Discriminating Established Cardiovascular Disease Using a Novel Multiterritory Ultrasound Plaque Burden Measure (wTPT): Findings From the P-SONAR Study

Discriminating Established Cardiovascular Disease Using a Novel Multiterritory Ultrasound Plaque Burden Measure (wTPT): Findings From the P-SONAR Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Discriminating Established Cardiovascular Disease Using a Novel Multiterritory Ultrasound Plaque Burden Measure (wTPT): Findings From the P-SONAR Study Gunnar Austad, Jonn Terje Geitung, Owen Thomas, Serena Tonstad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8215491/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Apr, 2026 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 11 You are reading this latest preprint version Abstract Objective Ultrasound-imaging of subclinical atherosclerosis may refine cardiovascular (CV) risk assessment, but quantification methods vary and often include carotid arteries only. Because atherosclerosis is multiterritory, global (carotid–femoral) plaque quantification may better reflect systemic burden. We examined whether a novel multiterritory measure, weighted total plaque thickness (wTPT), better discriminates established CV disease than traditional risk factors and plaque measures. Methods In 5 418 participants (59.8 ± 8.1 years; 53.0% women) from the Prospective Screening Of Non-invasive Atherosclerosis Risk study, plaque burden was assessed across 12 carotid and femoral segments using wTPT, maximal plaque thickness (MPT), plaque count, and number of arteries with plaque. Discrimination of prior CV disease was evaluated using c-statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results Global wTPT showed the strongest discrimination of established CV disease (c-statistic 0.85 [95% CI: 0.83–0.87]), outperforming alternative plaque measures (p < 0.001). Adding global wTPT to risk factors improved discrimination from 0.84 to 0.89 (95% CI: 0.87–0.91), NRI 0.71 (95% CI: 0.57–0.84) and IDI 0.058 (95% CI: 0.004–0.071). Femoral wTPT outperformed carotid wTPT (p < 0.001), and global wTPT exceeded both (p = 0.014). Across wTPT quartiles, odds of prior CV disease increased stepwise: 2.0 (95% CI: 0.89–4.4), 4.2 (95% CI: 2.0–8.6), and 10.0 (95% CI: 5.0–20.1) versus the lowest quartile. Conclusion Global wTPT showed superior discrimination of established CV disease and added substantial predictive information beyond standard risk factors. These findings support wTPT as a marker of systemic atherosclerosis and a strong candidate for outcome-based risk refinement. Trial registration clinicaltrials.gov. Identifier: NCT06933745 . Registered 22 april 2025 subclinical atherosclerosis cardiovascular disease plaque burden cardiovascular risk ultrasonography peripheral artery disease NORRISK-2 risk prediction Figures Figure 1 Figure 2 Figure 3 Introduction The majority of deaths due to cardiovascular (CV) disease are preventable by addressing key risk factors.( 1 ) Early identification of individuals at highest risk is essential for effective prevention. Although risk calculators based on demographic and clinical variables form the foundation of CV disease prevention, they are increasingly recognised as imprecise,( 2 ) in part due to the dominant influence of age in the development of atherosclerosis.( 3 ) Furthermore, the prevalence of subclinical atherosclerosis is high even among individuals classified as low risk.( 4 ) Vascular ultrasound enables detection of atherosclerotic plaques prior to calcification and can identify subclinical disease even in middle-aged, low-risk individuals.( 4 , 5 ) Moreover, ultrasound-based plaque burden assessment improves risk prediction beyond traditional clinical risk scores,( 6 ) with predictive performance comparable to that of coronary artery calcium (CAC) scoring, even when only carotid plaque burden is evaluated.( 7 ) The presence of plaque alone has been shown to confer added prognostic value beyond conventional risk factors.( 8 – 10 ) However, stratifying plaque burden beyond a binary presence/absence conveys additional information about degree of subclinical atherosclerosis and future CV risk.( 11 , 12 ) Still, models to quantify plaque burden have varied between studies,( 6 , 13 , 14 ) and there is a lack of consensus on which plaque burden model is the gold standard. Furthermore, although femoral plaque burden may provide incremental prognostic value,( 5 , 11 , 15 ) most prior studies have assessed carotid arteries alone. We recently developed and validated a novel plaque burden measure, that may be summarised as weighted total plaque thickness (wTPT), which integrates plaque burden from carotid and femoral arteries across 12 vascular segments.( 16 ) Moreover, we demonstrated that wTPT is strongly associated with CV risk factors, and correlates more closely with estimated CV risk, compared to traditional plaque burden measures (unpublished data under review). In this study, we examined whether wTPT discriminates established CV disease more effectively than commonly used plaque measures. We also evaluated whether wTPT provides incremental discrimination beyond the NORRISK-2 algorithm and a conventional risk factor model. Finally, we compared the strength of association across increasing levels of global wTPT versus carotid MPT to assess whether a novel multiterritory measure captures atherosclerotic disease burden more accurately than a conventional carotid-based plaque metric. Materials and methods The Prospective Screening Of Non-invasive Atherosclerosis Risk (P-SONAR) is a prospective cohort study that has previously been described (Unpublished data under review). Briefly, between April 2022 to March 2025, 21 029 consecutive men and women 45–74 years who self-referred for an ultrasound-based health-check in one of 10 clinics in Norway were invited to participate. A total of 19 895 (94.6%) enrolled and underwent ultrasound examinations and medical interviews. In five clinics, a subgroup of 5 500 consecutive participants also provided fasting blood samples and underwent a clinical examination. Of the total, 55 withdrew consent, in 7 the ultrasound examination was inadequate, 1 had missing ultrasound data and 27 had missing information about CV risk factors, leaving 5 418 participants as the study sample (Fig. 1 ). The study protocol was approved by Regional Committees for Medical and Health Research Ethics in Norway (ref. number 258985). In accordance with the WORLD Medical Association Declaration of Helsinki, all participants provided written informed consent before entering the study.( 17 ) The methodology of the 2D ultrasound examination and quantification of wTPT plaque burden have been previously reported.( 16 ) In brief, radiologists examined the carotid arteries (common carotid artery, carotid bifurcation, internal carotid artery) and the femoral arteries (common femoral artery, femoral bifurcation, superficial femoral artery). Plaque burden was quantified as wTPT, maximal plaque thickness (MPT), number of arteries with plaque and by plaque count. Plaque was defined according to the Mannheim Consensus Criteria,( 18 ) but only plaques > 1.0 mm were registered. Prior CV disease was assessed through a medical interview conducted by the radiologists and included history of ischemic stroke, ischemic heart disease (IHD; including myocardial infarction and coronary revascularization) and peripheral artery disease (undergone peripheral arterial revascularization procedure). Levels of glucose, triglycerides, total cholesterol, LDL-C, HDL-C and Lipoprotein(a) (Lp(a)) were assessed through venous blood samples. Blood pressure and abdominal obesity were registered through clinical examination. Estimated CV risk was calculated using the NORRISK-2 algorithm, which is a version of SCORE that has been validated for the Norwegian population.( 19 ) The algorithm includes age, sex, current smoking, systolic blood pressure, total cholesterol, first-degree relatives with CV disease younger than 60 years, use of antihypertensive medication and low HDL-C. Additionally, we used a risk factor model to estimate CV risk, including sex, age, dyslipidemia, elevated Lp(a), diabetes, smoking history (yes/no), abdominal obesity and family history. Diagnosis of diabetes type 1 and type 2 was based on the medical interview. Participants (n = 42) with glucose ≥ 7.0 mmol/L but no known diabetes were not classified as diabetic, as confirmatory testing was unavailable. Dyslipidemia was defined as total cholesterol > 6.2 mmol/l, LDL-C > 4.1 mmol/l or HDL-C 500 mg/L. Abdominal obesity was defined as waist circumference > 102 cm for men and > 88 cm for women. Blood pressure was measured after 5 minutes of rest; the mean of the second and third readings was used. Hypertension was defined as systolic BP > 140 mmHg, diastolic BP > 90 mmHg, or antihypertensive use. The study population was stratified by the presence of prior CV disease. Continuous variables were listed as mean and standard deviation (SD) or as median and interquartile range (IQR). Categorical variables were presented as number and percentage or as percentage and 95% confidence interval (CI). Differences between continuous variables were tested by Student t-tests, whereas categorical data were tested by chi-square tests. Logistic regression was used to estimate crude and adjusted odds ratios (OR) for CV disease by quartiles of plaque burden. The multivariable models were adjusted for the variables in the risk factor model. Discrimination was assessed using c-statistic for NORRISK-2, the risk factor model and for each plaque burden model. Incremental performance of plaque burden was evaluated using the c-statistic, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI) and the likelihood ratio test. To determine the best prognostic cutoff value for wTPT, we calculated the Youden J statistics from the c-statistic models. Statistical analyses were performed using Stata version 18.0. Level of significance of p > 0.05 was set for all analyses. Results Clinical Characteristics Clinical characteristics are presented in Table 1 . Mean age was 59.8 ± 8.1 years and 2 870 (54.0%) were female. A total of 230 individuals (4.2%) had prior CV disease, including 165 (3.0%) with IHD, 55 (1.0%) with ischemic stroke and 10 (0.2%) with peripheral artery disease. Table 1 Characteristics of the Study Population Prior CV disease Total N = 5 418 Yes N = 230 No N = 5 188 p Value Age, years 59.8 ± 8.1 65.2 ± 7.2 59.4 ± 8.1 < 0.001 Male 2548 (47.0) 168 (73.0) 2380 (45.9) < 0.001 CV risk factors Dyslipidemia 3213 (59.3) 214 (93.0) 2999 (57.8) < 0.001 Elevated Lp(a) 1150 (21.2) 69 (30.0) 1081 (20.8) 0.001 Diabetes 237 (4.4) 33 (14.4) 204 (3.9) < 0.001 Hypertension 2 426 (44.8) 181 (78.7) 2245 (43.3) < 0.001 Active smokers 527 (9.7) 24 (10.4) 414 (8.0) 0.181 Smoking history 1945 (38.4) 135 (58.7) 1945 (37.5)) < 0.001 Abdominal obesity 2371 (43.8) 103 (44.8) 2268 (43.7) 0.409 Family history < 0.001 − 0 4504 (83.1) 148 (64.4) 4356 (84.0) − 1 817 (15.1) 72 (31.3) 745 (14.4) − 2+ 97 (1.8) 10 (4.4) 87 (1.7) CV risk treatment Lipid lowering 1118 (20.6) 206 (89.6) 912 (17.6) < 0.001 Antihypertensive 1221 (22.5) 148 (64.4) 1073 (20.7) < 0.001 Blood thinning 576 (10.6) 216 (93.9) 360 (6.9) < 0.001 CV risk NORRISK-2 5.8 (2.6–10.5) 12.0 (6.9–17.8) 5.6 (2.6–10.2) < 0.001 CV = cardiovascular. Lp(a) = lipoprotein a. Smoking history = current and/or prior smokers. Compared with individuals without prior CV disease, those with established CV disease had a higher prevalence of all cardiovascular risk factors, excepting abdominal obesity and current smoking. Moreover, a greater proportion of participants with established CV disease were past or current smokers (58.7% vs. 37.5%, p < 0.001). Association between Plaque Burden and Prior CV Disease Overall, plaque prevalence was 99.1% in individuals with prior CV disease and 96.0% in those without (Table 2 ). Across all plaque burden measures, atherosclerosis was consistently higher in participants with established CV disease (Fig. 2 ). For wTPT, plaque burden was 21.5 mm (95% CI: 13.5–29.4) and 6.9 mm (95% CI: 3.6–12.4) in participants with and without prior CV disease, respectively. Table 2 Plaque Burden by CV Disease Prior CV disease Total N = 5 412 Yes N = 237 No N = 5 175 Plaque prevalence 5 212 (96.2) 228 (99.1) 4 984 (96.0) carotid 4 907 (90.6) 226 (98.3) 4 681 (90.2) femoral 4 363 (80.5) 225 (97.8) 4 138 (79.8) Plaque count, IQR 4 ( 2 – 5 ) 6 ( 5 – 7 ) 4 ( 2 – 5 ) Plaque count 1–2 22.4 (20.5–24.4) 1.05 (0.03–5.7) 23.4 (21.6–25.6) 3 15.7 (14.1–17.5) 4.2 (1.2–10.4) 16.3 (14.6–18.1) 4–5 33.9 (31.7–36.0) 38.9 (29.1–49.5) 33.6 (31.4–35.8) ≥ 6 24.3 (22.4–26.3) 54.7 (44.2–65.0) 22.7 (20.7–24.7) Arteries, IQR 3 ( 2 – 4 ) 4 ( 4 – 4 ) 3 ( 2 – 4 ) Arteries 1 8.1 (7.4–8.9) 0.43 (0.01–2.3) 8.4 (7.7–9.2) 2 19.3 (18.2–20.3) 2.2 (0.7-5.0) 20.0 (18.9–21.1) 3 20.6 (20.0-21.7) 6.5 (3.7–10.5) 21.2 (20.1–22.4) 4 48.2 (46.9–50.0) 90.0 (85-4-93.6) 46.4 (45.0-47.8) MPT 3.3 (2.3–4.3) 5.1 (4.2–6.2) 3.2 (2.2–4.2) wTPT 7.3 (3.6–13.2) 21.5 (13.5–29.4) 6.9 (3.6–12.4) CV = cardiovascular. IQR = interquartile range. MPT = maximal plaque thickness. wTPT = weighted total plaque thickness. Categorical variables are presented as number and percentages. Plaque count and arteries with plaque are listed by percentages with 95% confidence interval. Non-normal distributed variables (Plaque count, Arteries, MPT, wTPT) are presented as median and interquartile range. In multivariate analyses, associations between prior CV disease and plaque burden were consistent across all models (Supplemental Table 1). In regards to wTPT, ORs were 2.5 (95% CI: 2.0-3.2), 1.6 (95% CI: 1.4–1.9) and 2.0 (95% CI: 1.7–2.5) for global, carotid and femoral plaque burden, respectively. Similar associations were observed for IHD and ischemic stroke for all individuals and when stratified by sex (Supplemental Table 2–3). Compared to participants with plaque who were in the lowest global wTPT quartile for their 5-year age group and sex, the odds of having prior CV disease were 2.0 (0.89–4.4), 4.2 (2.0-8.6) and 10.0 (5.0-20.1) for those in quartiles 2, 3 and 4, respectively (Supplemental Table 4 and Graphical abstract). Corresponding results for the established measure carotid MPT were 1.3 (95% CI: 0.73–2.3), 1.7 (95% CI: 0.99–2.9) and 3.4 (95% CI 2.1–5.7), respectively. Plaque Burden, Risk Factors and Prior CV Disease The c-statistic for discriminating prior CV disease was 0.72 (95% CI: 0.71–0.74) for arteries with plaque, 0.75 (95% CI: 0.71–0.79) for plaque count, 0.83 (95% CI: 0.80–0.86) for MPT, and 0.85 (95% CI: 0.83–0.87) for wTPT. wTPT significantly outperformed all other plaque burden models (Table 3 ). wTPT also demonstrated superior performance compared to the other plaque burden measures in identifying individuals with prior IHD and ischemic stroke, except for stroke, where the difference between wTPT (0.78 [95% CI: 0.72–0.84]) and MPT (0.76 [95% CI: 0.69–0.82]) did not reach statistical significance (p = 0.06). Table 3 wTPT Discriminating Prior CV Disease Compared to Other Plaque Burden Measures Variable c-statistic (95% CI) p Value CV Disease Arteries 0.72 (0.71–0.74) < 0.001 Plaque count 0.75 (0.71–0.79) < 0.001 MPT 0.83 (0.80–0.86) < 0.001 wTPT 0.85 (0.83–0.87) NA IHD Arteries 0.73 (0.71–0.75) < 0.001 Plaque count 0.75 (0.71–0.79) < 0.001 MPT 0.83 (0.81–0.87) 0.0038 wTPT 0.86 (0.83–0.88) NA IS Arteries 0.68 (0.63–0.73) < 0.001 Plaque count 0,67 (0.59–0.76) < 0.001 MPT 0.75 (0.69–0.82) 0.060 wTPT 0.78 (0.72–0.84) NA p-Value compared with weighted Total Plaque Thickness (wTPT) CV = cardiovascular. IHD = ischemic heart disease. IS = ischemic stroke. MPT = maximal plaque thickness. wTPT = weighted total plaque thickness. Global wTPT yielded a higher c-statistic than femoral wTPT (0.84 [95% CI: 0.81–0.86]; p = 0.0142), which in turn outperformed carotid wTPT (0.78 [95% CI: 0.75–0.81]; p < 0.001) (Table 4 , Fig. 3 ). The c-statistic for the NORRISK-2 score and for the risk factor model were 0.73 (95% CI: 0.71–0.77) and 0.84 (95% CI: 0.82–0.87), respectively. Adding global, femoral, or carotid wTPT to the risk factor model significantly improved discrimination; p < 0.001 for all. The combination of the risk factor model and global wTPT achieved a c-statistic of 0.88 (95% CI: 0.86–0.90), with a continuous NRI of 0.71 (95% CI: 0.57–0.84) and IDI of 0.06 (95% CI: 0.04–0.07) (Fig. 3 ). Similar improvements were observed for prior IHD and ischemic stroke (Supplemental Table 5), though for stroke, the added value of carotid wTPT did not reach statistical significance (p = 0.079). Comparable improvements were also observed when stratified by sex, however, increase in c-statistic for IHD in females, as well as ischemic stroke for both sexes, did not reach statistical significance (Supplemental Table 6). Table 4 Incremental Value of wTPT to Risk Factors in Discriminating CV Disease Variable c-statistic (95% CI) p Value* Continuous NRI (95% CI) IDI (95% CI) Test statistics p Value** NORRISK-2 0.73 (0.71–0.77) N/A N/A N/A N/A N/A RF 0.84 (0.82–0.87) Ref. Ref. Ref. Ref. Ref. wTPT global 0.85 (0.83–0.87) N/A N/A N/A N/A N/A wTPT + RF 0.88 (0.86–0.90) < 0.001 0.71 (0.57–0.84) 0.06 (0.04–0.07) 122.6 < 0.001 wTPT carotid 0.78 (0.75–0.81) N/A N/A N/A N/A N/A wTPT carotid + RF 0.86 (0.84–0.88) < 0.001 0.39 (0.25–0.52) 0.02 (0.01–0.03) 44.8 < 0.001 wTPT femoral 0.84 (0.81–0.86) N/A N/A N/A N/A N/A wTPT femoral + RF 0.87 (0.85–0.90) < 0.001 0.67 (0.53–0.80) 0.05 (0.04–0.06) 114.9 < 0.001 *p value compared with risk factors **p Value Likelihood ratio test CV = cardiovascular. IDI = integrated discrimination improvement. NRI = net reclassification improvement. IDI = integrated discrimination improvement. RF = risk factors, including sex, age, dyslipidemia, hypertension, past or current smoking, diabetes, family history and elevated lipoprotein a. Test statistics = likelihood ratio test statistics. wTPT = weighted Total Plaque Thickness. Prognostic Cutoff Value for wTPT Plaque Burden Using the Youden J statistic, a wTPT threshold of > 11.4 mm was identified as the optimal cut point for discriminating established CV disease, yielding a sensitivity of 83.5%, specificity of 71.0%, positive predictive value of 11.3%, and a negative predictive value of 99.0%. The odds of having prior CV disease in participants above this threshold was 4.6 (95% CI: 3.1–6.9). When the wTPT cutoff was added to the risk factor model, the c-statistic increased to 0.87 (95% CI 0.85–0.89). Furthermore, the continuous NRI was 0.73 (0.61–0.86) and the IDI was 0.017 (95% CI: 0.012–0.021). Discussion Our findings demonstrate that wTPT, a novel method based on the weighted sum of plaque thickness across 12 vascular segments in carotid and femoral arteries, is superior to conventional models to measure plaque burden in discriminating between individuals with prior CV disease versus those without prior disease. Furthermore, wTPT added significant predictive value beyond traditional risk factors, as reflected by an improved c-statistic and a marked increase in NRI. Notably, global wTPT provided superior discrimination compared to femoral or carotid wTPT alone, with assessments of femoral burden outperforming that of carotid burden. Additionally, individuals with high wTPT plaque burden had substantially elevated odds of prior CV disease compared with those classified by the conventional carotid MPT measure. Assessing Subclinical Atherosclerosis by Ultrasound Studies of plaque prevalence in low to medium risk individuals has varied substantially, particularly for carotid plaques.( 20 , 21 ) However, with modern ultrasound technology and experienced sonographers, high prevalence rates of carotid plaque have been reported.( 21 ) In our study, plaque was present in 95.8% of participants without prior CV disease, indicating that a simple binary classification of plaque presence offers limited discriminatory value for risk prediction. Plaque volume assessed by 3D ultrasound may represent the most comprehensive measure of subclinical atherosclerosis, incorporating plaque number, height, width, and length.( 11 ) However, this method is time-consuming and impractical for routine clinical use.( 16 ) Furthermore, the BioImage study showed that carotid maximal plaque thickness (MPT) had comparable predictive utility to total plaque volume (TPV).( 13 ) wTPT serves as a surrogate for TPV, integrating plaque number, thickness, and length, while being more time-efficient to acquire.( 16 ) Since wTPT relies on detailed 2D scanning of all arterial segments, it may also detect a broader range of subclinical disease compared to standardised 3D sweeps.( 16 ) We have previously shown that wTPT correlates more strongly with estimated CV risk than MPT, plaque count, or number of affected arteries (Unpublished data under review). Consistently, in this study, wTPT outperformed all other plaque burden measures in discriminating prior CV disease. Adding wTPT to both NORRISK-2 and the traditional risk factor model significantly improved the c-statistic and NRI, supporting wTPT as a practical and predictive tool for assessing atherosclerotic burden in clinical settings. Femoral Plaque Burden Femoral plaque burden has been shown to be more extensive than carotid plaque burden ( 11 ) and more closely correlated with coronary artery calcium (CAC) scores.( 5 ) Additionally, it improves CV risk prediction when added to traditional risk factors and carotid plaque burden.( 15 ) Nevertheless, most ultrasound-based studies have focused solely on the carotid arteries. Recently, we demonstrated that femoral plaque burden exceeded carotid burden, and that global wTPT correlated most strongly with estimated CV risk, with the femoral territory contributing more than the carotid territory (Unpublished data under review). In the present study, global wTPT had the highest discriminatory power for prior CV disease, followed by femoral and then carotid wTPT. These findings were consistent across sexes. The superior performance of femoral over carotid wTPT reinforces the importance of including femoral arteries when evaluating subclinical atherosclerosis by ultrasound for CV risk assessment. Discrimination of IHD and Ischemic Stroke The ACE1950 study found that carotid plaque burden was more strongly associated with incident ischemic stroke than with MACE, likely due to the role of carotid plaques as a potential thromboembolic source.( 6 ) However, in the MESA study, the association seemed to be higher between carotid plaque presence and both CV disease and coronary heart disease, compared to ischemic stroke or transient ischemic attack.( 14 ) In the present study, predictive performance of both NORRISK-2, the risk factor model and all plaque burden models seemed to be lower for ischemic stroke, compared to both IHD and CV disease. As only a minority of ischemic strokes are caused by large vessel atherosclerosis, whereas most IHD are caused by rupture of coronary artery plaques, these findings might not be too surprising.( 22 , 23 ) Study Limitations For measures of plaque burden to yield clinically meaningful information, they must improve prediction beyond traditional risk models. However, conventional risk scores like NORRISK-2 are not designed to discriminate prior CV disease. Participants with established disease often initiate lifestyle changes and medical therapy, potentially altering their current risk profile. Indeed, as expected, use of lipid-lowering, antihypertensive, antidiabetic, and antithrombotic medications was significantly higher in those with established CV disease in our study. While the proportion of current smokers was similar between groups, former smoking was more common among those with prior disease, and no significant difference in abdominal obesity was observed. Given these factors, it is unsurprising that NORRISK-2 demonstrated only modest discriminatory ability (c-statistic 0.73; 95% CI:0.71–0.77). To better reflect underlying risk, we evaluated an alternative model incorporating age, sex, dyslipidemia, hypertension, diabetes, elevated Lp(a), past or current smoking, and family history. In this context, where 90% of participants with prior CV disease were on lipid-lowering therapy, dyslipidemia likely provided stronger risk stratification than total cholesterol, which is used in NORRISK-2. This risk factor model achieved a relatively high c-statistic of 0.84 (95% CI: 0.82–0.87), supporting the clinical value of the observed improvements in discrimination and NRI when adding wTPT. Additionally, as lipid lowering therapy reduces the progression of subclinical atherosclerosis and may in fact reduce plaque volume,( 24 ) the added value of wTPT for identifying prior CV disease might be underestimated. Moreover, the multivariate ORs for prior CV disease observed in the highest carotid MPT quartile in our study were very similar to the associations observed between carotid MPT and incident CV disease in recent studies.( 13 ) In the present study, radiologists were not blinded to participants’ risk factors or history of prior CV disease during ultrasound examinations. This introduces the potential for observer bias, possibly leading to higher plaque burden registration in participants with established disease. However, the high plaque prevalence even among participants without prior CV disease suggests that any such bias was likely minimal. Moreover, it is unlikely that this limitation significantly influenced the comparative performance of different plaque burden measures or vascular territories. Strengths of the Study Ultrasound examinations were conducted by radiologists with extensive experience in vascular imaging, using standardised equipment. Plaque assessment was comprehensive, covering 12 vascular segments across both carotid and femoral territories, and allowing for direct comparison of multiple plaque burden measures. Importantly, examinations were performed in routine clinical settings rather than specialised research environments, supporting the generalisability of our findings. The study included a large, diverse cohort spanning a wide age range and multiple regions of Norway. Finally, we applied a broad range of statistical measures to assess the relationship between plaque burden and prior CV disease, with consistent results supporting the superior performance of wTPT. Conclusion Global wTPT showed superior discrimination of established CV disease compared to established plaque measures and added substantial predictive information beyond traditional risk factors. These findings support wTPT as a marker of systemic atherosclerosis and a strong candidate for outcome-based risk refinement. Abbreviations P-SONAR (Prospective Screening Of Non-invasive Atherosclerosis Risk), CV (cardiovascular), wTPT (weighted total plaque thickness), MPT (maximal plaque thickness), TPV (total plaque volume), CAC (coronary artery calcium), SCORE (Systematic Coronary Risk Evaluation), Lp(a) (Lipoprotein a), IHD (ischemic heart disease) Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained by Regional Committees for Medical and Health Research Ethics in Norway (ref. number 258985). All participants provided written informed consent prior to inclusion. The study involved non-invasive vascular ultrasound only, and no additional procedures beyond routine clinical health-check assessments were performed. Consent for publication Not Applicable. Availability of data and materials Because of the sensitive nature of the data in this study, participants were assured that raw data would remain confidential and would not be shared. Conflict of interest The present study was funded by the private health care company Austad Diagnostikk. G. Austad is employed and part owner of Austad Diagnostikk. The other authors have no relationships to disclose. The study was conducted in cooperation with Oslo University Hospital. An external monitor, Link Medical, supervised the study. Declaration of Generative AI and AI-assisted technologies in the writing process There is nothing to disclose. Acknowledgements We acknowledge Philips Healthcare for providing medical equipment; and Austad Diagnostikk AS for funding of the study. References Cardiovascular diseases [Internet]. [cited 2025 May 20]. Available from: https://www.who.int/health-topics/cardiovascular-diseases Nasir K, Blankstein R. Transforming the Cardiovascular Disease Prevention Paradigm: See Disease, Treat Disease. JAMA. 2025;333(16):1398–400. Mortensen MB, Falk E. Primary Prevention With Statins in the Elderly. JACC. 2018;71(1):85–94. 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. Circulation. 2015;131(24):2104–13. Laclaustra M, Casasnovas JA, Fernández-Ortiz A, Fuster V, León-Latre M, Jiménez-Borreguero LJ, et al. Femoral and Carotid Subclinical Atherosclerosis Association With Risk Factors and Coronary Calcium: The AWHS Study. J Am Coll Cardiol. 2016;67(11):1263–74. Ihle-Hansen H, Vigen TV, Berge TB, Walle-Hansen MWH, Hagberg GH, Ihle-Hansen HIH, et al. Carotid plaque score for stroke and cardiovascular risk prediction in a middle-aged cohort from the general population: the akershus cardiac examination 1950 study. Eur Heart J. 2023;44(Supplement2):ehad6552414. 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: the BioImage study. J Am Coll Cardiol. 2015;65(11):1065–74. Inaba Y, Chen JA, Bergmann SR. Carotid plaque, compared with carotid intima-media thickness, more accurately predicts coronary artery disease events: A meta-analysis. Atherosclerosis. 2012;220(1):128–33. Sirimarco G, Amarenco P, Labreuche J, Touboul PJ, Alberts M, Goto S, et al. Carotid Atherosclerosis and Risk of Subsequent Coronary Event in Outpatients With Atherothrombosis. Stroke. 2013;44(2):373–9. Bao X, Xu B, Lind L, Engström G. Carotid ultrasound and systematic coronary risk assessment 2 in the prediction of cardiovascular events. Eur J Prev Cardiol. 2023;30(10):1007–14. López-Melgar B, Fernández-Friera L, Oliva B, García-Ruiz JM, Peñalvo JL, Gómez-Talavera S, et al. Subclinical Atherosclerosis Burden by 3D Ultrasound in Mid-Life: The PESA Study. J Am Coll Cardiol. 2017;70(3):301–13. Gudmundsson EF, Björnsdottir G, Sigurdsson S, Andersen K, Thorsson B, Aspelund T, et al. Carotid plaque is strongly associated with coronary artery calcium and predicts incident coronary heart disease in a population-based cohort. Atherosclerosis. 2022;346:117–23. Sillesen H, Sartori S, Sandholt B, Baber U, Mehran R, Fuster V. Carotid plaque thickness and carotid plaque burden predict future cardiovascular events in asymptomatic adult Americans. Eur Heart J Cardiovasc Imaging. 2018;19(9):1042–50. Gepner AD, Young R, Delaney JA, Tattersall MC, Blaha MJ, Post WS, et al. Comparison of Coronary Artery Calcium Presence, Carotid Plaque Presence, and Carotid Intima-Media Thickness for Cardiovascular Disease Prediction in the Multi-Ethnic Study of Atherosclerosis. Circ Cardiovasc Imaging. 2015;8(1):e002262. Davidsson L, Fagerberg B, Bergström G, Schmidt C. Ultrasound-assessed plaque occurrence in the carotid and femoral arteries are independent predictors of cardiovascular events in middle-aged men during 10 years of follow-up. Atherosclerosis. 2010;209(2):469–73. Austad G, Geitung JT, Tonstad S. Validation and Reproducibility of Total Plaque Thickness in Carotid and Femoral Arteries Using Ultrasound. Ultrasound Med Biol. 2024;50(2):207–15. WMA - The World Medical Association-WMA Declaration of Helsinki. – Ethical Principles for Medical Research Involving Human Subjects [Internet]. [cited 2022 Sep 20]. Available from: https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/ Touboul P, Hennerici M, Meairs S, Adams H, Amarenco P, Bornstein N et al. Mannheim Carotid Intima-Media Thickness and Plaque Consensus (2004-2006-2011): 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 Basel Switz. 2012;34(4):290–6. Selmer R, Igland J, Ariansen I, Tverdal A, Njølstad I, Furu K, et al. NORRISK 2: A Norwegian risk model for acute cerebral stroke and myocardial infarction. Eur J Prev Cardiol. 2017;24(7):773–82. Dahl M, Lindholt J, Søgaard R, Refsgaard J, Svenstrup D, Moeslund NJ, et al. Relevance of the Viborg Population Based Screening Programme (VISP) for Cardiovascular Conditions Among 67 Year Olds: Attendance Rate, Prevalence, and Proportion of Initiated Cardiovascular Medicines Stratified By Sex. Eur J Vasc Endovasc Surg. 2023;66(1):119–29. Ihle-Hansen H, Vigen T, Ihle‐Hansen H, Rønning OM, Berge T, Thommessen B, et al. Prevalence of Carotid Plaque in a 63‐ to 65‐Year‐Old Norwegian Cohort From the General Population: The ACE (Akershus Cardiac Examination) 1950 Study. J Am Heart Assoc. 2018;7(10):e008562. Elsheikh S, Hill A, Irving G, Lip GYH, Abdul-Rahim AH. Atrial fibrillation and stroke: State-of-the-art and future directions. Curr Probl Cardiol. 2024;49(1, Part C):102181. Falk E, Nakano M, Bentzon JF, Finn AV, Virmani R. Update on acute coronary syndromes: the pathologists’ view. Eur Heart J. 2013;34(10):719–28. Andelius L, Mortensen MB, Nørgaard BL, Abdulla J. Impact of statin therapy on coronary plaque burden and composition assessed by coronary computed tomographic angiography: a systematic review and meta-analysis. Eur Heart J - Cardiovasc Imaging. 2018;19(8):850–8. Additional Declarations Competing interest reported. The present study was funded by the private health care company Austad Diagnostikk. GA is employed and part owner of Austad Diagnostikk. The other authors have no relationships to disclose. The study was conducted in cooperation with Oslo University Hospital. An external monitor, Link Medical, supervised the study. Supplementary Files floatimage1.png Graphical abstract SupplementalMaterial2611.docx Cite Share Download PDF Status: Published Journal Publication published 09 Apr, 2026 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 05 Feb, 2026 Reviews received at journal 04 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviews received at journal 03 Feb, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers agreed at journal 11 Dec, 2025 Reviewers invited by journal 09 Dec, 2025 Editor assigned by journal 09 Dec, 2025 Editor invited by journal 08 Dec, 2025 Submission checks completed at journal 07 Dec, 2025 First submitted to journal 01 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8215491","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558933654,"identity":"1a48018f-045f-4436-b279-272367bc9d5e","order_by":0,"name":"Gunnar Austad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYNADngogwczcQKx6ZqCWMyCakRQtvG0gBgEt8hG5xx783MMgb3D+/MEPb+fVRvO3A7X8qNiGU4vhjbx0w55nDIYbbiQzS87ddjx3xmHGBsaeM7dxa5mRYybBc4CBccMNZgZp3m3HchuAWpgZ2/BrkfxzgMF+w/nDzL955xzLnU9Ii7xEjpk00JbEDQeS2aR5G2pyNxDSYsDzxkxa5oBE8swbyWaWc44dyN0I1HIQn1/k24EOe3PAxrbv/MHHN97U1OXOO3/44IMfFXhsOQCmJGD8w2DyAE71IFsaUPl1+BSPglEwCkbBCAUAjv5bK9tCdf4AAAAASUVORK5CYII=","orcid":"","institution":"Oslo University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Gunnar","middleName":"","lastName":"Austad","suffix":""},{"id":558933655,"identity":"9b28bec9-2be1-42b4-85dc-ac63c8be092e","order_by":1,"name":"Jonn Terje Geitung","email":"","orcid":"","institution":"Akershus University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jonn","middleName":"Terje","lastName":"Geitung","suffix":""},{"id":558933662,"identity":"ae4f9bc5-f3b4-4f70-9f6f-c7c11e903e14","order_by":2,"name":"Owen Thomas","email":"","orcid":"","institution":"Akershus University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Owen","middleName":"","lastName":"Thomas","suffix":""},{"id":558933665,"identity":"12c744d8-9ab6-436d-8816-6516603362fb","order_by":3,"name":"Serena Tonstad","email":"","orcid":"","institution":"Oslo University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Serena","middleName":"","lastName":"Tonstad","suffix":""}],"badges":[],"createdAt":"2025-11-26 18:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8215491/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8215491/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-026-05807-8","type":"published","date":"2026-04-09T15:58:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":98428961,"identity":"650a5dc2-7ee7-4837-8e81-573f1a6c9ae1","added_by":"auto","created_at":"2025-12-17 16:42:38","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1298448,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript011225incl.figuresclean.docx","url":"https://assets-eu.researchsquare.com/files/rs-8215491/v1/4e4a8eb746648e9123f05058.docx"},{"id":98074985,"identity":"8bade637-325e-4090-874c-92e9365a1ff2","added_by":"auto","created_at":"2025-12-12 13:30:41","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7169,"visible":true,"origin":"","legend":"","description":"","filename":"411c1583d2d14f53bb27a1211e55cec7.json","url":"https://assets-eu.researchsquare.com/files/rs-8215491/v1/1616b09db54cf796d63d0f57.json"},{"id":98074989,"identity":"df3abc12-5b8f-473d-9b3e-a862c3c05f57","added_by":"auto","created_at":"2025-12-12 13:30:41","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20662,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial2611.docx","url":"https://assets-eu.researchsquare.com/files/rs-8215491/v1/3cc85140119a2cbbb907c245.docx"},{"id":98074987,"identity":"bc45252c-d18d-4456-818e-f66ec2eda50f","added_by":"auto","created_at":"2025-12-12 13:30:41","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":105391,"visible":true,"origin":"","legend":"","description":"","filename":"411c1583d2d14f53bb27a1211e55cec71enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8215491/v1/351ff099278fc4a1aea31f38.xml"},{"id":98429451,"identity":"9d51cedd-2ac0-457a-9acf-de4907ccac90","added_by":"auto","created_at":"2025-12-17 16:43:29","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":35604,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8215491/v1/64c512a5b886edc20998fdc1.png"},{"id":98074993,"identity":"71baa91d-0218-4d36-8bff-3f2bc8455095","added_by":"auto","created_at":"2025-12-12 13:30:41","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32405,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8215491/v1/2ba0727f04d914b8a6094f8c.png"},{"id":98429976,"identity":"c23c6040-95a5-449d-a6e1-58efe554c202","added_by":"auto","created_at":"2025-12-17 16:44:31","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9534,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8215491/v1/f99e9dd30614813ec37661b3.png"},{"id":98429492,"identity":"660d3cf5-1fc7-4a83-b1d7-fd456fe91730","added_by":"auto","created_at":"2025-12-17 16:43:33","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16811,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8215491/v1/903a84c9c39e8f63ffc1cc73.png"},{"id":98429219,"identity":"504434d2-120e-4eeb-b3a0-fe6cbdc0900a","added_by":"auto","created_at":"2025-12-17 16:42:58","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":25246,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8215491/v1/9141b38752d80466ca442c80.png"},{"id":98074997,"identity":"69fc1cf2-fe0f-4a05-81c6-351f4ba65c8f","added_by":"auto","created_at":"2025-12-12 13:30:41","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":102373,"visible":true,"origin":"","legend":"","description":"","filename":"411c1583d2d14f53bb27a1211e55cec71structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8215491/v1/b721a52aab87eb91566bc9b9.xml"},{"id":98074998,"identity":"8e7400fa-444d-47d6-abb3-2f0c3d26f712","added_by":"auto","created_at":"2025-12-12 13:30:41","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110789,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8215491/v1/443d6238cbf46d058030fc1e.html"},{"id":98074984,"identity":"0b477353-566f-4121-9ffd-4f2a4b4712bf","added_by":"auto","created_at":"2025-12-12 13:30:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":123266,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the Study Population\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8215491/v1/227ff1d29c52f13d957cca16.png"},{"id":98075000,"identity":"16cb0947-fd1a-469c-ae31-004cb13eaa3d","added_by":"auto","created_at":"2025-12-12 13:30:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35723,"visible":true,"origin":"","legend":"\u003cp\u003ePlaque Burden by CV Disease\u003c/p\u003e\n\u003cp\u003eBar graph illustrating median plaque burden across prior CV disease status\u003c/p\u003e\n\u003cp\u003eCV = cardiovascular. MPT = maximal plaque thickness. wTPT = weighted total plaque thickness.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8215491/v1/bcd6fdd2d388e7877030c7f2.png"},{"id":98427601,"identity":"2abe7ada-1b3e-45e1-9a2c-b6ffdcfaa255","added_by":"auto","created_at":"2025-12-17 16:40:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":220365,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves for discrimination of prior cardiovascular disease. Figure A: ROC curves for carotid, femoral, and global wTPT plaque burden. Figure B: ROC curves for NORRISK-2, risk factor model, and risk factor model combined with global wTPT.\u003c/p\u003e\n\u003cp\u003eRisk factors include sex, age, dyslipidemia, elevated lipoprotein a, diabetes, smoking history (yes/no), abdominal obesity and family history.\u003c/p\u003e\n\u003cp\u003eCV = cardiovascular. RF = risk factors. wTPT = weighted total plaque thickness.\u003c/p\u003e","description":"","filename":"floatimage411.png","url":"https://assets-eu.researchsquare.com/files/rs-8215491/v1/8bb2643ed3822b17cf3072f9.png"},{"id":106810291,"identity":"c31e2447-aeaa-4345-b07e-54d170a04736","added_by":"auto","created_at":"2026-04-13 16:15:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1287198,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8215491/v1/7f37b42a-fed1-46c9-acdb-bdc2db4d7f75.pdf"},{"id":98074986,"identity":"2b4a2c6e-ba52-4a8b-8a1c-62642ca9f380","added_by":"auto","created_at":"2025-12-12 13:30:41","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":172094,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8215491/v1/87955de0001c8352bd1acbef.png"},{"id":98074996,"identity":"c10927cf-ea4a-492b-8aa0-d8671490b496","added_by":"auto","created_at":"2025-12-12 13:30:41","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20662,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial2611.docx","url":"https://assets-eu.researchsquare.com/files/rs-8215491/v1/cc9fc5333f708817d894e2bf.docx"}],"financialInterests":"Competing interest reported. The present study was funded by the private health care company Austad Diagnostikk. GA is employed and part owner of Austad Diagnostikk. The other authors have no relationships to disclose. The study was conducted in cooperation with Oslo University Hospital. An external monitor, Link Medical, supervised the study.","formattedTitle":"Discriminating Established Cardiovascular Disease Using a Novel Multiterritory Ultrasound Plaque Burden Measure (wTPT): Findings From the P-SONAR Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe majority of deaths due to cardiovascular (CV) disease are preventable by addressing key risk factors.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Early identification of individuals at highest risk is essential for effective prevention. Although risk calculators based on demographic and clinical variables form the foundation of CV disease prevention, they are increasingly recognised as imprecise,(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) in part due to the dominant influence of age in the development of atherosclerosis.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Furthermore, the prevalence of subclinical atherosclerosis is high even among individuals classified as low risk.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eVascular ultrasound enables detection of atherosclerotic plaques prior to calcification and can identify subclinical disease even in middle-aged, low-risk individuals.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Moreover, ultrasound-based plaque burden assessment improves risk prediction beyond traditional clinical risk scores,(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) with predictive performance comparable to that of coronary artery calcium (CAC) scoring, even when only carotid plaque burden is evaluated.(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe presence of plaque alone has been shown to confer added prognostic value beyond conventional risk factors.(\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) However, stratifying plaque burden beyond a binary presence/absence conveys additional information about degree of subclinical atherosclerosis and future CV risk.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) Still, models to quantify plaque burden have varied between studies,(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) and there is a lack of consensus on which plaque burden model is the gold standard. Furthermore, although femoral plaque burden may provide incremental prognostic value,(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) most prior studies have assessed carotid arteries alone.\u003c/p\u003e\u003cp\u003eWe recently developed and validated a novel plaque burden measure, that may be summarised as weighted total plaque thickness (wTPT), which integrates plaque burden from carotid and femoral arteries across 12 vascular segments.(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) Moreover, we demonstrated that wTPT is strongly associated with CV risk factors, and correlates more closely with estimated CV risk, compared to traditional plaque burden measures (unpublished data under review). In this study, we examined whether wTPT discriminates established CV disease more effectively than commonly used plaque measures. We also evaluated whether wTPT provides incremental discrimination beyond the NORRISK-2 algorithm and a conventional risk factor model. Finally, we compared the strength of association across increasing levels of global wTPT versus carotid MPT to assess whether a novel multiterritory measure captures atherosclerotic disease burden more accurately than a conventional carotid-based plaque metric.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThe Prospective Screening Of Non-invasive Atherosclerosis Risk (P-SONAR) is a prospective cohort study that has previously been described (Unpublished data under review). Briefly, between April 2022 to March 2025, 21 029 consecutive men and women 45\u0026ndash;74 years who self-referred for an ultrasound-based health-check in one of 10 clinics in Norway were invited to participate. A total of 19 895 (94.6%) enrolled and underwent ultrasound examinations and medical interviews. In five clinics, a subgroup of 5 500 consecutive participants also provided fasting blood samples and underwent a clinical examination. Of the total, 55 withdrew consent, in 7 the ultrasound examination was inadequate, 1 had missing ultrasound data and 27 had missing information about CV risk factors, leaving 5 418 participants as the study sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The study protocol was approved by Regional Committees for Medical and Health Research Ethics in Norway (ref. number 258985). In accordance with the WORLD Medical Association Declaration of Helsinki, all participants provided written informed consent before entering the study.(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe methodology of the 2D ultrasound examination and quantification of wTPT plaque burden have been previously reported.(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) In brief, radiologists examined the carotid arteries (common carotid artery, carotid bifurcation, internal carotid artery) and the femoral arteries (common femoral artery, femoral bifurcation, superficial femoral artery). Plaque burden was quantified as wTPT, maximal plaque thickness (MPT), number of arteries with plaque and by plaque count. Plaque was defined according to the Mannheim Consensus Criteria,(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) but only plaques\u0026thinsp;\u0026gt;\u0026thinsp;1.0 mm were registered.\u003c/p\u003e\u003cp\u003ePrior CV disease was assessed through a medical interview conducted by the radiologists and included history of ischemic stroke, ischemic heart disease (IHD; including myocardial infarction and coronary revascularization) and peripheral artery disease (undergone peripheral arterial revascularization procedure). Levels of glucose, triglycerides, total cholesterol, LDL-C, HDL-C and Lipoprotein(a) (Lp(a)) were assessed through venous blood samples. Blood pressure and abdominal obesity were registered through clinical examination.\u003c/p\u003e\u003cp\u003eEstimated CV risk was calculated using the NORRISK-2 algorithm, which is a version of SCORE that has been validated for the Norwegian population.(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) The algorithm includes age, sex, current smoking, systolic blood pressure, total cholesterol, first-degree relatives with CV disease younger than 60 years, use of antihypertensive medication and low HDL-C. Additionally, we used a risk factor model to estimate CV risk, including sex, age, dyslipidemia, elevated Lp(a), diabetes, smoking history (yes/no), abdominal obesity and family history. Diagnosis of diabetes type 1 and type 2 was based on the medical interview. Participants (n\u0026thinsp;=\u0026thinsp;42) with glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L but no known diabetes were not classified as diabetic, as confirmatory testing was unavailable. Dyslipidemia was defined as total cholesterol\u0026thinsp;\u0026gt;\u0026thinsp;6.2 mmol/l, LDL-C\u0026thinsp;\u0026gt;\u0026thinsp;4.1 mmol/l or HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;1.0 mmol/l, triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;1.7 mmol/l or use of cholesterol-lowering therapy. Elevated Lp(a) was defined as \u0026gt;\u0026thinsp;500 mg/L. Abdominal obesity was defined as waist circumference\u0026thinsp;\u0026gt;\u0026thinsp;102 cm for men and \u0026gt;\u0026thinsp;88 cm for women. Blood pressure was measured after 5 minutes of rest; the mean of the second and third readings was used. Hypertension was defined as systolic BP\u0026thinsp;\u0026gt;\u0026thinsp;140 mmHg, diastolic BP\u0026thinsp;\u0026gt;\u0026thinsp;90 mmHg, or antihypertensive use.\u003c/p\u003e\u003cp\u003eThe study population was stratified by the presence of prior CV disease. Continuous variables were listed as mean and standard deviation (SD) or as median and interquartile range (IQR). Categorical variables were presented as number and percentage or as percentage and 95% confidence interval (CI). Differences between continuous variables were tested by Student t-tests, whereas categorical data were tested by chi-square tests.\u003c/p\u003e\u003cp\u003eLogistic regression was used to estimate crude and adjusted odds ratios (OR) for CV disease by quartiles of plaque burden. The multivariable models were adjusted for the variables in the risk factor model.\u003c/p\u003e\u003cp\u003eDiscrimination was assessed using c-statistic for NORRISK-2, the risk factor model and for each plaque burden model. Incremental performance of plaque burden was evaluated using the c-statistic, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI) and the likelihood ratio test. To determine the best prognostic cutoff value for wTPT, we calculated the Youden J statistics from the c-statistic models. Statistical analyses were performed using Stata version 18.0. Level of significance of p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 was set for all analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eClinical Characteristics\u003c/h2\u003e\u003cp\u003eClinical characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Mean age was 59.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1 years and 2 870 (54.0%) were female. A total of 230 individuals (4.2%) had prior CV disease, including 165 (3.0%) with IHD, 55 (1.0%) with ischemic stroke and 10 (0.2%) with peripheral artery disease.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the Study Population\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003ePrior CV disease\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;5 418\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;230\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;5 188\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2548 (47.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e168 (73.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2380 (45.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCV risk factors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3213 (59.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e214 (93.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2999 (57.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElevated Lp(a)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1150 (21.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1081 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e237 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (14.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e204 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 426 (44.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e181 (78.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2245 (43.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActive smokers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e527 (9.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e414 (8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1945 (38.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135 (58.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1945 (37.5))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbdominal obesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2371 (43.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e103 (44.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2268 (43.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4504 (83.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e148 (64.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4356 (84.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e817 (15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72 (31.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e745 (14.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;2+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCV risk treatment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLipid lowering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1118 (20.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e206 (89.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e912 (17.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntihypertensive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1221 (22.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e148 (64.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1073 (20.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood thinning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e576 (10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e216 (93.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e360 (6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCV risk\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNORRISK-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.8 (2.6\u0026ndash;10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.0 (6.9\u0026ndash;17.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.6 (2.6\u0026ndash;10.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eCV\u0026thinsp;=\u0026thinsp;cardiovascular. Lp(a)\u0026thinsp;=\u0026thinsp;lipoprotein a. Smoking history\u0026thinsp;=\u0026thinsp;current and/or prior smokers.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCompared with individuals without prior CV disease, those with established CV disease had a higher prevalence of all cardiovascular risk factors, excepting abdominal obesity and current smoking. Moreover, a greater proportion of participants with established CV disease were past or current smokers (58.7% vs. 37.5%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssociation between Plaque Burden and Prior CV Disease\u003c/h3\u003e\n\u003cp\u003eOverall, plaque prevalence was 99.1% in individuals with prior CV disease and 96.0% in those without (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Across all plaque burden measures, atherosclerosis was consistently higher in participants with established CV disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For wTPT, plaque burden was 21.5 mm (95% CI: 13.5\u0026ndash;29.4) and 6.9 mm (95% CI: 3.6\u0026ndash;12.4) in participants with and without prior CV disease, respectively.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePlaque Burden by CV Disease\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003ePrior CV disease\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;5 412\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;237\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;5 175\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlaque prevalence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 212 (96.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e228 (99.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 984 (96.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecarotid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 907 (90.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e226 (98.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 681 (90.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efemoral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 363 (80.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e225 (97.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 138 (79.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlaque count, IQR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlaque count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.4 (20.5\u0026ndash;24.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.05 (0.03\u0026ndash;5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.4 (21.6\u0026ndash;25.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.7 (14.1\u0026ndash;17.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.2 (1.2\u0026ndash;10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.3 (14.6\u0026ndash;18.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.9 (31.7\u0026ndash;36.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.9 (29.1\u0026ndash;49.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.6 (31.4\u0026ndash;35.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge; 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.3 (22.4\u0026ndash;26.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.7 (44.2\u0026ndash;65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.7 (20.7\u0026ndash;24.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArteries, IQR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArteries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.1 (7.4\u0026ndash;8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.43 (0.01\u0026ndash;2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.4 (7.7\u0026ndash;9.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.3 (18.2\u0026ndash;20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.2 (0.7-5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.0 (18.9\u0026ndash;21.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.6 (20.0-21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.5 (3.7\u0026ndash;10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.2 (20.1\u0026ndash;22.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.2 (46.9\u0026ndash;50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90.0 (85-4-93.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.4 (45.0-47.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMPT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.3 (2.3\u0026ndash;4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.1 (4.2\u0026ndash;6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.2 (2.2\u0026ndash;4.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewTPT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.3 (3.6\u0026ndash;13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.5 (13.5\u0026ndash;29.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.9 (3.6\u0026ndash;12.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eCV\u0026thinsp;=\u0026thinsp;cardiovascular. IQR\u0026thinsp;=\u0026thinsp;interquartile range. MPT\u0026thinsp;=\u0026thinsp;maximal plaque thickness. wTPT\u0026thinsp;=\u0026thinsp;weighted total plaque thickness.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eCategorical variables are presented as number and percentages. Plaque count and arteries with plaque are listed by percentages with 95% confidence interval. Non-normal distributed variables (Plaque count, Arteries, MPT, wTPT) are presented as median and interquartile range.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn multivariate analyses, associations between prior CV disease and plaque burden were consistent across all models (Supplemental Table\u0026nbsp;1). In regards to wTPT, ORs were 2.5 (95% CI: 2.0-3.2), 1.6 (95% CI: 1.4\u0026ndash;1.9) and 2.0 (95% CI: 1.7\u0026ndash;2.5) for global, carotid and femoral plaque burden, respectively. Similar associations were observed for IHD and ischemic stroke for all individuals and when stratified by sex (Supplemental Table\u0026nbsp;2\u0026ndash;3). Compared to participants with plaque who were in the lowest global wTPT quartile for their 5-year age group and sex, the odds of having prior CV disease were 2.0 (0.89\u0026ndash;4.4), 4.2 (2.0-8.6) and 10.0 (5.0-20.1) for those in quartiles 2, 3 and 4, respectively (Supplemental Table\u0026nbsp;4 and Graphical abstract). Corresponding results for the established measure carotid MPT were 1.3 (95% CI: 0.73\u0026ndash;2.3), 1.7 (95% CI: 0.99\u0026ndash;2.9) and 3.4 (95% CI 2.1\u0026ndash;5.7), respectively.\u003c/p\u003e\n\u003ch3\u003ePlaque Burden, Risk Factors and Prior CV Disease\u003c/h3\u003e\n\u003cp\u003eThe c-statistic for discriminating prior CV disease was 0.72 (95% CI: 0.71\u0026ndash;0.74) for arteries with plaque, 0.75 (95% CI: 0.71\u0026ndash;0.79) for plaque count, 0.83 (95% CI: 0.80\u0026ndash;0.86) for MPT, and 0.85 (95% CI: 0.83\u0026ndash;0.87) for wTPT. wTPT significantly outperformed all other plaque burden models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). wTPT also demonstrated superior performance compared to the other plaque burden measures in identifying individuals with prior IHD and ischemic stroke, except for stroke, where the difference between wTPT (0.78 [95% CI: 0.72\u0026ndash;0.84]) and MPT (0.76 [95% CI: 0.69\u0026ndash;0.82]) did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.06).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ewTPT Discriminating Prior CV Disease Compared to Other Plaque Burden Measures\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ec-statistic (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArteries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.72 (0.71\u0026ndash;0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlaque count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.75 (0.71\u0026ndash;0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMPT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.83 (0.80\u0026ndash;0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewTPT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.85 (0.83\u0026ndash;0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIHD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArteries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.73 (0.71\u0026ndash;0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlaque count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.75 (0.71\u0026ndash;0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMPT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.83 (0.81\u0026ndash;0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewTPT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.86 (0.83\u0026ndash;0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArteries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.68 (0.63\u0026ndash;0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlaque count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0,67 (0.59\u0026ndash;0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMPT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.75 (0.69\u0026ndash;0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewTPT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.78 (0.72\u0026ndash;0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003ep-Value compared with weighted Total Plaque Thickness (wTPT)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eCV\u0026thinsp;=\u0026thinsp;cardiovascular. IHD\u0026thinsp;=\u0026thinsp;ischemic heart disease. IS\u0026thinsp;=\u0026thinsp;ischemic stroke. MPT\u0026thinsp;=\u0026thinsp;maximal plaque thickness. wTPT\u0026thinsp;=\u0026thinsp;weighted total plaque thickness.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGlobal wTPT yielded a higher c-statistic than femoral wTPT (0.84 [95% CI: 0.81\u0026ndash;0.86]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0142), which in turn outperformed carotid wTPT (0.78 [95% CI: 0.75\u0026ndash;0.81]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The c-statistic for the NORRISK-2 score and for the risk factor model were 0.73 (95% CI: 0.71\u0026ndash;0.77) and 0.84 (95% CI: 0.82\u0026ndash;0.87), respectively. Adding global, femoral, or carotid wTPT to the risk factor model significantly improved discrimination; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all. The combination of the risk factor model and global wTPT achieved a c-statistic of 0.88 (95% CI: 0.86\u0026ndash;0.90), with a continuous NRI of 0.71 (95% CI: 0.57\u0026ndash;0.84) and IDI of 0.06 (95% CI: 0.04\u0026ndash;0.07) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Similar improvements were observed for prior IHD and ischemic stroke (Supplemental Table\u0026nbsp;5), though for stroke, the added value of carotid wTPT did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.079). Comparable improvements were also observed when stratified by sex, however, increase in c-statistic for IHD in females, as well as ischemic stroke for both sexes, did not reach statistical significance (Supplemental Table\u0026nbsp;6).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIncremental Value of wTPT to Risk Factors in Discriminating CV Disease\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ec-statistic (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep Value*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous NRI (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIDI (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTest statistics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep Value**\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNORRISK-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.73 (0.71\u0026ndash;0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.84 (0.82\u0026ndash;0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewTPT global\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.85 (0.83\u0026ndash;0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewTPT\u0026thinsp;+\u0026thinsp;RF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.88 (0.86\u0026ndash;0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.71 (0.57\u0026ndash;0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.06 (0.04\u0026ndash;0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e122.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewTPT carotid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.78 (0.75\u0026ndash;0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewTPT carotid\u0026thinsp;+\u0026thinsp;RF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.86 (0.84\u0026ndash;0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.39 (0.25\u0026ndash;0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02 (0.01\u0026ndash;0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewTPT femoral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.84 (0.81\u0026ndash;0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewTPT femoral\u0026thinsp;+\u0026thinsp;RF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.87 (0.85\u0026ndash;0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67 (0.53\u0026ndash;0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05 (0.04\u0026ndash;0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e114.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e*p value compared with risk factors\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e**p Value Likelihood ratio test\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eCV\u0026thinsp;=\u0026thinsp;cardiovascular. IDI\u0026thinsp;=\u0026thinsp;integrated discrimination improvement. NRI\u0026thinsp;=\u0026thinsp;net reclassification improvement. IDI\u0026thinsp;=\u0026thinsp;integrated discrimination improvement. RF\u0026thinsp;=\u0026thinsp;risk factors, including sex, age, dyslipidemia, hypertension, past or current smoking, diabetes, family history and elevated lipoprotein a. Test statistics\u0026thinsp;=\u0026thinsp;likelihood ratio test statistics. wTPT\u0026thinsp;=\u0026thinsp;weighted Total Plaque Thickness.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003ePrognostic Cutoff Value for wTPT Plaque Burden\u003c/h3\u003e\n\u003cp\u003eUsing the Youden J statistic, a wTPT threshold of \u0026gt;\u0026thinsp;11.4 mm was identified as the optimal cut point for discriminating established CV disease, yielding a sensitivity of 83.5%, specificity of 71.0%, positive predictive value of 11.3%, and a negative predictive value of 99.0%. The odds of having prior CV disease in participants above this threshold was 4.6 (95% CI: 3.1\u0026ndash;6.9). When the wTPT cutoff was added to the risk factor model, the c-statistic increased to 0.87 (95% CI 0.85\u0026ndash;0.89). Furthermore, the continuous NRI was 0.73 (0.61\u0026ndash;0.86) and the IDI was 0.017 (95% CI: 0.012\u0026ndash;0.021).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings demonstrate that wTPT, a novel method based on the weighted sum of plaque thickness across 12 vascular segments in carotid and femoral arteries, is superior to conventional models to measure plaque burden in discriminating between individuals with prior CV disease versus those without prior disease. Furthermore, wTPT added significant predictive value beyond traditional risk factors, as reflected by an improved c-statistic and a marked increase in NRI. Notably, global wTPT provided superior discrimination compared to femoral or carotid wTPT alone, with assessments of femoral burden outperforming that of carotid burden. Additionally, individuals with high wTPT plaque burden had substantially elevated odds of prior CV disease compared with those classified by the conventional carotid MPT measure.\u003c/p\u003e\n\u003ch3\u003eAssessing Subclinical Atherosclerosis by Ultrasound\u003c/h3\u003e\n\u003cp\u003eStudies of plaque prevalence in low to medium risk individuals has varied substantially, particularly for carotid plaques.(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) However, with modern ultrasound technology and experienced sonographers, high prevalence rates of carotid plaque have been reported.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) In our study, plaque was present in 95.8% of participants without prior CV disease, indicating that a simple binary classification of plaque presence offers limited discriminatory value for risk prediction.\u003c/p\u003e\u003cp\u003ePlaque volume assessed by 3D ultrasound may represent the most comprehensive measure of subclinical atherosclerosis, incorporating plaque number, height, width, and length.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) However, this method is time-consuming and impractical for routine clinical use.(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) Furthermore, the BioImage study showed that carotid maximal plaque thickness (MPT) had comparable predictive utility to total plaque volume (TPV).(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) wTPT serves as a surrogate for TPV, integrating plaque number, thickness, and length, while being more time-efficient to acquire.(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) Since wTPT relies on detailed 2D scanning of all arterial segments, it may also detect a broader range of subclinical disease compared to standardised 3D sweeps.(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) We have previously shown that wTPT correlates more strongly with estimated CV risk than MPT, plaque count, or number of affected arteries (Unpublished data under review). Consistently, in this study, wTPT outperformed all other plaque burden measures in discriminating prior CV disease. Adding wTPT to both NORRISK-2 and the traditional risk factor model significantly improved the c-statistic and NRI, supporting wTPT as a practical and predictive tool for assessing atherosclerotic burden in clinical settings.\u003c/p\u003e\n\u003ch3\u003eFemoral Plaque Burden\u003c/h3\u003e\n\u003cp\u003eFemoral plaque burden has been shown to be more extensive than carotid plaque burden (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) and more closely correlated with coronary artery calcium (CAC) scores.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Additionally, it improves CV risk prediction when added to traditional risk factors and carotid plaque burden.(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) Nevertheless, most ultrasound-based studies have focused solely on the carotid arteries.\u003c/p\u003e\u003cp\u003eRecently, we demonstrated that femoral plaque burden exceeded carotid burden,\u003c/p\u003e\u003cp\u003eand that global wTPT correlated most strongly with estimated CV risk, with the femoral territory contributing more than the carotid territory (Unpublished data under review). In the present study, global wTPT had the highest discriminatory power for prior CV disease, followed by femoral and then carotid wTPT. These findings were consistent across sexes. The superior performance of femoral over carotid wTPT reinforces the importance of including femoral arteries when evaluating subclinical atherosclerosis by ultrasound for CV risk assessment.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDiscrimination of IHD and Ischemic Stroke\u003c/h2\u003e\u003cp\u003eThe ACE1950 study found that carotid plaque burden was more strongly associated with incident ischemic stroke than with MACE, likely due to the role of carotid plaques as a potential thromboembolic source.(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) However, in the MESA study, the association seemed to be higher between carotid plaque presence and both CV disease and coronary heart disease, compared to ischemic stroke or transient ischemic attack.(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) In the present study, predictive performance of both NORRISK-2, the risk factor model and all plaque burden models seemed to be lower for ischemic stroke, compared to both IHD and CV disease. As only a minority of ischemic strokes are caused by large vessel atherosclerosis, whereas most IHD are caused by rupture of coronary artery plaques, these findings might not be too surprising.(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eStudy Limitations\u003c/h2\u003e\u003cp\u003eFor measures of plaque burden to yield clinically meaningful information, they must improve prediction beyond traditional risk models. However, conventional risk scores like NORRISK-2 are not designed to discriminate prior CV disease. Participants with established disease often initiate lifestyle changes and medical therapy, potentially altering their current risk profile. Indeed, as expected, use of lipid-lowering, antihypertensive, antidiabetic, and antithrombotic medications was significantly higher in those with established CV disease in our study. While the proportion of current smokers was similar between groups, former smoking was more common among those with prior disease, and no significant difference in abdominal obesity was observed. Given these factors, it is unsurprising that NORRISK-2 demonstrated only modest discriminatory ability (c-statistic 0.73; 95% CI:0.71\u0026ndash;0.77). To better reflect underlying risk, we evaluated an alternative model incorporating age, sex, dyslipidemia, hypertension, diabetes, elevated Lp(a), past or current smoking, and family history. In this context, where 90% of participants with prior CV disease were on lipid-lowering therapy, dyslipidemia likely provided stronger risk stratification than total cholesterol, which is used in NORRISK-2. This risk factor model achieved a relatively high c-statistic of 0.84 (95% CI: 0.82\u0026ndash;0.87), supporting the clinical value of the observed improvements in discrimination and NRI when adding wTPT. Additionally, as lipid lowering therapy reduces the progression of subclinical atherosclerosis and may in fact reduce plaque volume,(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) the added value of wTPT for identifying prior CV disease might be underestimated. Moreover, the multivariate ORs for prior CV disease observed in the highest carotid MPT quartile in our study were very similar to the associations observed between carotid MPT and incident CV disease in recent studies.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eIn the present study, radiologists were not blinded to participants\u0026rsquo; risk factors or history of prior CV disease during ultrasound examinations. This introduces the potential for observer bias, possibly leading to higher plaque burden registration in participants with established disease. However, the high plaque prevalence even among participants without prior CV disease suggests that any such bias was likely minimal. Moreover, it is unlikely that this limitation significantly influenced the comparative performance of different plaque burden measures or vascular territories.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eStrengths of the Study\u003c/h2\u003e\u003cp\u003eUltrasound examinations were conducted by radiologists with extensive experience in vascular imaging, using standardised equipment. Plaque assessment was comprehensive, covering 12 vascular segments across both carotid and femoral territories, and allowing for direct comparison of multiple plaque burden measures. Importantly, examinations were performed in routine clinical settings rather than specialised research environments, supporting the generalisability of our findings. The study included a large, diverse cohort spanning a wide age range and multiple regions of Norway. Finally, we applied a broad range of statistical measures to assess the relationship between plaque burden and prior CV disease, with consistent results supporting the superior performance of wTPT.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eGlobal wTPT showed superior discrimination of established CV disease compared to established plaque measures and added substantial predictive information beyond traditional risk factors. These findings support wTPT as a marker of systemic atherosclerosis and a strong candidate for outcome-based risk refinement.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eP-SONAR (Prospective Screening Of Non-invasive Atherosclerosis Risk), CV (cardiovascular), wTPT (weighted total plaque thickness), MPT (maximal plaque thickness), TPV (total plaque volume), CAC (coronary artery calcium), SCORE (Systematic Coronary Risk Evaluation), Lp(a) (Lipoprotein a), IHD (ischemic heart disease)\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained by Regional Committees for Medical and Health Research Ethics in Norway (ref. number 258985). All participants provided written informed consent prior to inclusion. The study involved non-invasive vascular ultrasound only, and no additional procedures beyond routine clinical health-check assessments were performed.\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eBecause of the sensitive nature of the data in this study, participants were assured that raw data would remain confidential and would not be shared.\u003c/p\u003e\n\u003ch3\u003eConflict of interest\u003c/h3\u003e\n\u003cp\u003eThe present study was funded by the private health care company Austad Diagnostikk. G. Austad is employed and part owner of Austad Diagnostikk. The other authors have no relationships to disclose. The study was conducted in cooperation with Oslo University Hospital. An external monitor, Link Medical, supervised the study.\u003c/p\u003e\n\u003ch3\u003eDeclaration of Generative AI and AI-assisted technologies in the writing process\u003c/h3\u003e\n\u003cp\u003eThere is nothing to disclose.\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eWe acknowledge Philips Healthcare for providing medical equipment; and Austad Diagnostikk AS for funding of the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCardiovascular diseases [Internet]. [cited 2025 May 20]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/health-topics/cardiovascular-diseases\u003c/span\u003e\u003cspan address=\"https://www.who.int/health-topics/cardiovascular-diseases\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNasir K, Blankstein R. Transforming the Cardiovascular Disease Prevention Paradigm: See Disease, Treat Disease. JAMA. 2025;333(16):1398\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMortensen MB, Falk E. Primary Prevention With Statins in the Elderly. JACC. 2018;71(1):85\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez-Friera L, Pe\u0026ntilde;alvo JL, Fern\u0026aacute;ndez-Ortiz A, Iba\u0026ntilde;ez B, L\u0026oacute;pez-Melgar B, Laclaustra M, et al. Prevalence, Vascular Distribution, and Multiterritorial Extent of Subclinical Atherosclerosis in a Middle-Aged Cohort. Circulation. 2015;131(24):2104\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaclaustra M, Casasnovas JA, Fern\u0026aacute;ndez-Ortiz A, Fuster V, Le\u0026oacute;n-Latre M, Jim\u0026eacute;nez-Borreguero LJ, et al. Femoral and Carotid Subclinical Atherosclerosis Association With Risk Factors and Coronary Calcium: The AWHS Study. J Am Coll Cardiol. 2016;67(11):1263\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIhle-Hansen H, Vigen TV, Berge TB, Walle-Hansen MWH, Hagberg GH, Ihle-Hansen HIH, et al. Carotid plaque score for stroke and cardiovascular risk prediction in a middle-aged cohort from the general population: the akershus cardiac examination 1950 study. Eur Heart J. 2023;44(Supplement2):ehad6552414.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaber 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: the BioImage study. J Am Coll Cardiol. 2015;65(11):1065\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInaba Y, Chen JA, Bergmann SR. Carotid plaque, compared with carotid intima-media thickness, more accurately predicts coronary artery disease events: A meta-analysis. Atherosclerosis. 2012;220(1):128\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSirimarco G, Amarenco P, Labreuche J, Touboul PJ, Alberts M, Goto S, et al. Carotid Atherosclerosis and Risk of Subsequent Coronary Event in Outpatients With Atherothrombosis. Stroke. 2013;44(2):373\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBao X, Xu B, Lind L, Engstr\u0026ouml;m G. Carotid ultrasound and systematic coronary risk assessment 2 in the prediction of cardiovascular events. Eur J Prev Cardiol. 2023;30(10):1007\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eL\u0026oacute;pez-Melgar B, Fern\u0026aacute;ndez-Friera L, Oliva B, Garc\u0026iacute;a-Ruiz JM, Pe\u0026ntilde;alvo JL, G\u0026oacute;mez-Talavera S, et al. Subclinical Atherosclerosis Burden by 3D Ultrasound in Mid-Life: The PESA Study. J Am Coll Cardiol. 2017;70(3):301\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGudmundsson EF, Bj\u0026ouml;rnsdottir G, Sigurdsson S, Andersen K, Thorsson B, Aspelund T, et al. Carotid plaque is strongly associated with coronary artery calcium and predicts incident coronary heart disease in a population-based cohort. Atherosclerosis. 2022;346:117\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSillesen H, Sartori S, Sandholt B, Baber U, Mehran R, Fuster V. Carotid plaque thickness and carotid plaque burden predict future cardiovascular events in asymptomatic adult Americans. Eur Heart J Cardiovasc Imaging. 2018;19(9):1042\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGepner AD, Young R, Delaney JA, Tattersall MC, Blaha MJ, Post WS, et al. Comparison of Coronary Artery Calcium Presence, Carotid Plaque Presence, and Carotid Intima-Media Thickness for Cardiovascular Disease Prediction in the Multi-Ethnic Study of Atherosclerosis. Circ Cardiovasc Imaging. 2015;8(1):e002262.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavidsson L, Fagerberg B, Bergstr\u0026ouml;m G, Schmidt C. Ultrasound-assessed plaque occurrence in the carotid and femoral arteries are independent predictors of cardiovascular events in middle-aged men during 10 years of follow-up. Atherosclerosis. 2010;209(2):469\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAustad G, Geitung JT, Tonstad S. Validation and Reproducibility of Total Plaque Thickness in Carotid and Femoral Arteries Using Ultrasound. Ultrasound Med Biol. 2024;50(2):207\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWMA - The World Medical Association-WMA Declaration of Helsinki. \u0026ndash; Ethical Principles for Medical Research Involving Human Subjects [Internet]. [cited 2022 Sep 20]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/\u003c/span\u003e\u003cspan address=\"https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTouboul P, Hennerici M, Meairs S, Adams H, Amarenco P, Bornstein N et al. Mannheim Carotid Intima-Media Thickness and Plaque Consensus (2004-2006-2011): 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 Basel Switz. 2012;34(4):290\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSelmer R, Igland J, Ariansen I, Tverdal A, Nj\u0026oslash;lstad I, Furu K, et al. NORRISK 2: A Norwegian risk model for acute cerebral stroke and myocardial infarction. Eur J Prev Cardiol. 2017;24(7):773\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDahl M, Lindholt J, S\u0026oslash;gaard R, Refsgaard J, Svenstrup D, Moeslund NJ, et al. Relevance of the Viborg Population Based Screening Programme (VISP) for Cardiovascular Conditions Among 67 Year Olds: Attendance Rate, Prevalence, and Proportion of Initiated Cardiovascular Medicines Stratified By Sex. Eur J Vasc Endovasc Surg. 2023;66(1):119\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIhle-Hansen H, Vigen T, Ihle‐Hansen H, R\u0026oslash;nning OM, Berge T, Thommessen B, et al. Prevalence of Carotid Plaque in a 63‐ to 65‐Year‐Old Norwegian Cohort From the General Population: The ACE (Akershus Cardiac Examination) 1950 Study. J Am Heart Assoc. 2018;7(10):e008562.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElsheikh S, Hill A, Irving G, Lip GYH, Abdul-Rahim AH. Atrial fibrillation and stroke: State-of-the-art and future directions. Curr Probl Cardiol. 2024;49(1, Part C):102181.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFalk E, Nakano M, Bentzon JF, Finn AV, Virmani R. Update on acute coronary syndromes: the pathologists\u0026rsquo; view. Eur Heart J. 2013;34(10):719\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndelius L, Mortensen MB, N\u0026oslash;rgaard BL, Abdulla J. Impact of statin therapy on coronary plaque burden and composition assessed by coronary computed tomographic angiography: a systematic review and meta-analysis. Eur Heart J - Cardiovasc Imaging. 2018;19(8):850\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"subclinical atherosclerosis, cardiovascular disease, plaque burden, cardiovascular risk, ultrasonography, peripheral artery disease, NORRISK-2, risk prediction","lastPublishedDoi":"10.21203/rs.3.rs-8215491/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8215491/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eUltrasound-imaging of subclinical atherosclerosis may refine cardiovascular (CV) risk assessment, but quantification methods vary and often include carotid arteries only. Because atherosclerosis is multiterritory, global (carotid\u0026ndash;femoral) plaque quantification may better reflect systemic burden. We examined whether a novel multiterritory measure, weighted total plaque thickness (wTPT), better discriminates established CV disease than traditional risk factors and plaque measures.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn 5 418 participants (59.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1 years; 53.0% women) from the Prospective Screening Of Non-invasive Atherosclerosis Risk study, plaque burden was assessed across 12 carotid and femoral segments using wTPT, maximal plaque thickness (MPT), plaque count, and number of arteries with plaque. Discrimination of prior CV disease was evaluated using c-statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eGlobal wTPT showed the strongest discrimination of established CV disease (c-statistic 0.85 [95% CI: 0.83\u0026ndash;0.87]), outperforming alternative plaque measures (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Adding global wTPT to risk factors improved discrimination from 0.84 to 0.89 (95% CI: 0.87\u0026ndash;0.91), NRI 0.71 (95% CI: 0.57\u0026ndash;0.84) and IDI 0.058 (95% CI: 0.004\u0026ndash;0.071). Femoral wTPT outperformed carotid wTPT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and global wTPT exceeded both (p\u0026thinsp;=\u0026thinsp;0.014). Across wTPT quartiles, odds of prior CV disease increased stepwise: 2.0 (95% CI: 0.89\u0026ndash;4.4), 4.2 (95% CI: 2.0\u0026ndash;8.6), and 10.0 (95% CI: 5.0\u0026ndash;20.1) versus the lowest quartile.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eGlobal wTPT showed superior discrimination of established CV disease and added substantial predictive information beyond standard risk factors. These findings support wTPT as a marker of systemic atherosclerosis and a strong candidate for outcome-based risk refinement.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e\u003cp\u003eclinicaltrials.gov. Identifier: \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNCT06933745\u003c/span\u003e. Registered 22 april 2025\u003c/p\u003e","manuscriptTitle":"Discriminating Established Cardiovascular Disease Using a Novel Multiterritory Ultrasound Plaque Burden Measure (wTPT): Findings From the P-SONAR Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-12 13:30:34","doi":"10.21203/rs.3.rs-8215491/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-05T10:08:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-04T08:25:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78288716115410766650767368732406504634","date":"2026-02-04T08:15:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-03T07:24:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198100669506015241999532018740830278661","date":"2026-01-15T03:39:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159109275527920941878857473155156347282","date":"2025-12-11T12:16:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-09T10:29:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-09T10:27:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-08T08:57:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-07T17:27:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-12-01T19:21:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bea3da8f-afd6-40fb-96c5-c03d6961ff09","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:11:47+00:00","versionOfRecord":{"articleIdentity":"rs-8215491","link":"https://doi.org/10.1186/s12872-026-05807-8","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2026-04-09 15:58:39","publishedOnDateReadable":"April 9th, 2026"},"versionCreatedAt":"2025-12-12 13:30:34","video":"","vorDoi":"10.1186/s12872-026-05807-8","vorDoiUrl":"https://doi.org/10.1186/s12872-026-05807-8","workflowStages":[]},"version":"v1","identity":"rs-8215491","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8215491","identity":"rs-8215491","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}