lesions

Figure 1. Frequency of Culprit Lesions According to Number of Adverse Plaque Characteristics (APCs) and Adverse Geometric Characteristics (AGCs).

Figure 2. Kaplan-Meier Curve for Culprit Lesion Risk According to Number of Adverse Geometric Characteristics (AGCs) and Presence of Adverse Plaque Characteristics (APCs) and/or AGCs.

Figure 3. Performance of Plaque Characteristics for Identifying Future Culprit Lesions.

Adverse Geometric Characteristics in Patients With ACS vs Controls

Discussion

Limitations

Conclusions

References