Carotid plaque thickness and carotid plaque burden predict future cardiovascular events in asymptomatic adult Americans

Carotid plaque thickness and carotid plaque burden predict future cardiovascular events in... Abstract Introduction Prediction of cardiovascular events improves using imaging, i.e. coronary calcium score and ultrasound assessment of carotid plaque. This study analysed the predictive value of two ultrasound measures of carotid plaque size: carotid plaque thickness and carotid and intima–media thickness (IMT). Methods and results A total of 6102 asymptomatic persons underwent assessment of conventional risk factors and imaging by carotid ultrasound. Carotid plaque burden (cPB) and maximum carotid plaque thickness (cPTmax) were measured from ‘cross-sectional sweep’ video acquisition of the carotid artery. IMT was measured from distal common carotid artery images. All participants were followed up for ∼3 years, and major cardiovascular events (MACE) were collected and adjudicated. All data were available for 5808 participants, in whom 216 first MACE events were observed. Increasing both cPB and cPTmax were associated with increasing the risk of future MACE when compared with participants without carotid atherosclerosis. Fully adjusted for risk factors, hazard ratios for cPTmax were 1.96 [95% confidence interval (CI) 0.91–4.25, P = 0.015] for primary MACE and 3.13 (95% CI 1.80–5.51, P < 0.001) for secondary MACE, similar to that of cPB. IMT did not improve risk prediction significantly. Non-categorical net reclassification index (NRI) for cPTmax was 0.178 (95% CI 0.027–0.299, P = 0.032) for primary MACE and 0.173 (95% CI 0.109–0.243, P < 0.001) for secondary MACE, which is almost similar to cPB. IMT assessment did not result in significant NRI. Conclusion The simpler cPTmax predicted cardiovascular events similarly to the more comprehensive cPB, whereas IMT did not. Awaiting true 3D ultrasound technology cPTmax may be a simple useful measure for prediction of future ASCVD. carotid ultrasound, carotid plaque, IMT, prediction of cardiovascular events Introduction Despite advances in treatment for atherosclerotic cardiovascular disease (ASCVD), atherosclerosis and its complications remain the leading cause of morbidity and mortality, being the source of the greatest health care costs in the Western world. Although the underlying pathogenesis of atherosclerosis is well understood, predicting who will become affected and suffer clinical disease is not, despite much knowledge about risk factors. In fact, risk prediction derived from risk factors for ASCVD has been shown to perform rather poorly,1 probably because individuals have different tolerance to lifestyle, cholesterol values and so on. Furthermore, health checks did not reduce mortality from ASCVD,2 and individual risk prediction from risk factors for atherosclerosis followed by individual lifestyle counselling has not affected mortality and morbidity.3 An alternative approach for predicting symptomatic ASCVD is based on identifying subclinical atherosclerosis in presumably healthy people. The underlying hypothesis is that without atherosclerosis in the main arteries, the risk of ASCVD is minimal, and vice versa. Several methods for the assessment of asymptomatic atherosclerosis exist and most are based on the fact that atherosclerosis is a generalized disease of the arterial tree.4–7 The most studied methods include coronary artery calcium score (CACS) and carotid ultrasound, the latter mostly used for measuring intima–media thickness (IMT) and lately for assessing carotid plaque. CACS has been documented to predict future coronary and other cardiovascular events for the individual person much better than risk factor-based scoring systems.6–10 The drawbacks of this method include the use of radiation, the relative poor mobility of computed tomography (CT) scanners and that it identifies atherosclerosis at a relatively late stage.11 IMT, in comparison with CACS, has been shown to be a rather weak predictor of future events for groups of people, whereas the value for the individual seems questionable.7,12,13 On the other hand, using ultrasound for the assessment of carotid plaque seems a much stronger predictor than IMT and has recently been shown to have similar predictive value as CACS.10 Moreover, ultrasound, in contrast to CT scanning, is harmless, mobile, and less expensive and may identify atherosclerosis at an early stage. We recently reported that carotid plaque burden (cPB), derived from carotid ultrasound, was similarly predictive as CACS for the development of future cardiovascular events.10 Although cPB is a comprehensive, offline assessment of all carotid plaque throughout the carotid artery, maximum carotid plaque thickness (cPTmax) is a simple measure that in principle can be performed during examination. This study reports the predictive value of cPTmax, carotid IMT, and cPB, all investigated in the High Risk Plaque BioImage Study. Methods The High Risk Plaque BioImage Study has previously been described in detail14 and was a prospective study evaluating cross-sectional associations betwwen imaging and circulating biomarkers and their ability to predict near-term atherothrombotic events (3-year) in asymptomatic subjects (https://clinicaltrials.gov/ct2/show/NCT00738725?term=Bioimage+study&rank=1, NCT00738725). Materials Between January 2008 and June 2009, the BioImage Study enrolled 7687 asymptomatic men aged 55–80 years and women aged 60–80 years who were members of the Humana Health System and residents of the Chicago, IL, USA, or Fort Lauderdale, FL, USA, metropolitan areas. Of these, 6102 subjects entered the imaging arm of the study. Subject eligibility, including freedom from previous history of cardiovascular disease [myocardial infarction (MI), stroke, angina, heart failure, arterial revascularization], was ascertained by baseline review of administrative claims data, followed by telephone interview, and finally by in-person baseline examination and interview. Participants were additionally required to be free of active cancer treatment, any medical condition precluding long-term participation or inability to complete 3-year follow-up, and have no language barrier or inability to comply with study procedures. The BioImage Study was approved by the Western Institutional Review Board, Olympia, WA, USA. Before enrolment, all study participants provided written informed consent and Health Insurance Portability and Accountability Act authorization. Baseline examinations A non-fasting venous blood sample was processed for routine chemistry tests, including serum creatinine and lipid levels. Diabetes mellitus was defined as current use of oral hypoglycaemic agents, insulin, or self-report of the diagnosis. Hypertension was defined as systolic blood pressure > 140 mmHg, diastolic blood pressure > 90 mmHg, or current use of antihypertensive medication. Current smoking status was self-reported. Acquisition of ultrasound data Details regarding ultrasound examination in the BioImage Study were previously published.4 In brief, Philips iU22 ultrasound systems (Philips Healthcare, Bothell, WA, USA) equipped with L12-5 and L9-3 transducers were used for all carotid studies. The scanning protocol included standard imaging of the carotid artery and its branches using generally accepted Doppler criteria for assessment of any degree of stenosis.15 Measurement of IMT was performed offline in the core laboratory from a 10-s video clip of the distal common carotid artery (CCA) recorded from the lateral aspect of the neck in long axis, ensuring the CCA was parallel to the transducer surface (horizontal in the image). For assessment of plaque thickness and plaque burden, the carotid artery was scanned cross-sectionally, slowly moving the transducer manually in the cranial direction from the proximal CCA into the distal internal carotid artery, at an angle perpendicular to the neck. The resulting 10-s digital video clip of this ‘manual 3D’ cross-sectional sweep was examined offline in the core ultrasound laboratory for quantification of plaque. Assessment of IMT, cPB, and cPTmax Ultrasound scans were read by the core laboratory at the Department of Vascular Surgery, Rigshospitalet, University of Copenhagen, Denmark, after all ultrasound data had been acquired. Measurement of IMT was performed with Philips QLAB IMT® plug-in, using the 10-s video clips mentioned above. The reader selected frames with good perpendicular alignment and image quality and adjusted IMT box position if necessary to ensure measurement of mean IMT over the distal 10 mm of the far wall of the CCA. For every participant, 5–10 mean IMT measurements were taken at the same phase of the cardiac cycle (diastole, electrocardiography gated) on each artery (right/left) for every participant. IMT measurements from both arteries were averaged to create an IMT score. Carotid plaque was defined as a focal structure encroaching into the arterial lumen of at least 0.5 mm; or 50% of the surrounding IMT value; or demonstrating a thickness > 1.5 mm, as measured from the media–adventitia interface to the intima–lumen interface.16,17 Assessment of plaque thickness and plaque burden was performed using Philips QLAB quantification software, which was enhanced with specially developed, semi-automated plaque analysis software, QLAB-VPQ® (Vascular Plaque Quantification) (Figure 1). The recorded 10-s cross-sectional sweeps were reviewed for the presence of plaque. Each image showing plaque was outlined as shown in Figure 1. Plaque areas from all images in the cross-sectional sweeps from both the right and left carotid arteries were summed as cPB, a quantitative metric of the total plaque area (mm2) across the length of the visualized carotid artery.4 From the outlined plaque images QLAB–VPQ automatically calculated carotid plaque thickness (cPT), being the radial distance from media/adventitia border to the centre of the vessel (Figure 1). The outlined image with the greatest thickness of the plaque from either side (right and left carotid artery) was used as plaque thickness (cPTmax). Figure 1 View largeDownload slide Segment of carotid artery with a plaque (orange), which is scanned with a linear array transducer as a series of image slices in transverse section (top). Each image is analysed with semi‐automated software to quantify plaque area, plaque greyscale statistics, percent stenosis, and other metrics of interest. The lower left ultrasound image shows the common carotid artery when no plaque is present. The blue border represents the lumen/intima border; the red border represents the media–adventitia boundary. When plaque is present, the yellow line represents lumen/plaque border. Right ultrasound image shows the common carotid artery when plaque is present. The red and blue borders are the same as in previous image, but the orange border represents the boundary of the plaque. cPT is indicated by the light green line. Figure 1 View largeDownload slide Segment of carotid artery with a plaque (orange), which is scanned with a linear array transducer as a series of image slices in transverse section (top). Each image is analysed with semi‐automated software to quantify plaque area, plaque greyscale statistics, percent stenosis, and other metrics of interest. The lower left ultrasound image shows the common carotid artery when no plaque is present. The blue border represents the lumen/intima border; the red border represents the media–adventitia boundary. When plaque is present, the yellow line represents lumen/plaque border. Right ultrasound image shows the common carotid artery when plaque is present. The red and blue borders are the same as in previous image, but the orange border represents the boundary of the plaque. cPT is indicated by the light green line. End points The identification and adjudication of end points have previously been described.10 An independent clinical events committee used source medical records to adjudicate non-fatal and fatal events. Myocardial infarction (MI) was defined according to the 2007 Universal Definition.18 Unstable angina was defined according to the Braunwald classification.19,20 Stroke was defined as a sudden focal neurological deficit of cerebrovascular aetiology persisting beyond 24 h and not due to another identifiable cause, such as a tumour or seizure, or as a clinically relevant new lesion detected on CT or magnetic resonance imaging.21 Deaths were classified as cardiovascular or non-cardiovascular. The primary end point included cardiovascular death, MI, or ischaemic stroke [major adverse cardiovascular events (MACE)]. The secondary MACE end point comprised all-cause death, MI, ischaemic stroke, unstable angina, or coronary revascularization. Statistics Baseline characteristics are presented as mean and standard deviation for continuous variables and number and percentage for categorical variables. Differences in baseline characteristics were compared across cPT groups using analysis of variance for continuous variables and the χ2 test for categorical variables. We categorized cPTmax and cPB as ‘no measurable atherosclerosis’and by increasing tertiles for those with atherosclerosis. Thresholds for the first, second and third tertile of cPTmax were 0.7 mm, 1.84 mm, and 2.55 mm, respectively. Analogous cut-points for cPB were 4.3 mm2, 169.4 mm2 and 536.6 mm2, respectively. We split IMT in quartiles: first quartile 0.43–0.65 mm, second quartile 0.66–0.73 mm, third quartile 0.74–0.84 mm, and fourth quartile 0.85–2.58 mm. The rates of adverse events were estimated at 3 years using the Kaplan–Maier method and compared across groups using the log-rank test. Associations between cPTmax, cPB, IMT, and adverse events were assessed using Cox proportional hazard regression models that included age, race, and gender in Model 1. Model 2 included in addition diabetes mellitus, current smoking, body mass index, systolic blood pressure, antihypertensive agent use, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and use of lipid-lowering drugs. The incremental value of adding the log-transformed cPTmax, cPB, or IMT for risk prediction was evaluated using the metrics of the model: overall fit, calibration, and reclassification. The model fit changes were assessed using likelihood ratio test.22 Calibration was evaluated using a modified version of Hosmer–Lemeshow test.23 Differences in C-index between models and 95% CI were calculated using the method of Newson.24 To assess the net effect of adding a marker to the risk prediction, we calculated the category-free net reclassification index (NRI).25 This study was designed to follow the participants for a minimum of 3 years or until the occurrence of 600 events. All analyses were carried out using Stata version 14 (StataCorp, College Station, TX, USA) and R (version 3.2.1; R Foundation for Statistical Computing, Vienna, Austria) software. Results Of the 6102 individuals, who were included in the BioImage Study, 294 were excluded due to missing covariates and/or imaging data, yielding a final study population of 5808 adults. At the end of the study period, a total of 1139 (19.6%) study participants no longer were Humana members and had not experienced any adverse events during their membership. Median follow-up period among these individuals was 1.1 years. All analyses were repeated after excluding these participants, yielding similar results to the overall cohort. Over a median follow-up period of 2.7 years, there were a total of 216 first MACE events (4.2%) including 108 deaths (2.2%), of which 27 were cardiovascular (0.5%), 34 MIs (0.7%), 30 ischaemic strokes (0.6%), 18 hospitalizations for unstable angina (0.3%), and 79 coronary revascularization procedures (1.6%). Table 1 presents baseline demographics and clinical characteristics for the entire cohort. The average age was ∼69 years, and 56% of participants were female. Table 1 Baseline characteristics for persons with no carotid plaque (no atherosclerosis) and tertiles of carotid maximum plaque thickness (cPTmax)   No atherosclerosis  Tertile 1  Tertile 2  Tertile 3  P-value  Age, years  67.4 ± 5.7  68.4 ± 6.0  69.2 ± 5.9  70.2 ± 5.8  <0.0001  Female  865 (66.5)  911 (60.3)  800 (53.4)  705 (47.0)  <0.0001  White race  827 (63.6)  1163 (77.0)  1143 (76.4)  1168 (77.9)  <0.0001  Diabetes mellitus  173 (13.3)  188 (12.5)  238 (15.9)  258 (17.2)  0.001  Current smoker  53 (9.6)  102 (13.0)  154 (17.3)  187 (19.5)  <0.0001  Hypertension  730 (56.1)  873 (57.8)  982 (65.6)  1029 (68.6)  <0.0001  BMI, kg/m2  29.5 ± 5.8  28.5 ± 5.2  29.2 ± 5.6  29.0 ± 5.5  <0.0001  LDL-C, mg/dL  114.1 ± 32.6  115.6 ± 33.4  113.9 ± 33.3  113.0 ± 33.5  <0.0001  HDL-C, mg/dL  57.8 ± 15.3  56.9 ± 15.4  54.5 ± 15.2  53.8 ± 14.9  <0.0001  Total cholesterol, mg/dL  203.0 ± 38.2  204.7 ± 38.4  201.8 ± 38.6  200.6 ± 39.0  0.0294  Systolic BP, mmHg  136.6 ± 18.2  138.2 ± 18.2  140.4 ± 18.0  142.3 ± 19.2  <0.0001  Diastolic BP, mmHg  79.2 ± 9.3  78.0 ± 8.7  78.1 ± 9.0  77.6 ± 9.2  <0.0001  Lipid-lowering therapy  369 (28.4)  507 (33.6)  572 (38.2)  545 (36.3)  <0.0001  Serum creatinine, mg/dL  0.96 ± 0.18  0.96 ± 0.20  0.98 ± 0.22  1.00 ± 0.22  <0.0001  Framingham riska   <10%  745 (58.3)  773 (52.3)  629 (43.2)  582 (39.5)  <0.0001   10–20%  443 (34.7)  551 (37.3)  591 (40.6)  585 (40.2)     ≥20%  90 (7.04)  154 (10.4)  237 (16.3)  290 (19.9)      No atherosclerosis  Tertile 1  Tertile 2  Tertile 3  P-value  Age, years  67.4 ± 5.7  68.4 ± 6.0  69.2 ± 5.9  70.2 ± 5.8  <0.0001  Female  865 (66.5)  911 (60.3)  800 (53.4)  705 (47.0)  <0.0001  White race  827 (63.6)  1163 (77.0)  1143 (76.4)  1168 (77.9)  <0.0001  Diabetes mellitus  173 (13.3)  188 (12.5)  238 (15.9)  258 (17.2)  0.001  Current smoker  53 (9.6)  102 (13.0)  154 (17.3)  187 (19.5)  <0.0001  Hypertension  730 (56.1)  873 (57.8)  982 (65.6)  1029 (68.6)  <0.0001  BMI, kg/m2  29.5 ± 5.8  28.5 ± 5.2  29.2 ± 5.6  29.0 ± 5.5  <0.0001  LDL-C, mg/dL  114.1 ± 32.6  115.6 ± 33.4  113.9 ± 33.3  113.0 ± 33.5  <0.0001  HDL-C, mg/dL  57.8 ± 15.3  56.9 ± 15.4  54.5 ± 15.2  53.8 ± 14.9  <0.0001  Total cholesterol, mg/dL  203.0 ± 38.2  204.7 ± 38.4  201.8 ± 38.6  200.6 ± 39.0  0.0294  Systolic BP, mmHg  136.6 ± 18.2  138.2 ± 18.2  140.4 ± 18.0  142.3 ± 19.2  <0.0001  Diastolic BP, mmHg  79.2 ± 9.3  78.0 ± 8.7  78.1 ± 9.0  77.6 ± 9.2  <0.0001  Lipid-lowering therapy  369 (28.4)  507 (33.6)  572 (38.2)  545 (36.3)  <0.0001  Serum creatinine, mg/dL  0.96 ± 0.18  0.96 ± 0.20  0.98 ± 0.22  1.00 ± 0.22  <0.0001  Framingham riska   <10%  745 (58.3)  773 (52.3)  629 (43.2)  582 (39.5)  <0.0001   10–20%  443 (34.7)  551 (37.3)  591 (40.6)  585 (40.2)     ≥20%  90 (7.04)  154 (10.4)  237 (16.3)  290 (19.9)    Values are represented as mean ± SD of n (%). a Framingham risk calculated from d’Agostino et al.26 Table 1 Baseline characteristics for persons with no carotid plaque (no atherosclerosis) and tertiles of carotid maximum plaque thickness (cPTmax)   No atherosclerosis  Tertile 1  Tertile 2  Tertile 3  P-value  Age, years  67.4 ± 5.7  68.4 ± 6.0  69.2 ± 5.9  70.2 ± 5.8  <0.0001  Female  865 (66.5)  911 (60.3)  800 (53.4)  705 (47.0)  <0.0001  White race  827 (63.6)  1163 (77.0)  1143 (76.4)  1168 (77.9)  <0.0001  Diabetes mellitus  173 (13.3)  188 (12.5)  238 (15.9)  258 (17.2)  0.001  Current smoker  53 (9.6)  102 (13.0)  154 (17.3)  187 (19.5)  <0.0001  Hypertension  730 (56.1)  873 (57.8)  982 (65.6)  1029 (68.6)  <0.0001  BMI, kg/m2  29.5 ± 5.8  28.5 ± 5.2  29.2 ± 5.6  29.0 ± 5.5  <0.0001  LDL-C, mg/dL  114.1 ± 32.6  115.6 ± 33.4  113.9 ± 33.3  113.0 ± 33.5  <0.0001  HDL-C, mg/dL  57.8 ± 15.3  56.9 ± 15.4  54.5 ± 15.2  53.8 ± 14.9  <0.0001  Total cholesterol, mg/dL  203.0 ± 38.2  204.7 ± 38.4  201.8 ± 38.6  200.6 ± 39.0  0.0294  Systolic BP, mmHg  136.6 ± 18.2  138.2 ± 18.2  140.4 ± 18.0  142.3 ± 19.2  <0.0001  Diastolic BP, mmHg  79.2 ± 9.3  78.0 ± 8.7  78.1 ± 9.0  77.6 ± 9.2  <0.0001  Lipid-lowering therapy  369 (28.4)  507 (33.6)  572 (38.2)  545 (36.3)  <0.0001  Serum creatinine, mg/dL  0.96 ± 0.18  0.96 ± 0.20  0.98 ± 0.22  1.00 ± 0.22  <0.0001  Framingham riska   <10%  745 (58.3)  773 (52.3)  629 (43.2)  582 (39.5)  <0.0001   10–20%  443 (34.7)  551 (37.3)  591 (40.6)  585 (40.2)     ≥20%  90 (7.04)  154 (10.4)  237 (16.3)  290 (19.9)      No atherosclerosis  Tertile 1  Tertile 2  Tertile 3  P-value  Age, years  67.4 ± 5.7  68.4 ± 6.0  69.2 ± 5.9  70.2 ± 5.8  <0.0001  Female  865 (66.5)  911 (60.3)  800 (53.4)  705 (47.0)  <0.0001  White race  827 (63.6)  1163 (77.0)  1143 (76.4)  1168 (77.9)  <0.0001  Diabetes mellitus  173 (13.3)  188 (12.5)  238 (15.9)  258 (17.2)  0.001  Current smoker  53 (9.6)  102 (13.0)  154 (17.3)  187 (19.5)  <0.0001  Hypertension  730 (56.1)  873 (57.8)  982 (65.6)  1029 (68.6)  <0.0001  BMI, kg/m2  29.5 ± 5.8  28.5 ± 5.2  29.2 ± 5.6  29.0 ± 5.5  <0.0001  LDL-C, mg/dL  114.1 ± 32.6  115.6 ± 33.4  113.9 ± 33.3  113.0 ± 33.5  <0.0001  HDL-C, mg/dL  57.8 ± 15.3  56.9 ± 15.4  54.5 ± 15.2  53.8 ± 14.9  <0.0001  Total cholesterol, mg/dL  203.0 ± 38.2  204.7 ± 38.4  201.8 ± 38.6  200.6 ± 39.0  0.0294  Systolic BP, mmHg  136.6 ± 18.2  138.2 ± 18.2  140.4 ± 18.0  142.3 ± 19.2  <0.0001  Diastolic BP, mmHg  79.2 ± 9.3  78.0 ± 8.7  78.1 ± 9.0  77.6 ± 9.2  <0.0001  Lipid-lowering therapy  369 (28.4)  507 (33.6)  572 (38.2)  545 (36.3)  <0.0001  Serum creatinine, mg/dL  0.96 ± 0.18  0.96 ± 0.20  0.98 ± 0.22  1.00 ± 0.22  <0.0001  Framingham riska   <10%  745 (58.3)  773 (52.3)  629 (43.2)  582 (39.5)  <0.0001   10–20%  443 (34.7)  551 (37.3)  591 (40.6)  585 (40.2)     ≥20%  90 (7.04)  154 (10.4)  237 (16.3)  290 (19.9)    Values are represented as mean ± SD of n (%). a Framingham risk calculated from d’Agostino et al.26 Carotid plaque was found in 4507 (78%) individuals. The level of risk factors increased with increasing cPT. Figure 2 shows the crude 3-year event rates for primary and secondary MACE by cPTmax and IMT groups. Trends of higher risk were observed with increasing cPTmax and IMT although slightly weaker for primary MACE. IMT quartiles seemed to separate poorer between low and high risk as did cPTmax (cPTmax log-rank P < 0.001, for primary MACE and P < 0.001, for secondary MACE when compared with IMT log-rank P < 0.013 and 0.009 for primary and secondary MACE) although both statistically significant. Figure 2 View largeDownload slide Crude rates calculated as the Kaplan–Meier estimates at 3 years for primary and secondary major adverse cardiac event(s) (MACE) by carotid plaque tickness (cPT) and intima–media Thickness (IMT). Figure 2 View largeDownload slide Crude rates calculated as the Kaplan–Meier estimates at 3 years for primary and secondary major adverse cardiac event(s) (MACE) by carotid plaque tickness (cPT) and intima–media Thickness (IMT). Table 2 presents hazard ratios (HRs) for primary and secondary MACE associated with cPTmax, cPB, and IMT. Increasing HRs were observed with increasing values for all three, although only statistically significant for cPTmax and cPB after adjustment for all risk factors (Models 1 and 2). HRs for cPTmax predicted similarly to cPB with regard to future adverse events. Table 2 HRs (95% CI) for primary and secondary MACE end points associated with cPTmax, cPB, and carotid IMT   No atherosclerosis  Tertile 1  Tertile 2  Tertile 3  P-value (trend)  Hazard ratios (95% CI) for primary MACE end point   cPTmax    Model 1  1.0 (ref)  0.88 (0.36–2.19)  2.41 (1.13–5.14)  2.52 (1.18–5.35)  0.001    Model 2  1.0 (ref)  0.85 (0.34–2.11)  2.09 (0.97–4.50)  1.96 (0.91–4.25)  0.015   cPB    Model 1  1.0 (ref)  0.87 (0.36–2.10)  1.56 (0.72–3.36)  2.85 (1.39–5.82)  <0.001    Model 2  1.0 (ref)  0.78 (0.31–1.91)  1.45 (0.67–3.14)  2.36 (1.13–4.92)  0.030   IMT    Model 1  1.0 (ref)  1.16 (0.57–2.34)  1.06 (0.52–2.18)  1.82 (0.95–3.50)  0.066    Model 2  1.0 (ref)  1.10 (0.54–2.23)  0.87 (0.42–1.18)  1.38 (0.71–2.70)  0.372  Hazard ratios (95% CI) for secondary MACE end point   cPT max    Model 1  1.0 (ref)  1.71 (0.94–3.10)  3.59 (2.09–6.19)  3.73 (2.16–6.41)  <0.001    Model 2  1.0 (ref)  1.66 (0.91–3.01)  3.18 (1.83–5.51)  3.13 (1.80–5.51)  0.001   cPB    Model 1  1.0 (ref)  1.59 (0.92–2.74)  2.27 (1.36–3.79)  3.41 (2.08–5.58)  <0.001    Model 2  1.0 (ref)  1.53 (0.89–2.65)  2.14 (1.28–3.59)  2.87 (1.73–4.74)  <0.001   IMT    Model 1  1.0 (ref)  0.85 (0.56–1.31)  1.03 (0.69–1.55)  1.36 (0.92–2.00)  0.052    Model 2  1.0 (ref)  0.84 (0.55–1.28)  0.90 (0.59–1.36)  1.09 (0.73–1.62)  0.502    No atherosclerosis  Tertile 1  Tertile 2  Tertile 3  P-value (trend)  Hazard ratios (95% CI) for primary MACE end point   cPTmax    Model 1  1.0 (ref)  0.88 (0.36–2.19)  2.41 (1.13–5.14)  2.52 (1.18–5.35)  0.001    Model 2  1.0 (ref)  0.85 (0.34–2.11)  2.09 (0.97–4.50)  1.96 (0.91–4.25)  0.015   cPB    Model 1  1.0 (ref)  0.87 (0.36–2.10)  1.56 (0.72–3.36)  2.85 (1.39–5.82)  <0.001    Model 2  1.0 (ref)  0.78 (0.31–1.91)  1.45 (0.67–3.14)  2.36 (1.13–4.92)  0.030   IMT    Model 1  1.0 (ref)  1.16 (0.57–2.34)  1.06 (0.52–2.18)  1.82 (0.95–3.50)  0.066    Model 2  1.0 (ref)  1.10 (0.54–2.23)  0.87 (0.42–1.18)  1.38 (0.71–2.70)  0.372  Hazard ratios (95% CI) for secondary MACE end point   cPT max    Model 1  1.0 (ref)  1.71 (0.94–3.10)  3.59 (2.09–6.19)  3.73 (2.16–6.41)  <0.001    Model 2  1.0 (ref)  1.66 (0.91–3.01)  3.18 (1.83–5.51)  3.13 (1.80–5.51)  0.001   cPB    Model 1  1.0 (ref)  1.59 (0.92–2.74)  2.27 (1.36–3.79)  3.41 (2.08–5.58)  <0.001    Model 2  1.0 (ref)  1.53 (0.89–2.65)  2.14 (1.28–3.59)  2.87 (1.73–4.74)  <0.001   IMT    Model 1  1.0 (ref)  0.85 (0.56–1.31)  1.03 (0.69–1.55)  1.36 (0.92–2.00)  0.052    Model 2  1.0 (ref)  0.84 (0.55–1.28)  0.90 (0.59–1.36)  1.09 (0.73–1.62)  0.502  Model 1 was adjusted for age, race, and gender. Model 2 was additionally adjusted for diabetes mellitus, current smoking, body mass index, systolic blood pressure, antihypertensive agent use, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and use of lipid-lowering drugs. cPTmax, maximum carotid plaque thickness; CI, confidence interval; cPB, carotid plaque burden; HR, hazard ratio; IMT, intima–media thickness (quartile); MACE, major adverse cardiovascular events. Table 2 HRs (95% CI) for primary and secondary MACE end points associated with cPTmax, cPB, and carotid IMT   No atherosclerosis  Tertile 1  Tertile 2  Tertile 3  P-value (trend)  Hazard ratios (95% CI) for primary MACE end point   cPTmax    Model 1  1.0 (ref)  0.88 (0.36–2.19)  2.41 (1.13–5.14)  2.52 (1.18–5.35)  0.001    Model 2  1.0 (ref)  0.85 (0.34–2.11)  2.09 (0.97–4.50)  1.96 (0.91–4.25)  0.015   cPB    Model 1  1.0 (ref)  0.87 (0.36–2.10)  1.56 (0.72–3.36)  2.85 (1.39–5.82)  <0.001    Model 2  1.0 (ref)  0.78 (0.31–1.91)  1.45 (0.67–3.14)  2.36 (1.13–4.92)  0.030   IMT    Model 1  1.0 (ref)  1.16 (0.57–2.34)  1.06 (0.52–2.18)  1.82 (0.95–3.50)  0.066    Model 2  1.0 (ref)  1.10 (0.54–2.23)  0.87 (0.42–1.18)  1.38 (0.71–2.70)  0.372  Hazard ratios (95% CI) for secondary MACE end point   cPT max    Model 1  1.0 (ref)  1.71 (0.94–3.10)  3.59 (2.09–6.19)  3.73 (2.16–6.41)  <0.001    Model 2  1.0 (ref)  1.66 (0.91–3.01)  3.18 (1.83–5.51)  3.13 (1.80–5.51)  0.001   cPB    Model 1  1.0 (ref)  1.59 (0.92–2.74)  2.27 (1.36–3.79)  3.41 (2.08–5.58)  <0.001    Model 2  1.0 (ref)  1.53 (0.89–2.65)  2.14 (1.28–3.59)  2.87 (1.73–4.74)  <0.001   IMT    Model 1  1.0 (ref)  0.85 (0.56–1.31)  1.03 (0.69–1.55)  1.36 (0.92–2.00)  0.052    Model 2  1.0 (ref)  0.84 (0.55–1.28)  0.90 (0.59–1.36)  1.09 (0.73–1.62)  0.502    No atherosclerosis  Tertile 1  Tertile 2  Tertile 3  P-value (trend)  Hazard ratios (95% CI) for primary MACE end point   cPTmax    Model 1  1.0 (ref)  0.88 (0.36–2.19)  2.41 (1.13–5.14)  2.52 (1.18–5.35)  0.001    Model 2  1.0 (ref)  0.85 (0.34–2.11)  2.09 (0.97–4.50)  1.96 (0.91–4.25)  0.015   cPB    Model 1  1.0 (ref)  0.87 (0.36–2.10)  1.56 (0.72–3.36)  2.85 (1.39–5.82)  <0.001    Model 2  1.0 (ref)  0.78 (0.31–1.91)  1.45 (0.67–3.14)  2.36 (1.13–4.92)  0.030   IMT    Model 1  1.0 (ref)  1.16 (0.57–2.34)  1.06 (0.52–2.18)  1.82 (0.95–3.50)  0.066    Model 2  1.0 (ref)  1.10 (0.54–2.23)  0.87 (0.42–1.18)  1.38 (0.71–2.70)  0.372  Hazard ratios (95% CI) for secondary MACE end point   cPT max    Model 1  1.0 (ref)  1.71 (0.94–3.10)  3.59 (2.09–6.19)  3.73 (2.16–6.41)  <0.001    Model 2  1.0 (ref)  1.66 (0.91–3.01)  3.18 (1.83–5.51)  3.13 (1.80–5.51)  0.001   cPB    Model 1  1.0 (ref)  1.59 (0.92–2.74)  2.27 (1.36–3.79)  3.41 (2.08–5.58)  <0.001    Model 2  1.0 (ref)  1.53 (0.89–2.65)  2.14 (1.28–3.59)  2.87 (1.73–4.74)  <0.001   IMT    Model 1  1.0 (ref)  0.85 (0.56–1.31)  1.03 (0.69–1.55)  1.36 (0.92–2.00)  0.052    Model 2  1.0 (ref)  0.84 (0.55–1.28)  0.90 (0.59–1.36)  1.09 (0.73–1.62)  0.502  Model 1 was adjusted for age, race, and gender. Model 2 was additionally adjusted for diabetes mellitus, current smoking, body mass index, systolic blood pressure, antihypertensive agent use, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and use of lipid-lowering drugs. cPTmax, maximum carotid plaque thickness; CI, confidence interval; cPB, carotid plaque burden; HR, hazard ratio; IMT, intima–media thickness (quartile); MACE, major adverse cardiovascular events. Tables 3 and 4 present the impact on model performance of adding cPTmax, cPB, and IMT to the baseline conventional risk factor (CRF) Model 1. All three parameters significantly improved model fit although IMT the least. cPTmax and cPB significantly improved category-free NRIs, both for primary and secondary MACE, when added to the baseline CRF model, whereas IMT did not. The model performance of adding the ultrasound parameters to only gender, age, and race yielded similar results. Table 3 Impact on model performance by adding cPTmax, cPB and carotid IMT Model  Model fita   Discriminationb  Calibration   χ2  P-value  Change in C-index (95% CI)  χ2  P-value  Impact on model performance for prediction of primary MACE end point   Model (CRF)  41.5  Ref model  Ref model  4.3  0.37   Model (CRF) + cPT  50.7  <0.001  0.01 (−0.02 to 0.04)  6.3  0.18   Model (CRF) + cPB  50.1  0.003  0.01 (−0.02 to 0.04)  3.4  0.49   Model (CRF) + IMT  45.3  <0.001  −0.00 (−0.01 to 0.01)  3.8  0.44  Impact on model performance for prediction of secondary MACE end point   Model (CRF)  92.3  Ref model  Ref model  7.8  0.09   Model (CRF) + cPT  128.9  <0.001  0.02 (0.001 to 0.04)  2.7  0.61   Model (CRF) + cPB  115.6  <0.001  0.03 (0.006 to 0.05)  4.6  0.33   Model (CRF) + IMT  98.4  <0.001  −0.00 (−0.004 to 0.003)  6.8  0.15  Model  Model fita   Discriminationb  Calibration   χ2  P-value  Change in C-index (95% CI)  χ2  P-value  Impact on model performance for prediction of primary MACE end point   Model (CRF)  41.5  Ref model  Ref model  4.3  0.37   Model (CRF) + cPT  50.7  <0.001  0.01 (−0.02 to 0.04)  6.3  0.18   Model (CRF) + cPB  50.1  0.003  0.01 (−0.02 to 0.04)  3.4  0.49   Model (CRF) + IMT  45.3  <0.001  −0.00 (−0.01 to 0.01)  3.8  0.44  Impact on model performance for prediction of secondary MACE end point   Model (CRF)  92.3  Ref model  Ref model  7.8  0.09   Model (CRF) + cPT  128.9  <0.001  0.02 (0.001 to 0.04)  2.7  0.61   Model (CRF) + cPB  115.6  <0.001  0.03 (0.006 to 0.05)  4.6  0.33   Model (CRF) + IMT  98.4  <0.001  −0.00 (−0.004 to 0.003)  6.8  0.15  cPB, carotid plaque burden; cPT, carotid plaque thickness; CRF, conventional risk factor; IMT, intima–media thickness. aChanges in model fit assessed using the likelihood ratio test.23 bDifferences in c-index between models and 95% CI were calculated using the method of Newson.24 Table 3 Impact on model performance by adding cPTmax, cPB and carotid IMT Model  Model fita   Discriminationb  Calibration   χ2  P-value  Change in C-index (95% CI)  χ2  P-value  Impact on model performance for prediction of primary MACE end point   Model (CRF)  41.5  Ref model  Ref model  4.3  0.37   Model (CRF) + cPT  50.7  <0.001  0.01 (−0.02 to 0.04)  6.3  0.18   Model (CRF) + cPB  50.1  0.003  0.01 (−0.02 to 0.04)  3.4  0.49   Model (CRF) + IMT  45.3  <0.001  −0.00 (−0.01 to 0.01)  3.8  0.44  Impact on model performance for prediction of secondary MACE end point   Model (CRF)  92.3  Ref model  Ref model  7.8  0.09   Model (CRF) + cPT  128.9  <0.001  0.02 (0.001 to 0.04)  2.7  0.61   Model (CRF) + cPB  115.6  <0.001  0.03 (0.006 to 0.05)  4.6  0.33   Model (CRF) + IMT  98.4  <0.001  −0.00 (−0.004 to 0.003)  6.8  0.15  Model  Model fita   Discriminationb  Calibration   χ2  P-value  Change in C-index (95% CI)  χ2  P-value  Impact on model performance for prediction of primary MACE end point   Model (CRF)  41.5  Ref model  Ref model  4.3  0.37   Model (CRF) + cPT  50.7  <0.001  0.01 (−0.02 to 0.04)  6.3  0.18   Model (CRF) + cPB  50.1  0.003  0.01 (−0.02 to 0.04)  3.4  0.49   Model (CRF) + IMT  45.3  <0.001  −0.00 (−0.01 to 0.01)  3.8  0.44  Impact on model performance for prediction of secondary MACE end point   Model (CRF)  92.3  Ref model  Ref model  7.8  0.09   Model (CRF) + cPT  128.9  <0.001  0.02 (0.001 to 0.04)  2.7  0.61   Model (CRF) + cPB  115.6  <0.001  0.03 (0.006 to 0.05)  4.6  0.33   Model (CRF) + IMT  98.4  <0.001  −0.00 (−0.004 to 0.003)  6.8  0.15  cPB, carotid plaque burden; cPT, carotid plaque thickness; CRF, conventional risk factor; IMT, intima–media thickness. aChanges in model fit assessed using the likelihood ratio test.23 bDifferences in c-index between models and 95% CI were calculated using the method of Newson.24 Table 4 Effect on categori-free net reclassification index (MRI) of adding cPTmac, cPB and carotid IMT Model  Reclassification   NRI  (95% CI)  P-value  Impact on model performance for prediction of primary MACE end point   Model 1 (CRF)  Ref model       Model 1 + cPTmax  0.178  (0.027–0.299)  0.032   Model 1 + cPB  0.228  (0.002–0.320)  0.040   Model 1 + IMT  0.016  (−0.095–0.146)  0.798  Impact on model performance for prediction of secondary MACE end point   Model 1 (CRF)  Ref model       Model 1 + cPTmax  0.173  (0.109–0.243)  <0.0001   Model 1 + cPB  0.174  (0.102–0.245)  <0.0001   Model 1 + IMT  0.015  (−0.060–0.100)  0.559  Model  Reclassification   NRI  (95% CI)  P-value  Impact on model performance for prediction of primary MACE end point   Model 1 (CRF)  Ref model       Model 1 + cPTmax  0.178  (0.027–0.299)  0.032   Model 1 + cPB  0.228  (0.002–0.320)  0.040   Model 1 + IMT  0.016  (−0.095–0.146)  0.798  Impact on model performance for prediction of secondary MACE end point   Model 1 (CRF)  Ref model       Model 1 + cPTmax  0.173  (0.109–0.243)  <0.0001   Model 1 + cPB  0.174  (0.102–0.245)  <0.0001   Model 1 + IMT  0.015  (−0.060–0.100)  0.559  NRI calculated using the category-free version.25 cPB, carotid plaque burden; CI, confidence interval; cPT, carotid plaque thickness; CRF, conventional risk factor; IMT, intima–media thickness; MACE, major adverse cardiovascular events; NRI, net reclassification index. Table 4 Effect on categori-free net reclassification index (MRI) of adding cPTmac, cPB and carotid IMT Model  Reclassification   NRI  (95% CI)  P-value  Impact on model performance for prediction of primary MACE end point   Model 1 (CRF)  Ref model       Model 1 + cPTmax  0.178  (0.027–0.299)  0.032   Model 1 + cPB  0.228  (0.002–0.320)  0.040   Model 1 + IMT  0.016  (−0.095–0.146)  0.798  Impact on model performance for prediction of secondary MACE end point   Model 1 (CRF)  Ref model       Model 1 + cPTmax  0.173  (0.109–0.243)  <0.0001   Model 1 + cPB  0.174  (0.102–0.245)  <0.0001   Model 1 + IMT  0.015  (−0.060–0.100)  0.559  Model  Reclassification   NRI  (95% CI)  P-value  Impact on model performance for prediction of primary MACE end point   Model 1 (CRF)  Ref model       Model 1 + cPTmax  0.178  (0.027–0.299)  0.032   Model 1 + cPB  0.228  (0.002–0.320)  0.040   Model 1 + IMT  0.016  (−0.095–0.146)  0.798  Impact on model performance for prediction of secondary MACE end point   Model 1 (CRF)  Ref model       Model 1 + cPTmax  0.173  (0.109–0.243)  <0.0001   Model 1 + cPB  0.174  (0.102–0.245)  <0.0001   Model 1 + IMT  0.015  (−0.060–0.100)  0.559  NRI calculated using the category-free version.25 cPB, carotid plaque burden; CI, confidence interval; cPT, carotid plaque thickness; CRF, conventional risk factor; IMT, intima–media thickness; MACE, major adverse cardiovascular events; NRI, net reclassification index. Figure 3 shows HRs for cPTmax, IMT, and cPB. Both cPTmax and cPB had almost similar, strong prediction of primary and secondary MACE, but IMT did not. There was no difference in predictive value of cPB and cPTmax (primary end point P-value = 0.4279; secondary end point P-value = 0.7646). cPTsum, being the sum of the highest value from either side, was also analysed and found almost similarly predictive as cPTmax (data not shown). Figure 3 View largeDownload slide Hazard ratios for primary and secondary MACE, unadjusted and adjusted according to Model 1 for cPTmax, cPB, and IMT. Figure 3 View largeDownload slide Hazard ratios for primary and secondary MACE, unadjusted and adjusted according to Model 1 for cPTmax, cPB, and IMT. Discussion The presence of plaque in the carotid artery has already been identified as an independent predictor for future cardiovascular events9,27; however, in this study, we quantified carotid plaque from cross-sectional images (short axis) and found it to be stepwise predictive of future ASCVD; the thicker the plaque the higher the risk. Considering progression of atherosclerosis/growth of plaques, the association between cPTmax and risk of atherosclerotic complications is not surprising. Lacking true 3D technology, we introduced assessment of cPB, summarizing plaque areas from serial cross-sectional 2D images4 and showed that this approach could predict future adverse events similarly to CACS.10 In this study, cPTmax performed more or less similar to cPB. Despite that cPB was a more comprehensive assessment tool, taking into account that plaques may have different shapes and that there may be more than one plaque, cPTmax being much simpler to assess, performed similarly. This can certainly relate to the nature of the 10-s, cross-sectional sweep without control of speed of movement (lack of true 3D), which potentially introduces inaccuracy of cPB. However, the 3D nature of the cross-sectional sweep, ensuring data capture from the entire cervical portion of the carotid artery, therefore allowing for identification of any plaque, may be of importance for our findings. Further, the cross-sectional image shows from which anatomical location the measurement should be made to measure the true radial distance from the media/adventitia border to the centre of the artery, where the plaque is thickest. Of course, given that the image is acquired at a perpendicular angle with respect to the long axis of the artery. Rundek et al.28 also measured cPT, however, acquired from images in long axis. They also showed cPT to be predictive of future cardiovascular events, however, in a different population as 21% already had established ‘cardiac disease’ at baseline. The theoretical advantage of our technique is that only in cross-section can the true radial distance, plaque thickness, be measured. Due to focusing of the ultrasound beam to obtain sharp images, only a thin ‘slice’ of the artery/plaque is visualized, potentially not reflecting the true size of the plaque. In principle, cPTmax can be assessed directly from frozen ultrasound images on the ultrasound machine without the need for offline analyses. Future true 3D applications might improve prediction not only for cPTmax, since it can then be estimated perpendicular to the centre line of the vessel, but also allow plaque volume measurements. Reproducibility should improve from 3D imaging, allowing for accurate repetitive scans over time.29 In this manner, evaluation of anti-atherosclerotic treatment might improve being based on imaging observing changes in atherosclerosis amount rather than based on changes in blood tests.30,31 An alternative to cPTmax is cPTsum: the sum of the thickest plaque in both carotid arteries (right and left). The latter was also analysed and found to have almost similar predictive value (data not shown); however, since cPTmax is the simplest, we chose to analyse this primarily. In our study, we used semi-automated software outlining plaque in all images, automatically calculating plaque thickness as described above (Figure 1). Other methods of quantifying carotid plaque size by ultrasound have shown similar results, e.g. measuring plaque area on 2D images acquired in long axis.32,33 Whether this method is similar to, superior, or inferior to cPT cannot be judged as acquisition and analysis methods and populations are not directly comparable. However, data on plaque area are based on images in long axis with the limitations referred to the above, namely that only part of the plaque is visualized due to focusing of the ultrasound beam. Although patients with the highest IMT values experienced more adverse events than those with low IMT, the predictive value was non-significant when adjusted for traditional risk factors. Especially, prediction of low risk was inferior to that of no plaque. That IMT did not predict risk very well is not surprising, considering the data available regarding IMT today.7,10,12,34,35 Our study may be criticized for only acquiring IMT from one angle; however, apart from that, the methodology used was similar to the current recommendations. The important issue of IMT, and why it is not as predictive as presence and quantification of plaque, is the approach of only acquiring data from a given, small anatomical location, i.e. distal CCA, rather than interrogating the entire vessel for the presence of atherosclerosis. Other criticism of IMT has been raised, i.e. that thickening of the intima–media complex may be a result of hypertension rather than atherosclerosis. Most importantly, IMT does not predict risk beyond that of traditional risk factors in the individual person,7,10,34 and therefore, serial IMT measurements are not recommended.16,17,36 Given the advantages of ultrasound being harmless, portable, and inexpensive, this technique may be an alternative to CT based, despite its potential disadvantages. On the other hand, ultrasound is operator dependent, thus training and certification remains essential. However, 3D technology will ease correct acquisition using simultaneous imaging of several planes. In addition, ultrasound can identify soft and small plaques and therefore potentially identify earlier stages of disease than CACS, widening the window for prevention further. Another approach, potentially improving predictability, is to add observations from other anatomical locations, i.e. arteries of lower extremity where atherosclerosis may be more prevalent.5,37 Our study had limitations with regard to the method of participant follow-up and ultrasound technology. In case of the former, these have been described in detail4,10; however, in brief, the reliance on health insurance claims to identify adverse events may have resulted in a lower-than-expected rate of adverse events. However, comparing with other methods across the same population, this should have not affected our results. Longer period of follow-up would have strengthened the study. With respect to the ultrasound technology used in the BioImage Study, limitations of these have already been discussed in detail.4,10 However, specifically for this study, acquisition of cross-sectional images were assumed to be at 90° with respect to the long axis of the carotid artery; however, if acquired at non-perpendicular angles, error could have been introduced with measurement of too high values for cPTmax (and cPB). Specifically at the location of the carotid bifurcation, where the internal carotid artery branches off, this is where plaque often develops and ‘angle inaccuracy’ might occur. Limitations with regard to IMT have been discussed above. cPTmax values were automatically derived from the semi-automated outlines of plaques performed using QLAB–VPQ. Although the operator analysing the 10-s ultrasound video’s reviewed all outlines, especially in plaques reflecting ultrasound poorly (echo-weak/lucent plaques), or in case of calcification and/or severe stenosis, outlining was difficult and could be inaccurate. In conclusion, we found the simple cPTmax being similarly predictive as cPB for the development of symptomatic ASCVD. The presented data add to the accumulating evidence that quantification of carotid plaque by ultrasound may contribute significantly to personalized ASCVD risk prediction. Funding The High-Risk Plaque Initiative is a pre-competitive industry collaboration funded by BG Medicine, Abbott Vascular, AstraZeneca, Merck & Co, Philips, and Takeda. Conflict of interest: H.S. has received speaker honorarium from Philips Ultrasound, and Department of Vascular Surgery, Rigshospitalet, University of Copenhagen, Denmark, has received research grant from Philips Ultrasound. References 1 Mortensen MB, Sivesgaard K, Jensen HK, Comuth W, Kanstrup H, Gotzsche O et al.   Traditional SCORE-based health check fails to identify individuals who develop acute myocardial infarction. Dan Med J  2013; 60: A4629. Google Scholar PubMed  2 Jørgensen T, Jacobsen RK, Toft U, Aadahl M, Glümer C, Pisinger C. Effect of screening and lifestyle counseling on incidence of ischemic heart disease in general population: Inter99 randomized trial. BMJ  2014; 348: g3617. Google Scholar CrossRef Search ADS PubMed  3 Krogsbøll LT, Jørgensen KJ, Grønhøj Larsen C, Gøtzsche PC. General health checks in adults for reducing morbidity and mortality from disease. Cochrane Database Syst Rev  2012; 10: CD009009. doi: 10.1002/14651858.CD009009.pub2. Google Scholar PubMed  4 Sillesen H, Muntendam P, Adourian A, Entrekin R, Garcia M, Falk E et al.   Carotid plaque burden as a measure of subclinical atherosclerosis: comparison with other tests for subclinical arterial disease in the High Risk Plaque BioImage study. JACC Cardivasc Imaging  2012; 5: 681– 9. Google Scholar CrossRef Search ADS   5 Fernández-Friera L, Peñalvo JL, Fernández-Ortiz A, Ibañez B, López-Melgar B, Laclaustra M et al.   Prevalence, vascular distribution, and multiterritorial extent of subclinical atherosclerosis in a middle-aged cohort the PESA (Progression of Early Subclinical Atherosclerosis) Study. Circulation  2015; 131: ß2104– 113. Google Scholar CrossRef Search ADS   6 van der Meer IM, Bots ML, Hofman A, del Sol AI, van der Kuip DA, Witteman JC. Predictive value of noninvasive measures of atherosclerosis for incident myocardial infarction: the Rotterdam Study. Circulation  2004; 109: 1089– 94. Google Scholar CrossRef Search ADS PubMed  7 Yeboah J, McClelland RL, Polonsky TS, Burke GL, Sibley CT, O’Leary D et al.   Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate risk individuals. JAMA  2012; 308: 788– 95. Google Scholar CrossRef Search ADS PubMed  8 Kavousi M, Elias-Smale S, Rutten JH, Leening MJ, Vliegenthart R, Verwoert GC et al.   Evaluation of newer risk markers for coronary heart disease risk classification: a cohort study. Ann Intern Med  2012; 156: 438– 44. Google Scholar CrossRef Search ADS PubMed  9 Nambi V, Chambless L, Folsom AR, He M, Hu Y, Mosley T et al.   Carotid intima-media thickness and presence or absence of plaque improves prediction of coronary heart disease risk: the ARIC (Atherosclerosis Risk In Communities) study. J Am Coll Cardiol  2010; 55: 1600– 7. Google Scholar CrossRef Search ADS PubMed  10 Baber U, Mehran R, Sartori S, Schoos MM, Sillesen H, Muntendam P et al.   Prevalence, impact, and predictive value of detecting subclinical coronary and carotid atherosclerosis in asymptomatic adults. J Am Coll Cardiol  2015; 65: 1065– 74. Google Scholar CrossRef Search ADS PubMed  11 Otsuka F, Sakakura K, Yahagi K, Joner M, Virmani R. Has our understanding of calcification in human coronary atherosclerosis progressed? Arterioscler Thromb Vasc Biol  2014; 34: 724– 36. Google Scholar CrossRef Search ADS PubMed  12 Lorenz MW, Schaefer C, Steinmetz H, Sitzer M. Is carotid intima media thickness useful for individual prediction of cardiovascular risk? Ten-year results from the Carotid Atherosclerosis Progression Study (CAPS). Eur Heart J  2010; 31: 2041– 8. Google Scholar CrossRef Search ADS PubMed  13 Den Ruijter HM, Peters SAE, Anderson TJ, Britton AR, Dekker JM, Eijkemans MJ et al.   Common carotid intima-media thickness measurements in cardiovascular risk prediction: a meta-analysis. JAMA  2012; 308: 796– 803. Google Scholar CrossRef Search ADS PubMed  14 Muntendam P, McCall C, Sanz J, Falk E, Fuster V; High-Risk Plaque Initiative. The BioImage study: novel approaches to risk assessment in the primary prevention of atherosclerotic cardiovascular disease–study design and objectives. Am Heart J  2010; 160: 49– 57. Google Scholar CrossRef Search ADS PubMed  15 Oates CP, Naylor AR, Hartshorne T, Charles SM, Fail T, Humphries K et al.   Joint recommendations for reporting carotid ultrasound investigation in the United Kingdom. Eur J Vasc Endovasc Surg  2009; 37: 51– 61. Google Scholar CrossRef Search ADS   16 Stein JH, Korcarz CE, Hurst RT, Lonn E, Kendall CB, Mohler ER et al.   Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: a consensus statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force. Endorsed by the Society for Vascular Medicine. J Am Soc Echocardiogr  2008; 21: 93– 111. Google Scholar CrossRef Search ADS PubMed  17 Touboul PJ, Hennerici MG, Meairs S, Adams H, Amarenco P, Bornstein N et al.   Mannheim carotid intima-media thickness consensus (2004-2006). An update on behalf of the Advisory Board of the 3rd and 4th Watching the Risk Symposium, 13th and 15th European Stroke Conferences, Mannheim, Germany, 2004, and Brussels, Belgium, 2006. Cerebrovasc Dis  2007; 23: 75– 80. Google Scholar CrossRef Search ADS PubMed  18 Thygesen K, Alpert JS, White HD; Joint ESC/ACCF/AHA/WHF Task Force for the Redefinition of Myocardial Infarction. Universal definition of myocardial infarction. J Am Coll Cardiol  2007; 50: 2173– 95. Google Scholar CrossRef Search ADS PubMed  19 Braunwald E. Unstable angina. A classification. Circulation  1989; 80: 410– 4. Google Scholar CrossRef Search ADS PubMed  20 Braunwald E. Unstable angina: an etiologic approach to management. Circulation  1998; 98: 2219– 22. Google Scholar CrossRef Search ADS PubMed  21 Homma S, Thompson JL, Pullicino PM, Levin B, Freudenberger RS, Teerlink JR; for the WsARCEF Investigators et al.   Warfarin and aspirin in patients with heart failure and sinus rhythm. N Engl J Med  2012; 366: 1859– 69. Google Scholar CrossRef Search ADS PubMed  22 Harrell F. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis . New York, NY: Springer; 2001. 23 May S, Hosmer DW. A simplified method of calculating an overall goodness-of-fit test for the Cox proportional hazards model. Lifetime Data Anal  1998; 4: 109– 20. Google Scholar CrossRef Search ADS PubMed  24 Newson R. Comparing the predictive powers of survival models using Harrell’s C or Somers’ D. Stata J  2010; 10: 339– 58. 25 Pencina MJ, D’agostino RB, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med  2011; 30: 11– 21. Google Scholar CrossRef Search ADS PubMed  26 D’Agostino RBSr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM et al.   General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation  2008; 117: 743– 53. Google Scholar CrossRef Search ADS PubMed  27 Polak JF, Pencina MJ, Pencina KM, O’Donnell CJ, Wolf PA, D’Agostino RB. Carotid-wall intima–media thickness and cardiovascular events. N Engl J Med  2011; 365: 213– 21. Google Scholar CrossRef Search ADS PubMed  28 Rundek T, Arif H, Boden-Albala B, Elkind MS, Paik MC, Sacco RL. Carotid plaque, a subclinical precursorof vascular events: the Northern Manhattan Study. Neurology  2008; 70: 1200– 7. Google Scholar CrossRef Search ADS PubMed  29 Græbe M, Entrekin R, Collet-Billon A, Harrison G, Sillesen H. Reproducibility of two 3-D ultrasound carotid plaque quantification methods. Ultrasound Med Biol  2014; 40: 1641– 9. Google Scholar CrossRef Search ADS PubMed  30 Spence JD. Measurement of carotid plaque burden. JAMA Neurol  2015; 72: 383– 4. Google Scholar CrossRef Search ADS PubMed  31 Wannarong T, Parraga G, Buchanan D, Fenster A, House AA, Hackam DG et al.   Progression of carotid plaque volume predicts cardiovascular events. Stroke  2013; 44: 1859– 65. Google Scholar CrossRef Search ADS PubMed  32 Spence JD, Eliasziw M, Dicicco M, Hackam DG, Galil R, Lohmann T. Carotid plaque area. A tool for targeting and evaluating vascular preventive therapy. Stroke  2002; 33: 2916– 22. Google Scholar CrossRef Search ADS PubMed  33 Mathiesen EB, Johnsen SH, Wilsgaard T, Bønaa KH, Løchen ML, Njølstad I et al.   Carotid plaque area and intima-media thickness in prediction of first-ever ischemic stroke. A 10-year follow-up of 6584 men and women: the Tromsø Study. Stroke  2011; 42: 972– 8. Google Scholar CrossRef Search ADS PubMed  34 van den Oord SC, Sijbrands EJ, ten Kate GL, van Klaveren D, van Domburg RT, van der Steen AF et al.   Carotid intima-media thickness for cardiovascular risk assessment: systematic review and meta-analysis. Atherosclerosis  2013; 228: 1– 11. Google Scholar CrossRef Search ADS PubMed  35 Vlachopoulos C, Xaplanteris P, Aboyans V, Brodmann M, Cífkov R, Cosentino F et al.   The role of vascular biomarkers for primary and secondary prevention. A position paper from the European Society of Cardiology Working Group on peripheral circulation. Atherosclerosis  2015; 241: 507– 32. Google Scholar CrossRef Search ADS PubMed  36 Lorenz MW, Polak JF, Kavousi M, Mathiesen EB, Völzke H, Tuomainen TP et al.   Carotid intima-media thickness progression to predict cardiovascular events in the general population (the PROG-IMT collaborative project): a meta-analysis of individual participant data. Lancet  2012; 379: 2053– 62. Google Scholar CrossRef Search ADS PubMed  37 Belcaro G, Nicolaides AN, Ramaswami G, Cesarone MR, De Sanctis M, Incandela L et al.   Carotid and femoral ultrasound morphology screening and cardiovascular events in low risk subjects: a 10-year follow-up study (the CAFES-CAVE study). Atherosclerosis  2001; 156: 379– 87. Google Scholar CrossRef Search ADS PubMed  Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For permissions, please email: journals.permissions@oup.com. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png European Heart Journal – Cardiovascular Imaging Oxford University Press

Carotid plaque thickness and carotid plaque burden predict future cardiovascular events in asymptomatic adult Americans

Loading next page...
 
/lp/ou_press/carotid-plaque-thickness-and-carotid-plaque-burden-predict-future-Ni02DepE6q
Publisher
Oxford University Press
Copyright
Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For permissions, please email: journals.permissions@oup.com.
ISSN
2047-2404
D.O.I.
10.1093/ehjci/jex239
Publisher site
See Article on Publisher Site

Abstract

Abstract Introduction Prediction of cardiovascular events improves using imaging, i.e. coronary calcium score and ultrasound assessment of carotid plaque. This study analysed the predictive value of two ultrasound measures of carotid plaque size: carotid plaque thickness and carotid and intima–media thickness (IMT). Methods and results A total of 6102 asymptomatic persons underwent assessment of conventional risk factors and imaging by carotid ultrasound. Carotid plaque burden (cPB) and maximum carotid plaque thickness (cPTmax) were measured from ‘cross-sectional sweep’ video acquisition of the carotid artery. IMT was measured from distal common carotid artery images. All participants were followed up for ∼3 years, and major cardiovascular events (MACE) were collected and adjudicated. All data were available for 5808 participants, in whom 216 first MACE events were observed. Increasing both cPB and cPTmax were associated with increasing the risk of future MACE when compared with participants without carotid atherosclerosis. Fully adjusted for risk factors, hazard ratios for cPTmax were 1.96 [95% confidence interval (CI) 0.91–4.25, P = 0.015] for primary MACE and 3.13 (95% CI 1.80–5.51, P < 0.001) for secondary MACE, similar to that of cPB. IMT did not improve risk prediction significantly. Non-categorical net reclassification index (NRI) for cPTmax was 0.178 (95% CI 0.027–0.299, P = 0.032) for primary MACE and 0.173 (95% CI 0.109–0.243, P < 0.001) for secondary MACE, which is almost similar to cPB. IMT assessment did not result in significant NRI. Conclusion The simpler cPTmax predicted cardiovascular events similarly to the more comprehensive cPB, whereas IMT did not. Awaiting true 3D ultrasound technology cPTmax may be a simple useful measure for prediction of future ASCVD. carotid ultrasound, carotid plaque, IMT, prediction of cardiovascular events Introduction Despite advances in treatment for atherosclerotic cardiovascular disease (ASCVD), atherosclerosis and its complications remain the leading cause of morbidity and mortality, being the source of the greatest health care costs in the Western world. Although the underlying pathogenesis of atherosclerosis is well understood, predicting who will become affected and suffer clinical disease is not, despite much knowledge about risk factors. In fact, risk prediction derived from risk factors for ASCVD has been shown to perform rather poorly,1 probably because individuals have different tolerance to lifestyle, cholesterol values and so on. Furthermore, health checks did not reduce mortality from ASCVD,2 and individual risk prediction from risk factors for atherosclerosis followed by individual lifestyle counselling has not affected mortality and morbidity.3 An alternative approach for predicting symptomatic ASCVD is based on identifying subclinical atherosclerosis in presumably healthy people. The underlying hypothesis is that without atherosclerosis in the main arteries, the risk of ASCVD is minimal, and vice versa. Several methods for the assessment of asymptomatic atherosclerosis exist and most are based on the fact that atherosclerosis is a generalized disease of the arterial tree.4–7 The most studied methods include coronary artery calcium score (CACS) and carotid ultrasound, the latter mostly used for measuring intima–media thickness (IMT) and lately for assessing carotid plaque. CACS has been documented to predict future coronary and other cardiovascular events for the individual person much better than risk factor-based scoring systems.6–10 The drawbacks of this method include the use of radiation, the relative poor mobility of computed tomography (CT) scanners and that it identifies atherosclerosis at a relatively late stage.11 IMT, in comparison with CACS, has been shown to be a rather weak predictor of future events for groups of people, whereas the value for the individual seems questionable.7,12,13 On the other hand, using ultrasound for the assessment of carotid plaque seems a much stronger predictor than IMT and has recently been shown to have similar predictive value as CACS.10 Moreover, ultrasound, in contrast to CT scanning, is harmless, mobile, and less expensive and may identify atherosclerosis at an early stage. We recently reported that carotid plaque burden (cPB), derived from carotid ultrasound, was similarly predictive as CACS for the development of future cardiovascular events.10 Although cPB is a comprehensive, offline assessment of all carotid plaque throughout the carotid artery, maximum carotid plaque thickness (cPTmax) is a simple measure that in principle can be performed during examination. This study reports the predictive value of cPTmax, carotid IMT, and cPB, all investigated in the High Risk Plaque BioImage Study. Methods The High Risk Plaque BioImage Study has previously been described in detail14 and was a prospective study evaluating cross-sectional associations betwwen imaging and circulating biomarkers and their ability to predict near-term atherothrombotic events (3-year) in asymptomatic subjects (https://clinicaltrials.gov/ct2/show/NCT00738725?term=Bioimage+study&rank=1, NCT00738725). Materials Between January 2008 and June 2009, the BioImage Study enrolled 7687 asymptomatic men aged 55–80 years and women aged 60–80 years who were members of the Humana Health System and residents of the Chicago, IL, USA, or Fort Lauderdale, FL, USA, metropolitan areas. Of these, 6102 subjects entered the imaging arm of the study. Subject eligibility, including freedom from previous history of cardiovascular disease [myocardial infarction (MI), stroke, angina, heart failure, arterial revascularization], was ascertained by baseline review of administrative claims data, followed by telephone interview, and finally by in-person baseline examination and interview. Participants were additionally required to be free of active cancer treatment, any medical condition precluding long-term participation or inability to complete 3-year follow-up, and have no language barrier or inability to comply with study procedures. The BioImage Study was approved by the Western Institutional Review Board, Olympia, WA, USA. Before enrolment, all study participants provided written informed consent and Health Insurance Portability and Accountability Act authorization. Baseline examinations A non-fasting venous blood sample was processed for routine chemistry tests, including serum creatinine and lipid levels. Diabetes mellitus was defined as current use of oral hypoglycaemic agents, insulin, or self-report of the diagnosis. Hypertension was defined as systolic blood pressure > 140 mmHg, diastolic blood pressure > 90 mmHg, or current use of antihypertensive medication. Current smoking status was self-reported. Acquisition of ultrasound data Details regarding ultrasound examination in the BioImage Study were previously published.4 In brief, Philips iU22 ultrasound systems (Philips Healthcare, Bothell, WA, USA) equipped with L12-5 and L9-3 transducers were used for all carotid studies. The scanning protocol included standard imaging of the carotid artery and its branches using generally accepted Doppler criteria for assessment of any degree of stenosis.15 Measurement of IMT was performed offline in the core laboratory from a 10-s video clip of the distal common carotid artery (CCA) recorded from the lateral aspect of the neck in long axis, ensuring the CCA was parallel to the transducer surface (horizontal in the image). For assessment of plaque thickness and plaque burden, the carotid artery was scanned cross-sectionally, slowly moving the transducer manually in the cranial direction from the proximal CCA into the distal internal carotid artery, at an angle perpendicular to the neck. The resulting 10-s digital video clip of this ‘manual 3D’ cross-sectional sweep was examined offline in the core ultrasound laboratory for quantification of plaque. Assessment of IMT, cPB, and cPTmax Ultrasound scans were read by the core laboratory at the Department of Vascular Surgery, Rigshospitalet, University of Copenhagen, Denmark, after all ultrasound data had been acquired. Measurement of IMT was performed with Philips QLAB IMT® plug-in, using the 10-s video clips mentioned above. The reader selected frames with good perpendicular alignment and image quality and adjusted IMT box position if necessary to ensure measurement of mean IMT over the distal 10 mm of the far wall of the CCA. For every participant, 5–10 mean IMT measurements were taken at the same phase of the cardiac cycle (diastole, electrocardiography gated) on each artery (right/left) for every participant. IMT measurements from both arteries were averaged to create an IMT score. Carotid plaque was defined as a focal structure encroaching into the arterial lumen of at least 0.5 mm; or 50% of the surrounding IMT value; or demonstrating a thickness > 1.5 mm, as measured from the media–adventitia interface to the intima–lumen interface.16,17 Assessment of plaque thickness and plaque burden was performed using Philips QLAB quantification software, which was enhanced with specially developed, semi-automated plaque analysis software, QLAB-VPQ® (Vascular Plaque Quantification) (Figure 1). The recorded 10-s cross-sectional sweeps were reviewed for the presence of plaque. Each image showing plaque was outlined as shown in Figure 1. Plaque areas from all images in the cross-sectional sweeps from both the right and left carotid arteries were summed as cPB, a quantitative metric of the total plaque area (mm2) across the length of the visualized carotid artery.4 From the outlined plaque images QLAB–VPQ automatically calculated carotid plaque thickness (cPT), being the radial distance from media/adventitia border to the centre of the vessel (Figure 1). The outlined image with the greatest thickness of the plaque from either side (right and left carotid artery) was used as plaque thickness (cPTmax). Figure 1 View largeDownload slide Segment of carotid artery with a plaque (orange), which is scanned with a linear array transducer as a series of image slices in transverse section (top). Each image is analysed with semi‐automated software to quantify plaque area, plaque greyscale statistics, percent stenosis, and other metrics of interest. The lower left ultrasound image shows the common carotid artery when no plaque is present. The blue border represents the lumen/intima border; the red border represents the media–adventitia boundary. When plaque is present, the yellow line represents lumen/plaque border. Right ultrasound image shows the common carotid artery when plaque is present. The red and blue borders are the same as in previous image, but the orange border represents the boundary of the plaque. cPT is indicated by the light green line. Figure 1 View largeDownload slide Segment of carotid artery with a plaque (orange), which is scanned with a linear array transducer as a series of image slices in transverse section (top). Each image is analysed with semi‐automated software to quantify plaque area, plaque greyscale statistics, percent stenosis, and other metrics of interest. The lower left ultrasound image shows the common carotid artery when no plaque is present. The blue border represents the lumen/intima border; the red border represents the media–adventitia boundary. When plaque is present, the yellow line represents lumen/plaque border. Right ultrasound image shows the common carotid artery when plaque is present. The red and blue borders are the same as in previous image, but the orange border represents the boundary of the plaque. cPT is indicated by the light green line. End points The identification and adjudication of end points have previously been described.10 An independent clinical events committee used source medical records to adjudicate non-fatal and fatal events. Myocardial infarction (MI) was defined according to the 2007 Universal Definition.18 Unstable angina was defined according to the Braunwald classification.19,20 Stroke was defined as a sudden focal neurological deficit of cerebrovascular aetiology persisting beyond 24 h and not due to another identifiable cause, such as a tumour or seizure, or as a clinically relevant new lesion detected on CT or magnetic resonance imaging.21 Deaths were classified as cardiovascular or non-cardiovascular. The primary end point included cardiovascular death, MI, or ischaemic stroke [major adverse cardiovascular events (MACE)]. The secondary MACE end point comprised all-cause death, MI, ischaemic stroke, unstable angina, or coronary revascularization. Statistics Baseline characteristics are presented as mean and standard deviation for continuous variables and number and percentage for categorical variables. Differences in baseline characteristics were compared across cPT groups using analysis of variance for continuous variables and the χ2 test for categorical variables. We categorized cPTmax and cPB as ‘no measurable atherosclerosis’and by increasing tertiles for those with atherosclerosis. Thresholds for the first, second and third tertile of cPTmax were 0.7 mm, 1.84 mm, and 2.55 mm, respectively. Analogous cut-points for cPB were 4.3 mm2, 169.4 mm2 and 536.6 mm2, respectively. We split IMT in quartiles: first quartile 0.43–0.65 mm, second quartile 0.66–0.73 mm, third quartile 0.74–0.84 mm, and fourth quartile 0.85–2.58 mm. The rates of adverse events were estimated at 3 years using the Kaplan–Maier method and compared across groups using the log-rank test. Associations between cPTmax, cPB, IMT, and adverse events were assessed using Cox proportional hazard regression models that included age, race, and gender in Model 1. Model 2 included in addition diabetes mellitus, current smoking, body mass index, systolic blood pressure, antihypertensive agent use, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and use of lipid-lowering drugs. The incremental value of adding the log-transformed cPTmax, cPB, or IMT for risk prediction was evaluated using the metrics of the model: overall fit, calibration, and reclassification. The model fit changes were assessed using likelihood ratio test.22 Calibration was evaluated using a modified version of Hosmer–Lemeshow test.23 Differences in C-index between models and 95% CI were calculated using the method of Newson.24 To assess the net effect of adding a marker to the risk prediction, we calculated the category-free net reclassification index (NRI).25 This study was designed to follow the participants for a minimum of 3 years or until the occurrence of 600 events. All analyses were carried out using Stata version 14 (StataCorp, College Station, TX, USA) and R (version 3.2.1; R Foundation for Statistical Computing, Vienna, Austria) software. Results Of the 6102 individuals, who were included in the BioImage Study, 294 were excluded due to missing covariates and/or imaging data, yielding a final study population of 5808 adults. At the end of the study period, a total of 1139 (19.6%) study participants no longer were Humana members and had not experienced any adverse events during their membership. Median follow-up period among these individuals was 1.1 years. All analyses were repeated after excluding these participants, yielding similar results to the overall cohort. Over a median follow-up period of 2.7 years, there were a total of 216 first MACE events (4.2%) including 108 deaths (2.2%), of which 27 were cardiovascular (0.5%), 34 MIs (0.7%), 30 ischaemic strokes (0.6%), 18 hospitalizations for unstable angina (0.3%), and 79 coronary revascularization procedures (1.6%). Table 1 presents baseline demographics and clinical characteristics for the entire cohort. The average age was ∼69 years, and 56% of participants were female. Table 1 Baseline characteristics for persons with no carotid plaque (no atherosclerosis) and tertiles of carotid maximum plaque thickness (cPTmax)   No atherosclerosis  Tertile 1  Tertile 2  Tertile 3  P-value  Age, years  67.4 ± 5.7  68.4 ± 6.0  69.2 ± 5.9  70.2 ± 5.8  <0.0001  Female  865 (66.5)  911 (60.3)  800 (53.4)  705 (47.0)  <0.0001  White race  827 (63.6)  1163 (77.0)  1143 (76.4)  1168 (77.9)  <0.0001  Diabetes mellitus  173 (13.3)  188 (12.5)  238 (15.9)  258 (17.2)  0.001  Current smoker  53 (9.6)  102 (13.0)  154 (17.3)  187 (19.5)  <0.0001  Hypertension  730 (56.1)  873 (57.8)  982 (65.6)  1029 (68.6)  <0.0001  BMI, kg/m2  29.5 ± 5.8  28.5 ± 5.2  29.2 ± 5.6  29.0 ± 5.5  <0.0001  LDL-C, mg/dL  114.1 ± 32.6  115.6 ± 33.4  113.9 ± 33.3  113.0 ± 33.5  <0.0001  HDL-C, mg/dL  57.8 ± 15.3  56.9 ± 15.4  54.5 ± 15.2  53.8 ± 14.9  <0.0001  Total cholesterol, mg/dL  203.0 ± 38.2  204.7 ± 38.4  201.8 ± 38.6  200.6 ± 39.0  0.0294  Systolic BP, mmHg  136.6 ± 18.2  138.2 ± 18.2  140.4 ± 18.0  142.3 ± 19.2  <0.0001  Diastolic BP, mmHg  79.2 ± 9.3  78.0 ± 8.7  78.1 ± 9.0  77.6 ± 9.2  <0.0001  Lipid-lowering therapy  369 (28.4)  507 (33.6)  572 (38.2)  545 (36.3)  <0.0001  Serum creatinine, mg/dL  0.96 ± 0.18  0.96 ± 0.20  0.98 ± 0.22  1.00 ± 0.22  <0.0001  Framingham riska   <10%  745 (58.3)  773 (52.3)  629 (43.2)  582 (39.5)  <0.0001   10–20%  443 (34.7)  551 (37.3)  591 (40.6)  585 (40.2)     ≥20%  90 (7.04)  154 (10.4)  237 (16.3)  290 (19.9)      No atherosclerosis  Tertile 1  Tertile 2  Tertile 3  P-value  Age, years  67.4 ± 5.7  68.4 ± 6.0  69.2 ± 5.9  70.2 ± 5.8  <0.0001  Female  865 (66.5)  911 (60.3)  800 (53.4)  705 (47.0)  <0.0001  White race  827 (63.6)  1163 (77.0)  1143 (76.4)  1168 (77.9)  <0.0001  Diabetes mellitus  173 (13.3)  188 (12.5)  238 (15.9)  258 (17.2)  0.001  Current smoker  53 (9.6)  102 (13.0)  154 (17.3)  187 (19.5)  <0.0001  Hypertension  730 (56.1)  873 (57.8)  982 (65.6)  1029 (68.6)  <0.0001  BMI, kg/m2  29.5 ± 5.8  28.5 ± 5.2  29.2 ± 5.6  29.0 ± 5.5  <0.0001  LDL-C, mg/dL  114.1 ± 32.6  115.6 ± 33.4  113.9 ± 33.3  113.0 ± 33.5  <0.0001  HDL-C, mg/dL  57.8 ± 15.3  56.9 ± 15.4  54.5 ± 15.2  53.8 ± 14.9  <0.0001  Total cholesterol, mg/dL  203.0 ± 38.2  204.7 ± 38.4  201.8 ± 38.6  200.6 ± 39.0  0.0294  Systolic BP, mmHg  136.6 ± 18.2  138.2 ± 18.2  140.4 ± 18.0  142.3 ± 19.2  <0.0001  Diastolic BP, mmHg  79.2 ± 9.3  78.0 ± 8.7  78.1 ± 9.0  77.6 ± 9.2  <0.0001  Lipid-lowering therapy  369 (28.4)  507 (33.6)  572 (38.2)  545 (36.3)  <0.0001  Serum creatinine, mg/dL  0.96 ± 0.18  0.96 ± 0.20  0.98 ± 0.22  1.00 ± 0.22  <0.0001  Framingham riska   <10%  745 (58.3)  773 (52.3)  629 (43.2)  582 (39.5)  <0.0001   10–20%  443 (34.7)  551 (37.3)  591 (40.6)  585 (40.2)     ≥20%  90 (7.04)  154 (10.4)  237 (16.3)  290 (19.9)    Values are represented as mean ± SD of n (%). a Framingham risk calculated from d’Agostino et al.26 Table 1 Baseline characteristics for persons with no carotid plaque (no atherosclerosis) and tertiles of carotid maximum plaque thickness (cPTmax)   No atherosclerosis  Tertile 1  Tertile 2  Tertile 3  P-value  Age, years  67.4 ± 5.7  68.4 ± 6.0  69.2 ± 5.9  70.2 ± 5.8  <0.0001  Female  865 (66.5)  911 (60.3)  800 (53.4)  705 (47.0)  <0.0001  White race  827 (63.6)  1163 (77.0)  1143 (76.4)  1168 (77.9)  <0.0001  Diabetes mellitus  173 (13.3)  188 (12.5)  238 (15.9)  258 (17.2)  0.001  Current smoker  53 (9.6)  102 (13.0)  154 (17.3)  187 (19.5)  <0.0001  Hypertension  730 (56.1)  873 (57.8)  982 (65.6)  1029 (68.6)  <0.0001  BMI, kg/m2  29.5 ± 5.8  28.5 ± 5.2  29.2 ± 5.6  29.0 ± 5.5  <0.0001  LDL-C, mg/dL  114.1 ± 32.6  115.6 ± 33.4  113.9 ± 33.3  113.0 ± 33.5  <0.0001  HDL-C, mg/dL  57.8 ± 15.3  56.9 ± 15.4  54.5 ± 15.2  53.8 ± 14.9  <0.0001  Total cholesterol, mg/dL  203.0 ± 38.2  204.7 ± 38.4  201.8 ± 38.6  200.6 ± 39.0  0.0294  Systolic BP, mmHg  136.6 ± 18.2  138.2 ± 18.2  140.4 ± 18.0  142.3 ± 19.2  <0.0001  Diastolic BP, mmHg  79.2 ± 9.3  78.0 ± 8.7  78.1 ± 9.0  77.6 ± 9.2  <0.0001  Lipid-lowering therapy  369 (28.4)  507 (33.6)  572 (38.2)  545 (36.3)  <0.0001  Serum creatinine, mg/dL  0.96 ± 0.18  0.96 ± 0.20  0.98 ± 0.22  1.00 ± 0.22  <0.0001  Framingham riska   <10%  745 (58.3)  773 (52.3)  629 (43.2)  582 (39.5)  <0.0001   10–20%  443 (34.7)  551 (37.3)  591 (40.6)  585 (40.2)     ≥20%  90 (7.04)  154 (10.4)  237 (16.3)  290 (19.9)      No atherosclerosis  Tertile 1  Tertile 2  Tertile 3  P-value  Age, years  67.4 ± 5.7  68.4 ± 6.0  69.2 ± 5.9  70.2 ± 5.8  <0.0001  Female  865 (66.5)  911 (60.3)  800 (53.4)  705 (47.0)  <0.0001  White race  827 (63.6)  1163 (77.0)  1143 (76.4)  1168 (77.9)  <0.0001  Diabetes mellitus  173 (13.3)  188 (12.5)  238 (15.9)  258 (17.2)  0.001  Current smoker  53 (9.6)  102 (13.0)  154 (17.3)  187 (19.5)  <0.0001  Hypertension  730 (56.1)  873 (57.8)  982 (65.6)  1029 (68.6)  <0.0001  BMI, kg/m2  29.5 ± 5.8  28.5 ± 5.2  29.2 ± 5.6  29.0 ± 5.5  <0.0001  LDL-C, mg/dL  114.1 ± 32.6  115.6 ± 33.4  113.9 ± 33.3  113.0 ± 33.5  <0.0001  HDL-C, mg/dL  57.8 ± 15.3  56.9 ± 15.4  54.5 ± 15.2  53.8 ± 14.9  <0.0001  Total cholesterol, mg/dL  203.0 ± 38.2  204.7 ± 38.4  201.8 ± 38.6  200.6 ± 39.0  0.0294  Systolic BP, mmHg  136.6 ± 18.2  138.2 ± 18.2  140.4 ± 18.0  142.3 ± 19.2  <0.0001  Diastolic BP, mmHg  79.2 ± 9.3  78.0 ± 8.7  78.1 ± 9.0  77.6 ± 9.2  <0.0001  Lipid-lowering therapy  369 (28.4)  507 (33.6)  572 (38.2)  545 (36.3)  <0.0001  Serum creatinine, mg/dL  0.96 ± 0.18  0.96 ± 0.20  0.98 ± 0.22  1.00 ± 0.22  <0.0001  Framingham riska   <10%  745 (58.3)  773 (52.3)  629 (43.2)  582 (39.5)  <0.0001   10–20%  443 (34.7)  551 (37.3)  591 (40.6)  585 (40.2)     ≥20%  90 (7.04)  154 (10.4)  237 (16.3)  290 (19.9)    Values are represented as mean ± SD of n (%). a Framingham risk calculated from d’Agostino et al.26 Carotid plaque was found in 4507 (78%) individuals. The level of risk factors increased with increasing cPT. Figure 2 shows the crude 3-year event rates for primary and secondary MACE by cPTmax and IMT groups. Trends of higher risk were observed with increasing cPTmax and IMT although slightly weaker for primary MACE. IMT quartiles seemed to separate poorer between low and high risk as did cPTmax (cPTmax log-rank P < 0.001, for primary MACE and P < 0.001, for secondary MACE when compared with IMT log-rank P < 0.013 and 0.009 for primary and secondary MACE) although both statistically significant. Figure 2 View largeDownload slide Crude rates calculated as the Kaplan–Meier estimates at 3 years for primary and secondary major adverse cardiac event(s) (MACE) by carotid plaque tickness (cPT) and intima–media Thickness (IMT). Figure 2 View largeDownload slide Crude rates calculated as the Kaplan–Meier estimates at 3 years for primary and secondary major adverse cardiac event(s) (MACE) by carotid plaque tickness (cPT) and intima–media Thickness (IMT). Table 2 presents hazard ratios (HRs) for primary and secondary MACE associated with cPTmax, cPB, and IMT. Increasing HRs were observed with increasing values for all three, although only statistically significant for cPTmax and cPB after adjustment for all risk factors (Models 1 and 2). HRs for cPTmax predicted similarly to cPB with regard to future adverse events. Table 2 HRs (95% CI) for primary and secondary MACE end points associated with cPTmax, cPB, and carotid IMT   No atherosclerosis  Tertile 1  Tertile 2  Tertile 3  P-value (trend)  Hazard ratios (95% CI) for primary MACE end point   cPTmax    Model 1  1.0 (ref)  0.88 (0.36–2.19)  2.41 (1.13–5.14)  2.52 (1.18–5.35)  0.001    Model 2  1.0 (ref)  0.85 (0.34–2.11)  2.09 (0.97–4.50)  1.96 (0.91–4.25)  0.015   cPB    Model 1  1.0 (ref)  0.87 (0.36–2.10)  1.56 (0.72–3.36)  2.85 (1.39–5.82)  <0.001    Model 2  1.0 (ref)  0.78 (0.31–1.91)  1.45 (0.67–3.14)  2.36 (1.13–4.92)  0.030   IMT    Model 1  1.0 (ref)  1.16 (0.57–2.34)  1.06 (0.52–2.18)  1.82 (0.95–3.50)  0.066    Model 2  1.0 (ref)  1.10 (0.54–2.23)  0.87 (0.42–1.18)  1.38 (0.71–2.70)  0.372  Hazard ratios (95% CI) for secondary MACE end point   cPT max    Model 1  1.0 (ref)  1.71 (0.94–3.10)  3.59 (2.09–6.19)  3.73 (2.16–6.41)  <0.001    Model 2  1.0 (ref)  1.66 (0.91–3.01)  3.18 (1.83–5.51)  3.13 (1.80–5.51)  0.001   cPB    Model 1  1.0 (ref)  1.59 (0.92–2.74)  2.27 (1.36–3.79)  3.41 (2.08–5.58)  <0.001    Model 2  1.0 (ref)  1.53 (0.89–2.65)  2.14 (1.28–3.59)  2.87 (1.73–4.74)  <0.001   IMT    Model 1  1.0 (ref)  0.85 (0.56–1.31)  1.03 (0.69–1.55)  1.36 (0.92–2.00)  0.052    Model 2  1.0 (ref)  0.84 (0.55–1.28)  0.90 (0.59–1.36)  1.09 (0.73–1.62)  0.502    No atherosclerosis  Tertile 1  Tertile 2  Tertile 3  P-value (trend)  Hazard ratios (95% CI) for primary MACE end point   cPTmax    Model 1  1.0 (ref)  0.88 (0.36–2.19)  2.41 (1.13–5.14)  2.52 (1.18–5.35)  0.001    Model 2  1.0 (ref)  0.85 (0.34–2.11)  2.09 (0.97–4.50)  1.96 (0.91–4.25)  0.015   cPB    Model 1  1.0 (ref)  0.87 (0.36–2.10)  1.56 (0.72–3.36)  2.85 (1.39–5.82)  <0.001    Model 2  1.0 (ref)  0.78 (0.31–1.91)  1.45 (0.67–3.14)  2.36 (1.13–4.92)  0.030   IMT    Model 1  1.0 (ref)  1.16 (0.57–2.34)  1.06 (0.52–2.18)  1.82 (0.95–3.50)  0.066    Model 2  1.0 (ref)  1.10 (0.54–2.23)  0.87 (0.42–1.18)  1.38 (0.71–2.70)  0.372  Hazard ratios (95% CI) for secondary MACE end point   cPT max    Model 1  1.0 (ref)  1.71 (0.94–3.10)  3.59 (2.09–6.19)  3.73 (2.16–6.41)  <0.001    Model 2  1.0 (ref)  1.66 (0.91–3.01)  3.18 (1.83–5.51)  3.13 (1.80–5.51)  0.001   cPB    Model 1  1.0 (ref)  1.59 (0.92–2.74)  2.27 (1.36–3.79)  3.41 (2.08–5.58)  <0.001    Model 2  1.0 (ref)  1.53 (0.89–2.65)  2.14 (1.28–3.59)  2.87 (1.73–4.74)  <0.001   IMT    Model 1  1.0 (ref)  0.85 (0.56–1.31)  1.03 (0.69–1.55)  1.36 (0.92–2.00)  0.052    Model 2  1.0 (ref)  0.84 (0.55–1.28)  0.90 (0.59–1.36)  1.09 (0.73–1.62)  0.502  Model 1 was adjusted for age, race, and gender. Model 2 was additionally adjusted for diabetes mellitus, current smoking, body mass index, systolic blood pressure, antihypertensive agent use, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and use of lipid-lowering drugs. cPTmax, maximum carotid plaque thickness; CI, confidence interval; cPB, carotid plaque burden; HR, hazard ratio; IMT, intima–media thickness (quartile); MACE, major adverse cardiovascular events. Table 2 HRs (95% CI) for primary and secondary MACE end points associated with cPTmax, cPB, and carotid IMT   No atherosclerosis  Tertile 1  Tertile 2  Tertile 3  P-value (trend)  Hazard ratios (95% CI) for primary MACE end point   cPTmax    Model 1  1.0 (ref)  0.88 (0.36–2.19)  2.41 (1.13–5.14)  2.52 (1.18–5.35)  0.001    Model 2  1.0 (ref)  0.85 (0.34–2.11)  2.09 (0.97–4.50)  1.96 (0.91–4.25)  0.015   cPB    Model 1  1.0 (ref)  0.87 (0.36–2.10)  1.56 (0.72–3.36)  2.85 (1.39–5.82)  <0.001    Model 2  1.0 (ref)  0.78 (0.31–1.91)  1.45 (0.67–3.14)  2.36 (1.13–4.92)  0.030   IMT    Model 1  1.0 (ref)  1.16 (0.57–2.34)  1.06 (0.52–2.18)  1.82 (0.95–3.50)  0.066    Model 2  1.0 (ref)  1.10 (0.54–2.23)  0.87 (0.42–1.18)  1.38 (0.71–2.70)  0.372  Hazard ratios (95% CI) for secondary MACE end point   cPT max    Model 1  1.0 (ref)  1.71 (0.94–3.10)  3.59 (2.09–6.19)  3.73 (2.16–6.41)  <0.001    Model 2  1.0 (ref)  1.66 (0.91–3.01)  3.18 (1.83–5.51)  3.13 (1.80–5.51)  0.001   cPB    Model 1  1.0 (ref)  1.59 (0.92–2.74)  2.27 (1.36–3.79)  3.41 (2.08–5.58)  <0.001    Model 2  1.0 (ref)  1.53 (0.89–2.65)  2.14 (1.28–3.59)  2.87 (1.73–4.74)  <0.001   IMT    Model 1  1.0 (ref)  0.85 (0.56–1.31)  1.03 (0.69–1.55)  1.36 (0.92–2.00)  0.052    Model 2  1.0 (ref)  0.84 (0.55–1.28)  0.90 (0.59–1.36)  1.09 (0.73–1.62)  0.502    No atherosclerosis  Tertile 1  Tertile 2  Tertile 3  P-value (trend)  Hazard ratios (95% CI) for primary MACE end point   cPTmax    Model 1  1.0 (ref)  0.88 (0.36–2.19)  2.41 (1.13–5.14)  2.52 (1.18–5.35)  0.001    Model 2  1.0 (ref)  0.85 (0.34–2.11)  2.09 (0.97–4.50)  1.96 (0.91–4.25)  0.015   cPB    Model 1  1.0 (ref)  0.87 (0.36–2.10)  1.56 (0.72–3.36)  2.85 (1.39–5.82)  <0.001    Model 2  1.0 (ref)  0.78 (0.31–1.91)  1.45 (0.67–3.14)  2.36 (1.13–4.92)  0.030   IMT    Model 1  1.0 (ref)  1.16 (0.57–2.34)  1.06 (0.52–2.18)  1.82 (0.95–3.50)  0.066    Model 2  1.0 (ref)  1.10 (0.54–2.23)  0.87 (0.42–1.18)  1.38 (0.71–2.70)  0.372  Hazard ratios (95% CI) for secondary MACE end point   cPT max    Model 1  1.0 (ref)  1.71 (0.94–3.10)  3.59 (2.09–6.19)  3.73 (2.16–6.41)  <0.001    Model 2  1.0 (ref)  1.66 (0.91–3.01)  3.18 (1.83–5.51)  3.13 (1.80–5.51)  0.001   cPB    Model 1  1.0 (ref)  1.59 (0.92–2.74)  2.27 (1.36–3.79)  3.41 (2.08–5.58)  <0.001    Model 2  1.0 (ref)  1.53 (0.89–2.65)  2.14 (1.28–3.59)  2.87 (1.73–4.74)  <0.001   IMT    Model 1  1.0 (ref)  0.85 (0.56–1.31)  1.03 (0.69–1.55)  1.36 (0.92–2.00)  0.052    Model 2  1.0 (ref)  0.84 (0.55–1.28)  0.90 (0.59–1.36)  1.09 (0.73–1.62)  0.502  Model 1 was adjusted for age, race, and gender. Model 2 was additionally adjusted for diabetes mellitus, current smoking, body mass index, systolic blood pressure, antihypertensive agent use, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and use of lipid-lowering drugs. cPTmax, maximum carotid plaque thickness; CI, confidence interval; cPB, carotid plaque burden; HR, hazard ratio; IMT, intima–media thickness (quartile); MACE, major adverse cardiovascular events. Tables 3 and 4 present the impact on model performance of adding cPTmax, cPB, and IMT to the baseline conventional risk factor (CRF) Model 1. All three parameters significantly improved model fit although IMT the least. cPTmax and cPB significantly improved category-free NRIs, both for primary and secondary MACE, when added to the baseline CRF model, whereas IMT did not. The model performance of adding the ultrasound parameters to only gender, age, and race yielded similar results. Table 3 Impact on model performance by adding cPTmax, cPB and carotid IMT Model  Model fita   Discriminationb  Calibration   χ2  P-value  Change in C-index (95% CI)  χ2  P-value  Impact on model performance for prediction of primary MACE end point   Model (CRF)  41.5  Ref model  Ref model  4.3  0.37   Model (CRF) + cPT  50.7  <0.001  0.01 (−0.02 to 0.04)  6.3  0.18   Model (CRF) + cPB  50.1  0.003  0.01 (−0.02 to 0.04)  3.4  0.49   Model (CRF) + IMT  45.3  <0.001  −0.00 (−0.01 to 0.01)  3.8  0.44  Impact on model performance for prediction of secondary MACE end point   Model (CRF)  92.3  Ref model  Ref model  7.8  0.09   Model (CRF) + cPT  128.9  <0.001  0.02 (0.001 to 0.04)  2.7  0.61   Model (CRF) + cPB  115.6  <0.001  0.03 (0.006 to 0.05)  4.6  0.33   Model (CRF) + IMT  98.4  <0.001  −0.00 (−0.004 to 0.003)  6.8  0.15  Model  Model fita   Discriminationb  Calibration   χ2  P-value  Change in C-index (95% CI)  χ2  P-value  Impact on model performance for prediction of primary MACE end point   Model (CRF)  41.5  Ref model  Ref model  4.3  0.37   Model (CRF) + cPT  50.7  <0.001  0.01 (−0.02 to 0.04)  6.3  0.18   Model (CRF) + cPB  50.1  0.003  0.01 (−0.02 to 0.04)  3.4  0.49   Model (CRF) + IMT  45.3  <0.001  −0.00 (−0.01 to 0.01)  3.8  0.44  Impact on model performance for prediction of secondary MACE end point   Model (CRF)  92.3  Ref model  Ref model  7.8  0.09   Model (CRF) + cPT  128.9  <0.001  0.02 (0.001 to 0.04)  2.7  0.61   Model (CRF) + cPB  115.6  <0.001  0.03 (0.006 to 0.05)  4.6  0.33   Model (CRF) + IMT  98.4  <0.001  −0.00 (−0.004 to 0.003)  6.8  0.15  cPB, carotid plaque burden; cPT, carotid plaque thickness; CRF, conventional risk factor; IMT, intima–media thickness. aChanges in model fit assessed using the likelihood ratio test.23 bDifferences in c-index between models and 95% CI were calculated using the method of Newson.24 Table 3 Impact on model performance by adding cPTmax, cPB and carotid IMT Model  Model fita   Discriminationb  Calibration   χ2  P-value  Change in C-index (95% CI)  χ2  P-value  Impact on model performance for prediction of primary MACE end point   Model (CRF)  41.5  Ref model  Ref model  4.3  0.37   Model (CRF) + cPT  50.7  <0.001  0.01 (−0.02 to 0.04)  6.3  0.18   Model (CRF) + cPB  50.1  0.003  0.01 (−0.02 to 0.04)  3.4  0.49   Model (CRF) + IMT  45.3  <0.001  −0.00 (−0.01 to 0.01)  3.8  0.44  Impact on model performance for prediction of secondary MACE end point   Model (CRF)  92.3  Ref model  Ref model  7.8  0.09   Model (CRF) + cPT  128.9  <0.001  0.02 (0.001 to 0.04)  2.7  0.61   Model (CRF) + cPB  115.6  <0.001  0.03 (0.006 to 0.05)  4.6  0.33   Model (CRF) + IMT  98.4  <0.001  −0.00 (−0.004 to 0.003)  6.8  0.15  Model  Model fita   Discriminationb  Calibration   χ2  P-value  Change in C-index (95% CI)  χ2  P-value  Impact on model performance for prediction of primary MACE end point   Model (CRF)  41.5  Ref model  Ref model  4.3  0.37   Model (CRF) + cPT  50.7  <0.001  0.01 (−0.02 to 0.04)  6.3  0.18   Model (CRF) + cPB  50.1  0.003  0.01 (−0.02 to 0.04)  3.4  0.49   Model (CRF) + IMT  45.3  <0.001  −0.00 (−0.01 to 0.01)  3.8  0.44  Impact on model performance for prediction of secondary MACE end point   Model (CRF)  92.3  Ref model  Ref model  7.8  0.09   Model (CRF) + cPT  128.9  <0.001  0.02 (0.001 to 0.04)  2.7  0.61   Model (CRF) + cPB  115.6  <0.001  0.03 (0.006 to 0.05)  4.6  0.33   Model (CRF) + IMT  98.4  <0.001  −0.00 (−0.004 to 0.003)  6.8  0.15  cPB, carotid plaque burden; cPT, carotid plaque thickness; CRF, conventional risk factor; IMT, intima–media thickness. aChanges in model fit assessed using the likelihood ratio test.23 bDifferences in c-index between models and 95% CI were calculated using the method of Newson.24 Table 4 Effect on categori-free net reclassification index (MRI) of adding cPTmac, cPB and carotid IMT Model  Reclassification   NRI  (95% CI)  P-value  Impact on model performance for prediction of primary MACE end point   Model 1 (CRF)  Ref model       Model 1 + cPTmax  0.178  (0.027–0.299)  0.032   Model 1 + cPB  0.228  (0.002–0.320)  0.040   Model 1 + IMT  0.016  (−0.095–0.146)  0.798  Impact on model performance for prediction of secondary MACE end point   Model 1 (CRF)  Ref model       Model 1 + cPTmax  0.173  (0.109–0.243)  <0.0001   Model 1 + cPB  0.174  (0.102–0.245)  <0.0001   Model 1 + IMT  0.015  (−0.060–0.100)  0.559  Model  Reclassification   NRI  (95% CI)  P-value  Impact on model performance for prediction of primary MACE end point   Model 1 (CRF)  Ref model       Model 1 + cPTmax  0.178  (0.027–0.299)  0.032   Model 1 + cPB  0.228  (0.002–0.320)  0.040   Model 1 + IMT  0.016  (−0.095–0.146)  0.798  Impact on model performance for prediction of secondary MACE end point   Model 1 (CRF)  Ref model       Model 1 + cPTmax  0.173  (0.109–0.243)  <0.0001   Model 1 + cPB  0.174  (0.102–0.245)  <0.0001   Model 1 + IMT  0.015  (−0.060–0.100)  0.559  NRI calculated using the category-free version.25 cPB, carotid plaque burden; CI, confidence interval; cPT, carotid plaque thickness; CRF, conventional risk factor; IMT, intima–media thickness; MACE, major adverse cardiovascular events; NRI, net reclassification index. Table 4 Effect on categori-free net reclassification index (MRI) of adding cPTmac, cPB and carotid IMT Model  Reclassification   NRI  (95% CI)  P-value  Impact on model performance for prediction of primary MACE end point   Model 1 (CRF)  Ref model       Model 1 + cPTmax  0.178  (0.027–0.299)  0.032   Model 1 + cPB  0.228  (0.002–0.320)  0.040   Model 1 + IMT  0.016  (−0.095–0.146)  0.798  Impact on model performance for prediction of secondary MACE end point   Model 1 (CRF)  Ref model       Model 1 + cPTmax  0.173  (0.109–0.243)  <0.0001   Model 1 + cPB  0.174  (0.102–0.245)  <0.0001   Model 1 + IMT  0.015  (−0.060–0.100)  0.559  Model  Reclassification   NRI  (95% CI)  P-value  Impact on model performance for prediction of primary MACE end point   Model 1 (CRF)  Ref model       Model 1 + cPTmax  0.178  (0.027–0.299)  0.032   Model 1 + cPB  0.228  (0.002–0.320)  0.040   Model 1 + IMT  0.016  (−0.095–0.146)  0.798  Impact on model performance for prediction of secondary MACE end point   Model 1 (CRF)  Ref model       Model 1 + cPTmax  0.173  (0.109–0.243)  <0.0001   Model 1 + cPB  0.174  (0.102–0.245)  <0.0001   Model 1 + IMT  0.015  (−0.060–0.100)  0.559  NRI calculated using the category-free version.25 cPB, carotid plaque burden; CI, confidence interval; cPT, carotid plaque thickness; CRF, conventional risk factor; IMT, intima–media thickness; MACE, major adverse cardiovascular events; NRI, net reclassification index. Figure 3 shows HRs for cPTmax, IMT, and cPB. Both cPTmax and cPB had almost similar, strong prediction of primary and secondary MACE, but IMT did not. There was no difference in predictive value of cPB and cPTmax (primary end point P-value = 0.4279; secondary end point P-value = 0.7646). cPTsum, being the sum of the highest value from either side, was also analysed and found almost similarly predictive as cPTmax (data not shown). Figure 3 View largeDownload slide Hazard ratios for primary and secondary MACE, unadjusted and adjusted according to Model 1 for cPTmax, cPB, and IMT. Figure 3 View largeDownload slide Hazard ratios for primary and secondary MACE, unadjusted and adjusted according to Model 1 for cPTmax, cPB, and IMT. Discussion The presence of plaque in the carotid artery has already been identified as an independent predictor for future cardiovascular events9,27; however, in this study, we quantified carotid plaque from cross-sectional images (short axis) and found it to be stepwise predictive of future ASCVD; the thicker the plaque the higher the risk. Considering progression of atherosclerosis/growth of plaques, the association between cPTmax and risk of atherosclerotic complications is not surprising. Lacking true 3D technology, we introduced assessment of cPB, summarizing plaque areas from serial cross-sectional 2D images4 and showed that this approach could predict future adverse events similarly to CACS.10 In this study, cPTmax performed more or less similar to cPB. Despite that cPB was a more comprehensive assessment tool, taking into account that plaques may have different shapes and that there may be more than one plaque, cPTmax being much simpler to assess, performed similarly. This can certainly relate to the nature of the 10-s, cross-sectional sweep without control of speed of movement (lack of true 3D), which potentially introduces inaccuracy of cPB. However, the 3D nature of the cross-sectional sweep, ensuring data capture from the entire cervical portion of the carotid artery, therefore allowing for identification of any plaque, may be of importance for our findings. Further, the cross-sectional image shows from which anatomical location the measurement should be made to measure the true radial distance from the media/adventitia border to the centre of the artery, where the plaque is thickest. Of course, given that the image is acquired at a perpendicular angle with respect to the long axis of the artery. Rundek et al.28 also measured cPT, however, acquired from images in long axis. They also showed cPT to be predictive of future cardiovascular events, however, in a different population as 21% already had established ‘cardiac disease’ at baseline. The theoretical advantage of our technique is that only in cross-section can the true radial distance, plaque thickness, be measured. Due to focusing of the ultrasound beam to obtain sharp images, only a thin ‘slice’ of the artery/plaque is visualized, potentially not reflecting the true size of the plaque. In principle, cPTmax can be assessed directly from frozen ultrasound images on the ultrasound machine without the need for offline analyses. Future true 3D applications might improve prediction not only for cPTmax, since it can then be estimated perpendicular to the centre line of the vessel, but also allow plaque volume measurements. Reproducibility should improve from 3D imaging, allowing for accurate repetitive scans over time.29 In this manner, evaluation of anti-atherosclerotic treatment might improve being based on imaging observing changes in atherosclerosis amount rather than based on changes in blood tests.30,31 An alternative to cPTmax is cPTsum: the sum of the thickest plaque in both carotid arteries (right and left). The latter was also analysed and found to have almost similar predictive value (data not shown); however, since cPTmax is the simplest, we chose to analyse this primarily. In our study, we used semi-automated software outlining plaque in all images, automatically calculating plaque thickness as described above (Figure 1). Other methods of quantifying carotid plaque size by ultrasound have shown similar results, e.g. measuring plaque area on 2D images acquired in long axis.32,33 Whether this method is similar to, superior, or inferior to cPT cannot be judged as acquisition and analysis methods and populations are not directly comparable. However, data on plaque area are based on images in long axis with the limitations referred to the above, namely that only part of the plaque is visualized due to focusing of the ultrasound beam. Although patients with the highest IMT values experienced more adverse events than those with low IMT, the predictive value was non-significant when adjusted for traditional risk factors. Especially, prediction of low risk was inferior to that of no plaque. That IMT did not predict risk very well is not surprising, considering the data available regarding IMT today.7,10,12,34,35 Our study may be criticized for only acquiring IMT from one angle; however, apart from that, the methodology used was similar to the current recommendations. The important issue of IMT, and why it is not as predictive as presence and quantification of plaque, is the approach of only acquiring data from a given, small anatomical location, i.e. distal CCA, rather than interrogating the entire vessel for the presence of atherosclerosis. Other criticism of IMT has been raised, i.e. that thickening of the intima–media complex may be a result of hypertension rather than atherosclerosis. Most importantly, IMT does not predict risk beyond that of traditional risk factors in the individual person,7,10,34 and therefore, serial IMT measurements are not recommended.16,17,36 Given the advantages of ultrasound being harmless, portable, and inexpensive, this technique may be an alternative to CT based, despite its potential disadvantages. On the other hand, ultrasound is operator dependent, thus training and certification remains essential. However, 3D technology will ease correct acquisition using simultaneous imaging of several planes. In addition, ultrasound can identify soft and small plaques and therefore potentially identify earlier stages of disease than CACS, widening the window for prevention further. Another approach, potentially improving predictability, is to add observations from other anatomical locations, i.e. arteries of lower extremity where atherosclerosis may be more prevalent.5,37 Our study had limitations with regard to the method of participant follow-up and ultrasound technology. In case of the former, these have been described in detail4,10; however, in brief, the reliance on health insurance claims to identify adverse events may have resulted in a lower-than-expected rate of adverse events. However, comparing with other methods across the same population, this should have not affected our results. Longer period of follow-up would have strengthened the study. With respect to the ultrasound technology used in the BioImage Study, limitations of these have already been discussed in detail.4,10 However, specifically for this study, acquisition of cross-sectional images were assumed to be at 90° with respect to the long axis of the carotid artery; however, if acquired at non-perpendicular angles, error could have been introduced with measurement of too high values for cPTmax (and cPB). Specifically at the location of the carotid bifurcation, where the internal carotid artery branches off, this is where plaque often develops and ‘angle inaccuracy’ might occur. Limitations with regard to IMT have been discussed above. cPTmax values were automatically derived from the semi-automated outlines of plaques performed using QLAB–VPQ. Although the operator analysing the 10-s ultrasound video’s reviewed all outlines, especially in plaques reflecting ultrasound poorly (echo-weak/lucent plaques), or in case of calcification and/or severe stenosis, outlining was difficult and could be inaccurate. In conclusion, we found the simple cPTmax being similarly predictive as cPB for the development of symptomatic ASCVD. The presented data add to the accumulating evidence that quantification of carotid plaque by ultrasound may contribute significantly to personalized ASCVD risk prediction. Funding The High-Risk Plaque Initiative is a pre-competitive industry collaboration funded by BG Medicine, Abbott Vascular, AstraZeneca, Merck & Co, Philips, and Takeda. Conflict of interest: H.S. has received speaker honorarium from Philips Ultrasound, and Department of Vascular Surgery, Rigshospitalet, University of Copenhagen, Denmark, has received research grant from Philips Ultrasound. References 1 Mortensen MB, Sivesgaard K, Jensen HK, Comuth W, Kanstrup H, Gotzsche O et al.   Traditional SCORE-based health check fails to identify individuals who develop acute myocardial infarction. Dan Med J  2013; 60: A4629. Google Scholar PubMed  2 Jørgensen T, Jacobsen RK, Toft U, Aadahl M, Glümer C, Pisinger C. Effect of screening and lifestyle counseling on incidence of ischemic heart disease in general population: Inter99 randomized trial. BMJ  2014; 348: g3617. Google Scholar CrossRef Search ADS PubMed  3 Krogsbøll LT, Jørgensen KJ, Grønhøj Larsen C, Gøtzsche PC. General health checks in adults for reducing morbidity and mortality from disease. Cochrane Database Syst Rev  2012; 10: CD009009. doi: 10.1002/14651858.CD009009.pub2. Google Scholar PubMed  4 Sillesen H, Muntendam P, Adourian A, Entrekin R, Garcia M, Falk E et al.   Carotid plaque burden as a measure of subclinical atherosclerosis: comparison with other tests for subclinical arterial disease in the High Risk Plaque BioImage study. JACC Cardivasc Imaging  2012; 5: 681– 9. Google Scholar CrossRef Search ADS   5 Fernández-Friera L, Peñalvo JL, Fernández-Ortiz A, Ibañez B, López-Melgar B, Laclaustra M et al.   Prevalence, vascular distribution, and multiterritorial extent of subclinical atherosclerosis in a middle-aged cohort the PESA (Progression of Early Subclinical Atherosclerosis) Study. Circulation  2015; 131: ß2104– 113. Google Scholar CrossRef Search ADS   6 van der Meer IM, Bots ML, Hofman A, del Sol AI, van der Kuip DA, Witteman JC. Predictive value of noninvasive measures of atherosclerosis for incident myocardial infarction: the Rotterdam Study. Circulation  2004; 109: 1089– 94. Google Scholar CrossRef Search ADS PubMed  7 Yeboah J, McClelland RL, Polonsky TS, Burke GL, Sibley CT, O’Leary D et al.   Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate risk individuals. JAMA  2012; 308: 788– 95. Google Scholar CrossRef Search ADS PubMed  8 Kavousi M, Elias-Smale S, Rutten JH, Leening MJ, Vliegenthart R, Verwoert GC et al.   Evaluation of newer risk markers for coronary heart disease risk classification: a cohort study. Ann Intern Med  2012; 156: 438– 44. Google Scholar CrossRef Search ADS PubMed  9 Nambi V, Chambless L, Folsom AR, He M, Hu Y, Mosley T et al.   Carotid intima-media thickness and presence or absence of plaque improves prediction of coronary heart disease risk: the ARIC (Atherosclerosis Risk In Communities) study. J Am Coll Cardiol  2010; 55: 1600– 7. Google Scholar CrossRef Search ADS PubMed  10 Baber U, Mehran R, Sartori S, Schoos MM, Sillesen H, Muntendam P et al.   Prevalence, impact, and predictive value of detecting subclinical coronary and carotid atherosclerosis in asymptomatic adults. J Am Coll Cardiol  2015; 65: 1065– 74. Google Scholar CrossRef Search ADS PubMed  11 Otsuka F, Sakakura K, Yahagi K, Joner M, Virmani R. Has our understanding of calcification in human coronary atherosclerosis progressed? Arterioscler Thromb Vasc Biol  2014; 34: 724– 36. Google Scholar CrossRef Search ADS PubMed  12 Lorenz MW, Schaefer C, Steinmetz H, Sitzer M. Is carotid intima media thickness useful for individual prediction of cardiovascular risk? Ten-year results from the Carotid Atherosclerosis Progression Study (CAPS). Eur Heart J  2010; 31: 2041– 8. Google Scholar CrossRef Search ADS PubMed  13 Den Ruijter HM, Peters SAE, Anderson TJ, Britton AR, Dekker JM, Eijkemans MJ et al.   Common carotid intima-media thickness measurements in cardiovascular risk prediction: a meta-analysis. JAMA  2012; 308: 796– 803. Google Scholar CrossRef Search ADS PubMed  14 Muntendam P, McCall C, Sanz J, Falk E, Fuster V; High-Risk Plaque Initiative. The BioImage study: novel approaches to risk assessment in the primary prevention of atherosclerotic cardiovascular disease–study design and objectives. Am Heart J  2010; 160: 49– 57. Google Scholar CrossRef Search ADS PubMed  15 Oates CP, Naylor AR, Hartshorne T, Charles SM, Fail T, Humphries K et al.   Joint recommendations for reporting carotid ultrasound investigation in the United Kingdom. Eur J Vasc Endovasc Surg  2009; 37: 51– 61. Google Scholar CrossRef Search ADS   16 Stein JH, Korcarz CE, Hurst RT, Lonn E, Kendall CB, Mohler ER et al.   Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: a consensus statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force. Endorsed by the Society for Vascular Medicine. J Am Soc Echocardiogr  2008; 21: 93– 111. Google Scholar CrossRef Search ADS PubMed  17 Touboul PJ, Hennerici MG, Meairs S, Adams H, Amarenco P, Bornstein N et al.   Mannheim carotid intima-media thickness consensus (2004-2006). An update on behalf of the Advisory Board of the 3rd and 4th Watching the Risk Symposium, 13th and 15th European Stroke Conferences, Mannheim, Germany, 2004, and Brussels, Belgium, 2006. Cerebrovasc Dis  2007; 23: 75– 80. Google Scholar CrossRef Search ADS PubMed  18 Thygesen K, Alpert JS, White HD; Joint ESC/ACCF/AHA/WHF Task Force for the Redefinition of Myocardial Infarction. Universal definition of myocardial infarction. J Am Coll Cardiol  2007; 50: 2173– 95. Google Scholar CrossRef Search ADS PubMed  19 Braunwald E. Unstable angina. A classification. Circulation  1989; 80: 410– 4. Google Scholar CrossRef Search ADS PubMed  20 Braunwald E. Unstable angina: an etiologic approach to management. Circulation  1998; 98: 2219– 22. Google Scholar CrossRef Search ADS PubMed  21 Homma S, Thompson JL, Pullicino PM, Levin B, Freudenberger RS, Teerlink JR; for the WsARCEF Investigators et al.   Warfarin and aspirin in patients with heart failure and sinus rhythm. N Engl J Med  2012; 366: 1859– 69. Google Scholar CrossRef Search ADS PubMed  22 Harrell F. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis . New York, NY: Springer; 2001. 23 May S, Hosmer DW. A simplified method of calculating an overall goodness-of-fit test for the Cox proportional hazards model. Lifetime Data Anal  1998; 4: 109– 20. Google Scholar CrossRef Search ADS PubMed  24 Newson R. Comparing the predictive powers of survival models using Harrell’s C or Somers’ D. Stata J  2010; 10: 339– 58. 25 Pencina MJ, D’agostino RB, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med  2011; 30: 11– 21. Google Scholar CrossRef Search ADS PubMed  26 D’Agostino RBSr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM et al.   General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation  2008; 117: 743– 53. Google Scholar CrossRef Search ADS PubMed  27 Polak JF, Pencina MJ, Pencina KM, O’Donnell CJ, Wolf PA, D’Agostino RB. Carotid-wall intima–media thickness and cardiovascular events. N Engl J Med  2011; 365: 213– 21. Google Scholar CrossRef Search ADS PubMed  28 Rundek T, Arif H, Boden-Albala B, Elkind MS, Paik MC, Sacco RL. Carotid plaque, a subclinical precursorof vascular events: the Northern Manhattan Study. Neurology  2008; 70: 1200– 7. Google Scholar CrossRef Search ADS PubMed  29 Græbe M, Entrekin R, Collet-Billon A, Harrison G, Sillesen H. Reproducibility of two 3-D ultrasound carotid plaque quantification methods. Ultrasound Med Biol  2014; 40: 1641– 9. Google Scholar CrossRef Search ADS PubMed  30 Spence JD. Measurement of carotid plaque burden. JAMA Neurol  2015; 72: 383– 4. Google Scholar CrossRef Search ADS PubMed  31 Wannarong T, Parraga G, Buchanan D, Fenster A, House AA, Hackam DG et al.   Progression of carotid plaque volume predicts cardiovascular events. Stroke  2013; 44: 1859– 65. Google Scholar CrossRef Search ADS PubMed  32 Spence JD, Eliasziw M, Dicicco M, Hackam DG, Galil R, Lohmann T. Carotid plaque area. A tool for targeting and evaluating vascular preventive therapy. Stroke  2002; 33: 2916– 22. Google Scholar CrossRef Search ADS PubMed  33 Mathiesen EB, Johnsen SH, Wilsgaard T, Bønaa KH, Løchen ML, Njølstad I et al.   Carotid plaque area and intima-media thickness in prediction of first-ever ischemic stroke. A 10-year follow-up of 6584 men and women: the Tromsø Study. Stroke  2011; 42: 972– 8. Google Scholar CrossRef Search ADS PubMed  34 van den Oord SC, Sijbrands EJ, ten Kate GL, van Klaveren D, van Domburg RT, van der Steen AF et al.   Carotid intima-media thickness for cardiovascular risk assessment: systematic review and meta-analysis. Atherosclerosis  2013; 228: 1– 11. Google Scholar CrossRef Search ADS PubMed  35 Vlachopoulos C, Xaplanteris P, Aboyans V, Brodmann M, Cífkov R, Cosentino F et al.   The role of vascular biomarkers for primary and secondary prevention. A position paper from the European Society of Cardiology Working Group on peripheral circulation. Atherosclerosis  2015; 241: 507– 32. Google Scholar CrossRef Search ADS PubMed  36 Lorenz MW, Polak JF, Kavousi M, Mathiesen EB, Völzke H, Tuomainen TP et al.   Carotid intima-media thickness progression to predict cardiovascular events in the general population (the PROG-IMT collaborative project): a meta-analysis of individual participant data. Lancet  2012; 379: 2053– 62. Google Scholar CrossRef Search ADS PubMed  37 Belcaro G, Nicolaides AN, Ramaswami G, Cesarone MR, De Sanctis M, Incandela L et al.   Carotid and femoral ultrasound morphology screening and cardiovascular events in low risk subjects: a 10-year follow-up study (the CAFES-CAVE study). Atherosclerosis  2001; 156: 379– 87. Google Scholar CrossRef Search ADS PubMed  Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For permissions, please email: journals.permissions@oup.com.

Journal

European Heart Journal – Cardiovascular ImagingOxford University Press

Published: Oct 20, 2017

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off