Resistant Atherosclerosis

Clinical significance of atherosclerotic risk factors differs in early and advanced stages of plaque formation: A longitudinal study in the general population

BACKGROUND

Carotid plaque is a well-known prognostic factor for cardiovascular diseases. It is unclear which risk factors are associated with the transformation of carotid plaque over time. In this longitudinal study, we examined the risk factors related to carotid plaque progression.

METHODS

We enrolled 738 men without medication (mean age: 55 ± 10 years) who underwent the first and second health examinations. We measured carotid plaque thickness (PT) at three points of the right and left carotid artery. Plaque score (PS) was calculated by summing all the PTs. We divided the PS into three groups: None-group (PS <1.1), Early-group (1.1 ≤ PS <5.1), and Advanced-group (PS ≥5.1). We analyzed the relationship between PS progression and parameters such as age, body mass index, systolic blood pressure (SBP), fasting blood sugar, low-density lipoprotein cholesterol (LDL-C), and smoking and exercise habits.

RESULTS

In multivariable logistic regression analysis, age and SBP were independent factors for PS progression from none to early stages (age, OR 1.07, p = 0.002; SBP, 10 mmHg, OR 1.27, p = 0.041). Age, follow-up period and LDL-C were independently associated factors for PS progression from early to advanced stages (age, OR 1.08,p < 0.001; follow-up period OR1.19, p = 0.041; LDL-C, 10 mg/dL, OR 1.10, p = 0.049).

CONCLUSIONS

SBP was independently associated with the progress of early atherosclerosis, while LDL-C was independently associated with the progression of advanced atherosclerosis in the general population. Further studies are needed to assess whether early control of SBP and LDL-C levels can reduce the occurrence of future cardiovascular events.

C-reactive protein and coronary atheroma regression following statin therapy: A meta-regression of randomized controlled trials

OBJECTIVE

Several clinical trials have indicated that statins stabilize and reverse atherosclerotic plaque. However, different studies have provided inconsistent findings regarding mechanisms and influencing factors of plaque regression under statin therapy. Apart from lipid-lowering effect, statins have pleiotropic effects including anti inflammation in humans. In this study, meta-analysis and meta-regression were used to determine the effects of statin medications on coronary plaque volume. Meanwhile, to assess whether statins promote plaque regression effect was related to their anti-inflammatory ability, the impact of CRP/hsCRP reduction during statin therapy on plaque regression was investigated.

METHODS

Up to June 15, 2022, a systematic PubMed, EMBASE, and Cochrane search was performed for randomized controlled trials that assessed treatment effect using total atheroma volume (TAV), percent atheroma volume (PAV), or plaque volume (PV). Only CRP/hsCRP and LDL-C values reported before and after treatment were considered.

RESULTS

12 studies (2,812 patients with heart and/or vascular disease) fulfilled the inclusion criteria and were included in the systematic review. A meta-analysis of 15 statin-treated arms reported a significant reduction in change of TAV/PV [standardized mean difference (SMD): -0.27, 95% confidence intervals (-CI): -0.42, -0.12, p < 0.001], compared with the control arms. Another meta-analysis of 7 trials also found that patients in the intervention group had a significant reduction in change of PAV (SMD: -0.16, 95% CI: -0.29, -0.03, p = 0.019), compared with those in the control group. Meta-regressionanalysis revealed that the percent change of CRP/hsCRP was significantly associated with SMD in change of TAV/PV after adjusting for percent change of LDL-C, age, gender and study duration. Meta-regression analysis showed that percent change of CRP/hsCRP statistically influenced SMD in change of PAV, when percent change of CRP/hsCRP was included separately. However, the percent change of CRP/hsCRP was not significantly associated with SMD of PAV change after adjusting for all covariates.

CONCLUSION

In conclusion, statin therapy is beneficial for plaque regression. Statins promote plaque regression, which might be associated to their anti-inflammatory ability.

Cerebrovascular Disease, Cardiovascular Disease, and Chronic Kidney Disease: Interplays and Influences

PURPOSE OF REVIEW

We reviewed reasons for the high cardiovascular risk (CVD) of patients with chronic kidney disease (CKD), and explored alternatives to treatment of traditional risk factors to reduce CVD in CKD.

RECENT FINDINGS

Besides traditional risk factors, patients with CKD are exposed to uremic toxins of two kinds: systemically derived toxins include asymmetric dimethylarginine (ADMA), total homocysteine (tHcy), thiocyanate, tumor necrosis factor alpha, and interleukin 6. Gut-derived uremic toxins (GDUT), products of the intestinal microbiome, include hippuric acid, indoxyl sulfate, p-cresyl sulfate, p-cresyl glucuronide, phenylacetylglutamine, and trimethylamine N-oxide (TMAO). Cyanocobalamin is toxic in patients with CKD. Approaches to reducing plasma levels of these uremic toxins would include diet to reduce GDUT, kidney transplantation, more intensive dialysis, and vitamin therapy to lower tHcy with methylcobalamin rather than cyanocobalamin. The high CVD risk in CKD requires consideration of therapies beyond treatment of traditional risk factors.

Risk factors for carotid plaque progression after optimising the risk factor treatment: substudy results of the Atherosclerotic Plaque Characteristics Associated with a Progression Rate of the Plaque and a Risk of Stroke in Patients with the carotid Bifurcation Plaque Study (ANTIQUE)

BACKGROUND AND AIM

Carotid plaque progression contributes to increasing stroke risk. The study aims to identify factors influencing carotid plaque thickness progression after changing the preventive treatment to the 'treating arteries instead of risk factors' strategy, that is, change in treatment depending on the progression of atherosclerosis.

METHODS

The study participants who completed sonographic controls over the course of 3 years were enrolled to the analysis. Duplex sonography of cervical arteries was performed in 6-month intervals with measurement of carotid plaque thickness. Plaque thickness measurement error (σ) was set as 3 SD. Only evidently stable and progressive plaques (defined as plaque thickness difference between initial and final measurements of ˂σ and >2σ, respectively) were included to analysis. Univariate and multivariate logistic regression analysis was performed to identify factors influencing plaque progression.

RESULTS

A total of 1391 patients (466 males, age 67.2±9.2 years) were enrolled in the study. Progressive plaque in at least one carotid artery was detected in 255 (18.3%) patients. Older age, male sex, greater plaque thickness, coronary heart disease, vascular surgery/stenting history and smoking were more frequently present in patients with progressive plaque (p˂0.05 in all cases). Multivariate logistic regression analysis identified only the plaque thickness (OR 1.850 for left side, 95% CI 1.398 to 2.449; and OR 1.376 for right side, 95% CI 1.070 to 1.770) as an independent factor influencing plaque progression.

CONCLUSION

Carotid plaque thickness corresponding to stenosis severity is the only independent risk factor for plaque thickness progression after optimising the prevention treatment.

TRIAL REGISTRATION NUMBER

NCT02360137.

Automated deep learning-based paradigm for high-risk plaque detection in B-mode common carotid ultrasound scans: an asymptomatic Japanese cohort study

BACKGROUND

The death due to stroke is caused by embolism of the arteries which is due to the rupture of the atherosclerotic lesions in carotid arteries. The lesion formation is over time, and thus, early screening is recommended for asymptomatic and moderate-risk patients. The previous techniques adopted conventional methods or semi-automated and, more recently, machine learning solutions. A handful of studies have emerged based on solo deep learning (SDL) models such as UNet architecture.

METHODS

The proposed research is the first to adopt hybrid deep learning (HDL) artificial intelligence models such as SegNet-UNet. This model is benchmarked against UNet and advanced conventional models using scale-space such as AtheroEdge 2.0 (AtheroPoint, CA, USA). All our resultant statistics of the three systems were in the order of UNet, SegNet-UNet, and AtheroEdge 2.0.

RESULTS

Using the database of 379 ultrasound scans from a Japanese cohort of 190 patients having moderate risk and implementing the cross-validation deep learning framework, our system performance using area-under-the-curve (AUC) for UNet, SegNet-UNet, and AtheroEdge 2.0 were 0.93, 0.94, and 0.95 (p<0.001), respectively. The coefficient of correlation between the three systems and ground truth (GT) were: 0.82, 0.89, and 0.85 (p<0.001 for all three), respectively. The mean absolute area error for the three systems against manual GT was 4.07±4.70 mm2, 3.11±3.92 mm2, 3.72±4.76 mm2, respectively, proving the superior performance SegNet-UNet against UNet and AtheroEdge 2.0, respectively. Statistical tests were also conducted for their reliability and stability.

CONCLUSIONS

The proposed study demonstrates a fast, accurate, and reliable solution for early detection and quantification of plaque lesions in common carotid artery ultrasound scans. The system runs on a test US image in < 1 second, proving overall performance to be clinically reliable.

Pre-Operative Plasma Extracellular Vesicle Proteins are Associated with a High Risk of Long Term Secondary Major Cardiovascular Events in Patients Undergoing Carotid Endarterectomy

OBJECTIVE

Patients undergoing carotid endarterectomy (CEA) maintain a substantial residual risk of major cardiovascular events (MACE). Improved risk stratification is warranted to select high risk patients qualifying for secondary add on therapy. Plasma extracellular vesicles (EVs) are involved in atherothrombotic processes and their content has been related to the presence and recurrence of cardiovascular events. The association between pre-operative levels of five cardiovascular disease related proteins in plasma EVs and the post-operative risk of MACE was assessed.

METHODS

In 864 patients undergoing CEA from 2002 to 2016 included in the Athero-Express biobank, three plasma EV subfractions (low density lipoprotein [LDL], high density lipoprotein [HDL], and tiny extracellular vesicles [TEX]) were isolated from pre-operative blood samples. Using an electrochemiluminescence immunoassay, five proteins were quantified in each EV subfraction: cystatin C, serpin C1, serpin G1, serpin F2, and CD14. The association between EV protein levels and the three year post-operative risk of MACE (any stroke, myocardial infarction, or cardiovascular death) was evaluated using multivariable Cox proportional hazard regression analyses.

RESULTS

During a median follow up of three years (interquartile range 2.2 - 3.0), 137 (16%) patients developed MACE. In the HDL-EV subfraction, increased levels of CD14, cystatin C, serpin F2, and serpin C1 were associated with an increased risk of MACE (adjusted hazard ratios per one standard deviation increase of 1.30, 95% confidence interval [CI] 1.15-1.48; 1.22, 95% CI 1.06-1.42; 1.36, 95% CI 1.16-1.61; and 1.29, 95% CI 1.10-1.51; respectively), independently of cardiovascular risk factors. No significant associations were found for serpin G1. CD14 improved the predictive value of the clinical model encompassing cardiovascular risk factors (net re-classification index = 0.16, 95% CI 0.08-0.21).

CONCLUSION

EV derived pre-operative plasma levels of cystatin C, serpin C1, CD14, and serpin F2 were independently associated with an increased long term risk of MACE after CEA and are thus markers for residual cardiovascular risk. EV derived CD14 levels could improve the identification of high risk patients who may benefit from secondary preventive add on therapy in order to reduce future risk of MACE.

Triglyceride/High-Density Lipoprotein Cholesterol Ratio: A Clue to Metabolic Syndrome, Insulin Resistance, and Severe Atherosclerosis

High serum levels of triglycerides (Tg) and low levels of high-density lipoprotein cholesterol (HDL-C) are characteristic of the Metabolic Syndrome (MetS). We assessed the ratio of Tg to HDL-C as a way to identify MetS and insulin resistance. We also evaluated its association with severity of carotid atherosclerosis. Data were analyzed from three cohorts totaling 13,908 participants. MetS was defined according to the International Diabetes Federation criteria. Optimal cut-off for Tg/HDL-C ratio was obtained using Youden's index in receiver-operating characteristic (ROC) curve analyses. The risk of MetS and IR in those with a Tg/HDL-C ratio above the optimum cutoff was evaluated by logistic regression analysis. A Tg/HDL-C ratio above the optimal cutoff level significantly increased the odds ratio for MetS in the three cohorts (OR 6.00, 4.04, and 3.50, least in the healthy population), identified insulin resistance defined by the homeostatic model of insulin resistance (HOMA-IR) (p < 0.0001), and was strongly associated with atherosclerosis severity (p = 0.0001). Tg/HDL-C ratio identifies persons with MetS, insulin resistance, and severe atherosclerosis. It should be used more widely to identify patients at high risk. This is clinically important because insulin resistance is treatable.

Association Between Plasma Lipoprotein-Associated Phospholipase A2 and Plaque Vulnerability in TIA Patients With Unilateral Middle Cerebral Artery Stenosis

Background: Plasma lipoprotein-associated phospholipase A2 (Lp-PLA2) has emerged as a novel biomarker for coronary atherosclerosis. However, the association between Lp-PLA2 and plaque vulnerability in atherosclerosis of cervicocerebral arteries remains poorly defined, especially for intracranial atherosclerotic stenosis (ICAS). We aimed to investigate the association between Lp-PLA2 and plaque vulnerability in transient ischemic attack (TIA) patients with unilateral middle cerebral artery stenoses (MCAs).

Methods: In this study, a total of 207 patients were enrolled from April 2017 to April 2020. Clinical data were collected, and MCA plaques were examined with high-resolution magnetic resonance imaging (HRMRI). Baseline characteristics of patients were collected during hospitalization. Statistical comparisons were performed using Pearson's chi-squared test, Mann–Whitney U test, and the Breslow–Day/Tarone's test for the determination of heterogeneity in different age strata. Multivariate binary logistic analysis was used to investigate the potential independent predictors that were highly correlated to plaque vulnerability.

Results: The results showed that a high Lp-PLA2 level (>221 ng/ml) was associated with plaque vulnerability in TIA patients with unilateral MCAs. High Lp-PLA2 was independently associated with plaque vulnerability in patients ≤ 60 years old [multivariate adjusted odds ratio (OR) = 9.854; 95% CI, 2.458–39.501] but not in patients >60 years old (multivariate adjusted OR = 1.901; 95% CI, 0.640–5.650). Predictors of plaque vulnerability in different age strata were also different.

Conclusion: Lp-PLA2 levels may be correlated to plaque vulnerability in TIA patients with unilateral MCAs and might be a diagnostic biomarker for plaque vulnerability in this kind of patients, especially for ones aged ≤ 60 years old.

Plaque burden assessment and attenuation measurement of carotid atherosclerotic plaque using virtual monoenergetic images in comparison to conventional polyenergetic images from dual-layer spectral detector CT

PURPOSE

To compare virtual monoenergetic images (VMIs) with conventional polyenergetic images (PI) of Dual-layer spectral detector CT angiography (DLCTA) in plaque burden assessment and attenuation measurement of carotid atherosclerotic plaques.

METHODS

Supra-aortic DLCTA imaging of thirty patients (8 female, mean ages 63.1 ± 7.5 years) were respectively reviewed. Lumen area, wall area, and calcified area of plaques were outlined and recorded. Normalized wall index (NWI) was calculated for plaque burden and compared between PI and different VMIs. The attenuation of the non-calcified, calcified area of the plaques, sternocleidomastoid muscle (SCM), as well as Z effective values were measured and compared.

RESULTS

Fifty carotid plaques (27 left, 23 right) of thirty patients were analyzed. The average values of lumen, wall, calcified areas and NWI on PI were 34.50 ± 20.57mm², 47.61 ± 19.94 mm², 5.25 mm² (1.35- 51.86 mm²), and 0.59 ± 0.16 respectively. No significant difference was found in the lumen area (p = 0.314), wall area (p = 0.600), and NWI (p = 0.980) between different VMIs and PI. A significant difference was found in the calcified area between VMIs and PI (p = 0.009). Attenuations of non-calcified and calcified components in carotid plaques were comparable to PI for 50-120 keV (all: p > 0.05) and 60-120 keVs (all p > 0.05), respectively. Z Effective values for non-calcified, calcified and SCM were 7.67 ± 0.42, 11.70 ± 1.22, and 7.45 ± 0.12, respectively.

CONCLUSIONS

Carotid plaque burden assessment was comparable between PI and VMIs at 40-120 keVs. Attenuations of non-calcified components in carotid plaques were comparable to PI for 50-120 keV VMIs of DLCTA. VMIs might provide more information on carotid plaque features.

Uses of ultrasound in stroke prevention

Ultrasound methods are useful in stroke prevention in several ways. Measurement of carotid plaque burden, as either total plaque area (TPA) or total plaque volume (TPV) are strong predictors of cardiovascular risk: much stronger than intima-media thickness, which does not represent true atherosclerosis, but a biologically and genetically distinct phenotype. Measurement of plaque burden is also useful for the study of genetics, and of new risk factors such as toxic products of the intestinal microbiome. Carotid plaque burden is highly correlated with and as predictive of risk as coronary calcium scores, but is less costly and does not require radiation. Furthermore, because carotid plaques change in time over a period of months, they can be used for a new approach to vascular prevention: "Treating arteries instead of treating risk factors". In high-risk patients with asymptomatic carotid stenosis (ACS), this approach, implemented in 2003 in our clinics, was associated with a >80% reduction of stroke and myocardial infarction over 2 years. "Treating arteries without measuring plaque would be like treating hypertension without measuring blood pressure". Ultrasound methods can also be used to assess plaque vulnerability, by detecting echolucency, ulceration and plaque inhomogeneity on assessment of plaque texture. Transcranial Doppler (TCD) embolus detection is useful for risk stratification in patients with ACS; patients with two or more microemboli in an hour of monitoring have a 1-year risk of 15.6%, vs. 1% without microemboli, so this very clearly distinguishes which patients with ACS could benefit from intervention. TCD saline studies are more sensitive than trans-esophageal echocardiography for detection of patent foramen ovale, and more predictive of recurrent stroke. These methods should be more widely used, to reduce the increasing burden of stroke in our aging populations.

Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models

Background

Statistically derived cardiovascular risk calculators (CVRC) that use conventional risk factors, generally underestimate or overestimate the risk of cardiovascular disease (CVD) or stroke events primarily due to lack of integration of plaque burden. This study investigates the role of machine learning (ML)-based CVD/stroke risk calculators (CVRC_ML) and compares against statistically derived CVRC (CVRC_Stat) based on (I) conventional factors or (II) combined conventional with plaque burden (integrated factors).

Methods

The proposed study is divided into 3 parts: (I) statistical calculator: initially, the 10-year CVD/stroke risk was computed using 13 types of CVRC_Stat (without and with plaque burden) and binary risk stratification of the patients was performed using the predefined thresholds and risk classes; (II) ML calculator: using the same risk factors (without and with plaque burden), as adopted in 13 different CVRC_Stat, the patients were again risk-stratified using CVRC_ML based on support vector machine (SVM) and finally; (III) both types of calculators were evaluated using AUC based on ROC analysis, which was computed using combination of predicted class and endpoint equivalent to CVD/stroke events.

Results

An Institutional Review Board approved 202 patients (156 males and 46 females) of Japanese ethnicity were recruited for this study with a mean age of 69±11 years. The AUC for 13 different types of CVRC_Stat calculators were: AECRS2.0 (AUC 0.83, P<0.001), QRISK3 (AUC 0.72, P<0.001), WHO (AUC 0.70, P<0.001), ASCVD (AUC 0.67, P<0.001), FRS_cardio (AUC 0.67, P<0.01), FRS_stroke (AUC 0.64, P<0.001), MSRC (AUC 0.63, P=0.03), UKPDS56 (AUC 0.63, P<0.001), NIPPON (AUC 0.63, P<0.001), PROCAM (AUC 0.59, P<0.001), RRS (AUC 0.57, P<0.001), UKPDS60 (AUC 0.53, P<0.001), and SCORE (AUC 0.45, P<0.001), while the AUC for the CVRC_ML with integrated risk factors (AUC 0.88, P<0.001), a 42% increase in performance. The overall risk-stratification accuracy for the CVRC_ML with integrated risk factors was 92.52% which was higher compared all the other CVRC_Stat.

Conclusions

ML-based CVD/stroke risk calculator provided a higher predictive ability of 10-year CVD/stroke compared to the 13 different types of statistically derived risk calculators including integrated model AECRS 2.0.

Does the Carotid Bulb Offer a Better 10-Year CVD/Stroke Risk Assessment Compared to the Common Carotid Artery? A 1516 Ultrasound Scan Study

The objectives of this study are to (1) examine the "10-year cardiovascular risk" in the common carotid artery (CCA) versus carotid bulb using an integrated calculator called "AtheroEdge Composite Risk Score 2.0" (AECRS2.0) and (2) evaluate the performance of AECRS2.0 against "conventional cardiovascular risk calculators." These objectives are met by measuring (1) image-based phenotypes and AECRS2.0 score computation and (2) performance evaluation of AECRS2.0 against 12 conventional cardiovascular risk calculators. The Asian-Indian cohort (n = 379) with type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), or hypertension were retrospectively analyzed by acquiring the 1516 carotid ultrasound scans (mean age: 55 ± 10.1 years, 67% males, ∼92% with T2DM, ∼83% with CKD [stage 1-5], and 87.5% with hypertension [stage 1-2]). The carotid bulb showed a higher 10-year cardiovascular risk compared to the CCA by 18% (P < .0001). Patients with T2DM and/or CKD also followed a similar trend. The carotid bulb demonstrated a superior risk assessment compared to CCA in patients with T2DM and/or CKD by showing: (1) ∼13% better than CCA (0.93 vs 0.82, P = .0001) and (2) ∼29% better compared with 12 types of risk conventional calculators (0.93 vs 0.72, P = .06).

Traffic-Related Air Pollution and Carotid Plaque Burden in a Canadian City With Low-Level Ambient Pollution

Background

The association between fine particulate matter and cardiovascular disease has been convincingly demonstrated. The role of traffic‐related air pollutants is less clear. To better understand the role of traffic‐related air pollutants in cardiovascular disease development, we examined associations between NO₂, carotid atherosclerotic plaque, and cardiometabolic disorders associated with cardiovascular disease.

Methods and Results

Cross‐sectional analyses were conducted among 2227 patients (62.9±13.8 years; 49.5% women) from the Stroke Prevention and Atherosclerosis Research Centre (SPARC) in London, Ontario, Canada. Total carotid plaque area measured by ultrasound, cardiometabolic disorders, and residential locations were provided by SPARC medical records. Long‐term outdoor residential NO₂ concentrations were generated by a land use regression model. Associations between NO₂, total carotid plaque area, and cardiometabolic disorders were examined using multiple regression models adjusted for age, sex, smoking, and socioeconomic status. Mean NO₂ was 5.4±1.6 ppb in London, Ontario. NO₂ was associated with a significant increase in plaque (3.4 mm² total carotid plaque area per 1 ppb NO₂), exhibiting a linear dose‐response. NO₂ was also positively associated with triglycerides, total cholesterol, and the ratio of low‐ to high‐density lipoprotein cholesterol (P<0.05). Diabetes mellitus mediated the relationship between NO₂ and total carotid plaque area (P<0.05).

Conclusions

Our results demonstrate that even low levels of traffic‐related air pollutants are linked to atherosclerotic plaque burden, an association that may be partially attributable to pollution‐induced diabetes mellitus. Our findings suggest that reducing ambient concentrations in cities with NO₂ below current standards would result in additional health benefits. Given the billions of people exposed to traffic emissions, our study supports the global public health significance of reducing air pollution.

Integration of estimated glomerular filtration rate biomarker in image-based cardiovascular disease/stroke risk calculator: a south Asian-Indian diabetes cohort with moderate chronic kidney disease

BACKGROUND

Recently, a 10-year image-based integrated calculator (called AtheroEdge Composite Risk Score-AECRS1.0) was developed which combines conventional cardiovascular risk factors (CCVRF) with image phenotypes derived from carotid ultrasound (CUS). Such calculators did not include chronic kidney disease (CKD)-based biomarker called estimated glomerular filtration rate (eGFR). The novelty of this study is to design and develop an advanced integrated version called-AECRS2.0 that combines eGFR with image phenotypes to compute the composite risk score. Furthermore, AECRS2.0 was benchmarked against QRISK3 which considers eGFR for risk assessment.

METHODS

The method consists of three major steps: 1) five, current CUS image phenotypes (CUSIP) measurements using AtheroEdge system (AtheroPoint, CA, USA) consisting of: average carotid intima-media thickness (cIMTave), maximum cIMT (cIMTmax), minimum cIMT (cIMTmin), variability in cIMT (cIMTV), and total plaque area (TPA); 2) five, 10-year CUSIP measurements by combining these current five CUSIP with 11 CCVRF (age, ethnicity, gender, body mass index, systolic blood pressure, smoking, carotid artery type, hemoglobin, low-density lipoprotein cholesterol, total cholesterol, and eGFR); 3) AECRS2.0 risk score computation and its comparison to QRISK3 using area-under-the-curve (AUC).

RESULTS

South Asian-Indian 339 patients were retrospectively analyzed by acquiring their left/right common carotid arteries (678 CUS, mean age: 54.25±9.84 years; 75.22% males; 93.51% diabetic with HbA1c ≥6.5%; and mean eGFR 73.84±20.91 mL/min/1.73m²). The proposed AECRS2.0 reported higher AUC (AUC=0.89, P<0.001) compared to QRISK3 (AUC=0.51, P<0.001) by ~74% in CKD patients.

CONCLUSIONS

An integrated calculator AECRS2.0 can be used to assess the 10-year CVD/stroke risk in patients suffering from CKD. AECRS2.0 was much superior to QRISK3.

Morphological Carotid Plaque Area Is Associated With Glomerular Filtration Rate: A Study of South Asian Indian Patients With Diabetes and Chronic Kidney Disease

We evaluated the association between automatically measured carotid total plaque area (TPA) and the estimated glomerular filtration rate (eGFR), a biomarker of chronic kidney disease (CKD). Automated average carotid intima-media thickness (cIMTave) and TPA measurements in carotid ultrasound (CUS) were performed using AtheroEdge (AtheroPoint). Pearson correlation coefficient (CC) was then computed between the TPA and eGFR for (1) males versus females, (2) diabetic versus nondiabetic patients, and (3) between the left and right carotid artery. Overall, 339 South Asian Indian patients with either type 2 diabetes mellitus (T2DM) or CKD, or hypertension (stage 1 or stage 2) were retrospectively analyzed by acquiring cIMTave and TPA measurements of their left and right common carotid arteries (CCA; total CUS: 678, mean age: 54.2 ± 9.8 years; 75.2% males; 93.5% with T2DM). The CC between TPA and eGFR for different scenarios were (1) for males and females -0.25 (P < .001) and -0.35 (P < .001), respectively; (2) for T2DM and non-T2DM -0.26 (P < .001) and -0.49 (P = .02), respectively, and (3) for left and right CCA -0.25 (P < .001) and -0.23 (P < .001), respectively. Automated TPA is an equally reliable biomarker compared with cIMTave for patients with CKD (with or without T2DM) with subclinical atherosclerosis.

Rationale for screening selected patients for asymptomatic carotid artery stenosis

Stroke is a leading cause of death and disability worldwide. Approximately 15% of all first-ever strokes occur due to atheroembolism from a previously undetected/untreated asymptomatic carotid stenosis (ACS). Despite that, international guidelines do not recommend screening for ACS. The rationale for not recommending screening include: (a) the harm associated with screening, (b) the questionable clinical benefit associated with surgery, (c) the lack of proven reduction in the risk of stroke, (d) the large number of false positive/false negative tests, and (e) the cost-effectiveness of such screening programs. A critical analysis of each of these arguments is presented. Patients with ACS have a very high risk of all-cause and cardiac mortality. Detection of ACS should not be viewed as an indication for surgery, but rather as an opportunity to implement best medical treatment (BMT) and lifestyle changes to prevent not only strokes, but also cardiac events. The implementation of screening programs for abdominal aortic aneurysms (AAAs) has led to a considerable reduction in the number of ruptured AAAs and AAA-related deaths. Similarly, screening high-risk individuals for ACS would enable timely identification of patients with ACS and implementation of BMT and lifestyle measures to prevent future strokes and cardiac events.

Global perspective on carotid intima-media thickness and plaque: should the current measurement guidelines be revisited?

Carotid intima-media thickness (cIMT) and carotid plaque (CP) currently act as risk predictors for CVD/Stroke risk assessment. Over 2000 articles have been published that cover either use cIMT/CP or alterations of cIMT/CP and additional image-based phenotypes to associate cIMT related markers with CVD/Stroke risk. These articles have shown variable results, which likely reflect a lack of standardization in the tools for measurement, risk stratification, and risk assessment. Guidelines for cIMT/CP measurement are influenced by major factors like the atherosclerosis disease itself, conventional risk factors, 10-year measurement tools, types of CVD/Stroke risk calculators, incomplete validation of measurement tools, and the fast pace of computer technology advancements. This review discusses the following major points: 1) the American Society of Echocardiography and Mannheim guidelines for cIMT/CP measurements; 2) forces that influence the guidelines; and 3) calculators for risk stratification and assessment under the influence of advanced intelligence methods. The review also presents the knowledge-based learning strategies such as machine and deep learning which may play a future role in CVD/stroke risk assessment. We conclude that both machine learning and non-machine learning strategies will flourish for current and 10-year CVD/Stroke risk prediction as long as they integrate image-based phenotypes with conventional risk factors.

Increased carotid plaque burden in patients with family medical history of premature cardiovascular events in the absence of classical risk factors

INTRODUCTION

The hypothesis that relates atherosclerosis to traditional risk factors (TRF) seems to be not as adequate as previously thought; other risk factors (RF) need to be considered to prevent atherosclerosis progression. Although a family medical history of premature cardiovascular events (FHx) has been considered the putative RF for decades, it has not been incorporated routinely into cardiovascular risk evaluation along with another RF. The objective of this study was to investigate whether FHx is associated with a higher atherosclerotic burden, measured as carotid total plaque area (TPA) in a population having no traditional RF.

MATERIAL AND METHODS

The study included 4,351 primary care patients in Argentina. After excluding a personal history of cardiovascular disease (CVD) and TRF: hypertension, diabetes mellitus, hypercholesterolemia, smoking history, and body mass index (BMI) > 25 kg/cm², 34 patients with FHx were identified. Compared to 56 matched controls TPA was 86% higher in FHx patients (p < 0.05). A second analysis performed in hypertensive patients but no other TRF; 32 patients with FHx were identified.

RESULTS

Compared with 44 matched controls, TPA was 77% higher in FHx patients (p < 0.05). A final analysis using a generalized linear model with TPA progression as the response variable suggests that TPA progresses more rapidly in FHx patients compared to controls.

CONCLUSIONS

The FHx was associated with increased TPA burden and progression in the absence of other TRF. This supports ultrasound screening in FHx patients in order to detect high-risk patients who may benefit from early intervention.

Effect of carotid image-based phenotypes on cardiovascular risk calculator: AECRS1.0. Med Biol Eng Comput. 2019;57:1553-1566. [PMID: 30989577 DOI: 10.1007/s11517-019-01975-2]

Today, the 10-year cardiovascular risk largely relies on conventional cardiovascular risk factors (CCVRFs) and suffers from the effect of atherosclerotic wall changes. In this study, we present a novel risk calculator AtheroEdge Composite Risk Score (AECRS1.0), designed by fusing CCVRF with ultrasound image-based phenotypes. Ten-year risk was computed using the Framingham Risk Score (FRS), United Kingdom Prospective Diabetes Study 56 (UKPDS56), UKPDS60, Reynolds Risk Score (RRS), and pooled composite risk (PCR) score. AECRS1.0 was computed by measuring the 10-year five carotid phenotypes such as IMT (ave., max., min.), IMT variability, and total plaque area (TPA) by fusing eight CCVRFs and then compositing them. AECRS1.0 was then benchmarked against the five conventional cardiovascular risk calculators by computing the receiver operating characteristics (ROC) and area under curve (AUC) values with a 95% CI. Two hundred four IRB-approved Japanese patients' left/right common carotid arteries (407 ultrasound scans) were collected with a mean age of 69 ± 11 years. The calculators gave the following AUC: FRS, 0.615; UKPDS56, 0.576; UKPDS60, 0.580; RRS, 0.590; PCRS, 0.613; and AECRS1.0, 0.990. When fusing CCVRF, TPA reported the highest AUC of 0.81. The patients were risk-stratified into low, moderate, and high risk using the standardized thresholds. The AECRS1.0 demonstrated the best performance on a Japanese diabetes cohort when compared with five conventional calculators. Graphical abstract AECRS1.0: Carotid ultrasound image phenotype-based 10-year cardiovascular risk calculator. The figure provides brief overview of the proposed carotid image phenotype-based 10-year cardiovascular risk calculator called AECRS1.0. AECRS1.0 was also benchmarked against five conventional cardiovascular risk calculators (Framingham Risk Score (FRS), United Kingdom Prospective Diabetes Study 56 (UKPDS56), UKPDS60, Reynolds Risk Score (RRS), and pooled composite risk (PCR) score).

Ranking of stroke and cardiovascular risk factors for an optimal risk calculator design: Logistic regression approach

PURPOSE

Conventional cardiovascular risk factors (CCVRFs) and carotid ultrasound image-based phenotypes (CUSIP) are independently associated with long-term risk of cardiovascular (CV) disease. In this study, 26 cardiovascular risk (CVR) factors which consisted of a combination of CCVRFs and CUSIP together were ranked. Further, an optimal risk calculator using AtheroEdge composite risk score (AECRS1.0) was designed and benchmarked against seven conventional CV risk (CVR) calculators.

METHODS

Two types of ranking were performed: (i) ranking of 26 CVR factors and (ii) ranking of eight types of 10-year risk calculators. In the first case, multivariate logistic regression was used to compute the odds ratio (OR) and in the second, receiver operating characteristic curves were used to evaluate the performance of eight types of CVR calculators using SPSS23.0 and MEDCALC12.0 with validation against STATA15.0.

RESULTS

The left and right common carotid arteries (CCA) of 202 Japanese patients were examined to obtain 404 ultrasound scans. CUSIP ranked in the top 50% of the 26 covariates. Intima-media thickness variability (IMTV) and IMTV_10yr were the most influential carotid phenotypes for left CCA (OR = 250, P < 0.0001 and OR = 207, P < 0.0001 respectively) and right CCA (OR = 1614, P < 0.0001 and OR = 626, P < 0.0001 respectively). However, for the mean CCA, AECRS1.0 and AECRS1.0_10yr reported the most highly significant OR among all the CVR factors (OR = 1.073, P < 0.0001 and OR = 1.104, P < 0.0001). AECRS1.0_10yr also reported highest area-under-the-curve (AUC = 0.904, P < 0.0001) compared to seven types of conventional calculators. Age and glycated haemoglobin reported highest OR (1.96, P < 0.0001 and 1.05, P = 0.012) among all other CCVRFs.

CONCLUSION

AECRS1.0_10yr demonstrated the best performance due to presence of CUSIP and ranked at the first place with highest AUC.

Nonlinear model for the carotid artery disease 10-year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: A Japanese diabetes cohort study

MOTIVATION

This study presents a novel nonlinear model which can predict 10-year carotid ultrasound image-based phenotypes by fusing nine traditional cardiovascular risk factors (ethnicity, gender, age, artery type, body mass index, hemoglobin A1c, hypertension, low-density lipoprotein, and smoking) with five types of carotid automated image phenotypes (three types of carotid intima-media thickness (IMT), wall variability, and total plaque area).

METHODOLOGY

Two-step process was adapted: First, five baseline carotid image-based phenotypes were automatically measured using AtheroEdge^™ (AtheroPoint^™ , CA, USA) system by two operators (novice and experienced) and an expert. Second, based on the annual progression rates of cIMT due to nine traditional cardiovascular risk factors, a novel nonlinear model was adapted for 10-year predictions of carotid phenotypes.

RESULTS

Institute review board (IRB) approved 204 Japanese patients' left/right common carotid artery (407 ultrasound scans) was collected with a mean age of 69 ± 11 years. Age and hemoglobin were reported to have a high influence on the 10-year carotid phenotypes. Mean correlation coefficient (CC) between 10-year carotid image-based phenotype and age was improved by 39.35% in males and 25.38% in females. The area under the curves for the 10-year measurements of five phenotypes IMT_ave10yr , IMT_max10yr , IMT_min10yr , IMTV_10yr , and TPA_10yr were 0.96, 0.94, 0.90, 1.0, and 1.0. Inter-operator variability between two operators showed significant CC (P < 0.0001).

CONCLUSIONS

A nonlinear model was developed and validated by fusing nine conventional CV risk factors with current carotid image-based phenotypes for predicting the 10-year carotid ultrasound image-based phenotypes which may be used risk assessment.

Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: A diabetic study

MOTIVATION

AtheroEdge Composite Risk Score (AECRS1.0_10yr) is an integrated stroke/cardiovascular risk calculator that was recently developed and computes the 10-year risk of carotid image phenotypes by integrating conventional cardiovascular risk factors (CCVRFs). It is therefore important to understand how closely AECRS1.0_10yr is associated with the ten other currently available conventional cardiovascular risk calculators (CCVRCs).

METHODS

The Institutional Review Board of Toho University approved the examination of the left/right common carotid arteries of 202 Japanese patients. Step 1 consists of measurement of AECRS1.0_10yr, given current image phenotypes and CCVRFs. Step 2 consists of computing the risk score using ten different CCVRCs given CCVR factors: QRISK3, Framingham Risk Score (FRS), United Kingdom Prospective Diabetes Study (UKPDS) 56, UKPDS60, Reynolds Risk Score (RRS), Pooled cohort Risk Score (PCRS or ASCVD), Systematic Coronary Risk Evaluation (SCORE), Prospective Cardiovascular Munster Study (PROCAM) calculator, NIPPON, and World Health Organization (WHO) risk. Step 3 consists of computing the closeness factor between AECRS1.0_10yr and ten CCVRCs using cumulative ranking index derived using eight different statistically derived metrics.

RESULTS

AECRS1.0_10yr reported the highest area-under-the-curve (0.927;P < 0.001) among all the risk calculators. The top three CCVRCs closest to AECRS1.0_10yr were QRISK3, FRS, and UKPDS60 with cumulative ranking scores of 2.1, 3.0, and 3.8, respectively.

CONCLUSION

AECRS1.0_10yr produced the largest AUC due to the integration of image-based phenotypes with CCVR factors, and ranked at first place with the highest AUC. Cumulative ranking of ten CCVRCs demonstrated that QRISK3 was the closest calculator to AECRS1.0_10yr, which is also consistent with the industry trend.

Geometric Total Plaque Area Is an Equally Powerful Phenotype Compared With Carotid Intima-Media Thickness for Stroke Risk Assessment: A Deep Learning Approach

Currently, carotid intima-media thickness (cIMT) and geometric total plaque area (gTPA) are computed manually and thus are tedious and prone to interobserver and intraobserver variabilities. This study presents an intelligence-based automated deep learning (DL)–based technique for carotid wall interface detection, cIMT, and lumen diameter (LD) measurements, followed by a 3D cylindrical approach for TPA measurement. The observers were used for manual tracings of which were then used for the design of two DL-based systems. The DL boundaries for inner lumen wall and outer interadventitial borders were used for computing the cIMT and LD. Using cylindrical approach, we computed the gTPA. Furthermore, we compute the 10-year image-based cIMT and gTPA, using the progression rates. A total of 396 B-mode ultrasound right and left common carotid artery images were taken from 203 patients. The mean cIMT and gTPA using DL1 and DL2 is 0.91 mm, 20.52 mm2 and 0.88 mm, 19.44 mm2, respectively. The coefficient of correlation between gTPA and cIMT using DL1 and DL2 is 0.92 ( P < .001) and 0.94 ( P < .001), respectively. The area under the curve (AUC) for gTPA showed an improvement over cIMT by 14.36% and 12.57% for DL1 and DL2, respectively. The corresponding 10-year risk improvements were 9.09% and 6.26%. Our statistical significance tests successfully passed t test, Mann-Whitney, Wilcoxon, Kolmogorov-Smirnov, and Friedman. The study shows gTPA as an equally powerful carotid risk biomarker like cIMT. Given the cIMT and LD, cylindrical fitting is a fast method for gTPA measurements.

Echolucency-based phenotype in carotid atherosclerosis disease for risk stratification of diabetes patients

AIM

The study investigated the association of carotid ultrasound echolucent plaque-based biomarker with HbA1c, measured as age-adjusted grayscale median (AAGSM) as a function of chronological age, total plaque area, and conventional grayscale median (GSM_conv).

METHODS

Two stages were developed: (a) automated measurement of carotid parameters such as total plaque area (TPA); (b) computing the AAGSM as a function of GSM_conv, age, and TPA. Intra-operator (novice and experienced) analysis was conducted.

RESULTS

IRB approved, 204 patients' left/right CCA (408 images) ultrasound scans were collected: mean age: 69 ± 11 years; mean HbA1c: 6.12 ± 1.47%. A moderate inverse correlation was observed between AAGSM and HbA1c (CC of -0.13, P = 0.01), compared to GSM (CC of -0.06, P = 0.24). The RCCA and LCCA showed CC of -0.18, P < 0.01 and -0.08; P < 0.24. Female and males showed CC of -0.29, P < 0.01 and -0.10, P = 0.09. Using the threshold for AAGSM and HbA1c as: low-risk (AAGSM > 100; HbA1c < 5.7%), moderate-risk (40 < AAGSM < 100; 5.7% < HbA1c < 6.5%) and high-risk (AAGSM < 40; HbA1c > 6.5%), the area under the curve showed a better performance of AAGSM over GSM_conv. A paired t-test between operators and expert (P < 0.0001); inter-operator CC of 0.85 (P < 0.0001).

CONCLUSIONS

Echolucent plaque in patients with diabetes can be more accurately characterized for risk stratification using AAGSM compared to GSM_conv.

Morphologic TPA (mTPA) and composite risk score for moderate carotid atherosclerotic plaque is strongly associated with HbA1c in diabetes cohort

BACKGROUND

This study examines the association between six types of carotid artery disease image-based phenotypes and HbA1c in diabetes patients. Six phenotypes (intima-media thickness measurements (cIMT (ave.), cIMT (max.), cIMT (min.)), bidirectional wall variability (cIMTV), morphology-based total plaque area (mTPA), and composite risk score (CRS)) were measured in an automated setting using AtheroEdge™ (AtheroPoint, CA, USA).

METHOD

Consecutive 199 patients (157 M, age: 68.96 ± 10.98 years), L/R common carotid artery (CCA; 398 US scans) who underwent a carotid ultrasound (L/R) were retrospectively analyzed using AtheroEdge™ system. Two operators (novice and experienced) manually calibrated all the US scans using AtheroEdge™. Logistic regression (LR) and Odds ratio (OR) was computed and phenotypes were ranked.

RESULTS

The baseline results showed 150 low-risk patients (HbA1c < 6.50 mg/dl) and 49 high-risk patients (HbA1c ≥ 6.50 mg/dl). The fasting blood sugar (FBS) was highly associated with HbA1c (P < 0.001). Except for cIMTV, all phenotypes showed an OR > 1.0 (P < 0.001) for left common carotid artery (LCCA), right carotid artery (RCCA), and mean of left and right common carotid artery (MCCA). After adjusting the FBS, the OR for mTPA showed a higher risk for LCCA, RCCA, and MCCA. The coefficient of correlation (CC) between phenotypes and HbA1c were strong and inter-CC between cIMT and mTPA/CRS was above 0.9 (P < 0.001). The statistical tests showed that phenotypes were significantly associated with diabetes (P-value<0.0001).

CONCLUSIONS

All phenotypes using AtheroEdge™, except cIMTV, showed a strong association with HbA1c. mTPA and CRS were equally strong phenotypes as cIMT. The CRS phenotype showed the strongest relationship to HbA1c.

How to identify which patients with asymptomatic carotid stenosis could benefit from endarterectomy or stenting

Offering routine carotid endarterectomy (CEA) or carotid artery stenting (CAS) to patients with asymptomatic carotid artery stenosis (ACS) is no longer considered as the optimal management of these patients. Equally suboptimal, however, is the policy of offering only best medical treatment (BMT) to all patients with ACS and not considering any of them for prophylactic CEA. In the last few years, there have been many studies aiming to identify reliable predictors of future cerebrovascular events that would allow the identification of patients with high-risk ACS and offer a prophylactic carotid intervention only to these patients to prevent them from becoming symptomatic. All patients with ACS should receive BMT. The present article will summarise the evidence suggesting ways to identify these high-risk asymptomatic individuals, namely: (1) microemboli detection on transcranial Doppler, (2) plaque echolucency on Duplex ultrasound, (3) progression in the severity of ACS, (4) silent embolic infarcts on brain CT/MRI, (5) reduced cerebrovascular reserve, (6) increased size of juxtaluminal hypoechoic area, (7) identification of intraplaque haemorrhage using MRI and (8) carotid ulceration. The evidence suggests that approximately 10%-15% of patents with asymptomatic stenosis might benefit from intervention; this will become more clear after publication of ongoing studies comparing stenting or endarterectomy with best medical therapy. In the meantime, no patient should be offered intervention unless there is evidence of high risk of ipsilateral stroke, from modalities such as those discussed here.

Recent advances in preventing stroke recurrence

Recent advances in secondary stroke prevention include new evidence in hypertension, nutrition, anticoagulation, antiplatelet therapy, intracranial stenosis, percutaneous closure of patent foramen ovale, and lipid-lowering therapy. Individualized therapy for hypertension based on phenotyping with plasma renin and aldosterone markedly improves blood pressure control in patients with resistant hypertension. A Mediterranean diet can reduce the risk of stroke by nearly half. The diagnosis and treatment of metabolic vitamin B12 deficiency, and B vitamins to lower homocysteine, can reduce the risk of stroke by approximately 30%. There are problems with clopidogrel that can be overcome by using ticagrelor, and new anticoagulant drugs markedly improve anticoagulation for stroke prevention, particularly in atrial fibrillation. There are pharmacokinetic problems with dabigatran that deserve attention. Intensive medical therapy is better than stenting for intracranial stenosis, and new therapies directed at proprotein convertase subtilisin–kexin type 9 (PCSK9) will revolutionize lipid-lowering therapy. In the past, it was estimated that rational therapy could reduce recurrent stroke by about 80%. With recent advances, we should be able to do even better.