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

A Siamese ResNeXt network for predicting carotid intimal thickness of patients with T2DM from fundus images

Objective

To develop and validate an artificial intelligence diagnostic model based on fundus images for predicting Carotid Intima-Media Thickness (CIMT) in individuals with Type 2 Diabetes Mellitus (T2DM).

Methods

In total, 1236 patients with T2DM who had both retinal fundus images and CIMT ultrasound records within a single hospital stay were enrolled. Data were divided into normal and thickened groups and sent to eight deep learning models: convolutional neural networks of the eight models were all based on ResNet or ResNeXt. Their encoder and decoder modes are different, including the standard mode, the Parallel learning mode, and the Siamese mode. Except for the six unimodal networks, two multimodal networks based on ResNeXt under the Parallel learning mode or the Siamese mode were embedded with ages. Performance of eight models were compared via the confusion matrix, precision, recall, specificity, F1 value, and ROC curve, and recall was regarded as the main indicator. Besides, Grad-CAM was used to visualize the decisions made by Siamese ResNeXt network, which is the best performance.

Results

Performance of various models demonstrated the following points: 1) the RexNeXt showed a notable improvement over the ResNet; 2) the structural Siamese networks, which extracted features parallelly and independently, exhibited slight performance enhancements compared to the traditional networks. Notably, the Siamese networks resulted in significant improvements; 3) the performance of classification declined if the age factor was embedded in the network. Taken together, the Siamese ResNeXt unimodal model performed best for its superior efficacy and robustness. This model achieved a recall rate of 88.0% and an AUC value of 90.88% in the validation subset. Additionally, heatmaps calculated by the Grad-CAM algorithm presented concentrated and orderly mappings around the optic disc vascular area in normal CIMT groups and dispersed, irregular patterns in thickened CIMT groups.

Conclusion

We provided a Siamese ResNeXt neural network for predicting the carotid intimal thickness of patients with T2DM from fundus images and confirmed the correlation between fundus microvascular lesions and CIMT.

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

BACKGROUND AND MOTIVATION

Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias.

METHODS

Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework.

FINDINGS AND CONCLUSIONS

UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.

Deep learning based on carotid transverse B-mode scan videos for the diagnosis of carotid plaque: a prospective multicenter study

OBJECTIVES

Accurate detection of carotid plaque using ultrasound (US) is essential for preventing stroke. However, the diagnostic performance of junior radiologists (with approximately 1 year of experience in carotid US evaluation) is relatively poor. We thus aim to develop a deep learning (DL) model based on US videos to improve junior radiologists' performance in plaque detection.

METHODS

This multicenter prospective study was conducted at five hospitals. CaroNet-Dynamic automatically detected carotid plaque from carotid transverse US videos allowing clinical detection. Model performance was evaluated using expert annotations (with more than 10 years of experience in carotid US evaluation) as the ground truth. Model robustness was investigated on different plaque characteristics and US scanning systems. Furthermore, its clinical applicability was evaluated by comparing the junior radiologists' diagnoses with and without DL-model assistance.

RESULTS

A total of 1647 videos from 825 patients were evaluated. The DL model yielded high performance with sensitivities of 87.03% and 94.17%, specificities of 82.07% and 74.04%, and areas under the receiver operating characteristic curve of 0.845 and 0.841 on the internal and multicenter external test sets, respectively. Moreover, no significant difference in performance was noted among different plaque characteristics and scanning systems. Using the DL model, the performance of the junior radiologists improved significantly, especially in terms of sensitivity (largest increase from 46.3 to 94.44%).

CONCLUSIONS

The DL model based on US videos corresponding to real examinations showed robust performance for plaque detection and significantly improved the diagnostic performance of junior radiologists.

KEY POINTS

• The deep learning model based on US videos conforming to real examinations showed robust performance for plaque detection. • Computer-aided diagnosis can significantly improve the diagnostic performance of junior radiologists in clinical practice.

Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm

Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment.

Associations between electrocardiogram and carotid ultrasound parameters: a healthy chinese group study

Background: Electrocardiogram (ECG) and carotid ultrasound (CUS) are important tools for the diagnosis and prediction of cardiovascular disease (CVD). This study aimed to investigate the associations between ECG and CUS parameters and explore the feasibility of assessing carotid health with ECG.

Methods: This cross-sectional cohort study enrolled 319 healthy Chinese subjects. Standard 12-lead ECG parameters (including the ST-segment amplitude [STA]), CUS parameters (intima-media thickness [IMT] and blood flow resistance index [RI]), and CVD risk factors (including sex, age, and systolic blood pressure [SBP]) were collected for analysis. Participants were divided into the high-level RI group (average RI ≥ 0.76, n = 171) and the normal RI group (average RI < 0.76, n = 148). Linear and stepwise multivariable regression models were performed to explore the associations between ECG and CUS parameters.

Results: Statistically significant differences in sex, age, SBP, STA and other ECG parameters were observed in the normal and the high-level RI group. The STA in lead V₃ yielded stronger significant correlations (r = 0.27–0.42, p < 0.001) with RI than STA in other leads, while ECG parameters yielded weak correlations with IMT (|r| ≤ 0.20, p < 0.05). STA in lead V₂ or V₃, sex, age, and SBP had independent contributions (p < 0.01) to predicting RI in the stepwise multivariable models, although the models for IMT had only CVD risk factors (age, body mass index, and triglyceride) as independent variables. The prediction model for RI in the left proximal common carotid artery (CCA) had higher adjusted R² (adjusted R² = 0.31) than the model for RI in the left middle CCA (adjusted R² = 0.29) and the model for RI in the right proximal CCA (adjusted R² = 0.20).

Conclusion: In a cohort of healthy Chinese individuals, the STA was associated with the RI of CCA, which indicated that ECG could be utilized to assess carotid health. The utilization of ECG might contribute to a rapid screening of carotid health with convenient operations.

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson's Disease Affected by COVID-19: A Narrative Review

Background and Motivation: Parkinson's disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/Non-COVID-19 Frameworks Using Artificial Intelligence Paradigm: A Narrative Review

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for low-income countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, low-cost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework.

Evaluation of Intima-Media Thickness and Arterial Stiffness as Early Ultrasound Biomarkers of Carotid Artery Atherosclerosis

Carotid atherosclerosis is a major and potentially preventable cause of ischemic stroke. It begins early in life and progresses silently over the years. Identification of individuals with subclinical atherosclerosis is needed to initiate early aggressive vascular prevention. Although carotid plaque appears to be a powerful predictor of cardiovascular risk, carotid intima-media thickness (CIMT) and arterial stiffness can be detected at the initial phases and, therefore, they are considered important new biomarkers of carotid atherosclerosis. There is a well-documented association between CIMT and cerebrovascular events. CIMT provides a reliable marker in young people, in whom plaque formation or calcification is not established. However, the usefulness of CIMT measurement in the improvement of risk cardiovascular models is still controversial. Carotid stiffness is also significantly associated with ischemic stroke. Carotid stiffness adds value to the existing risk prediction based on Framingham risk factors, particularly individuals at intermediate cardiovascular risk. Carotid ultrasound is used to assess carotid atherosclerosis. During the last decade, automated techniques for sophisticated analysis of vascular mechanics have evolved, such as speckle tracking, and new methods based on deep learning have been proposed with promising outcomes. Additional research is needed to investigate the imaging-based cardiovascular risk prediction of CIMT and stiffness.

Endocan in Acute Leukemia: Current Knowledge and Future Perspectives

Endocan is a soluble dermatan sulfate proteoglycan expressed by endothelial cells and detected in serum/plasma. Its expression is increased in tumors/tumor vessels in several human malignancies, and high expression (high serum/plasma levels or tumor levels) has an adverse prognostic impact in several malignancies. The p14 endocan degradation product can also be detected in serum/plasma, but previous clinical studies as well as previously unpublished results presented in this review suggest that endocan and p14 endocan fragment levels reflect different biological characteristics, and the endocan levels seem to reflect the disease heterogeneity in acute leukemia better than the p14 fragment levels. Furthermore, decreased systemic endocan levels in previously immunocompetent sepsis patients are associated with later severe respiratory complications, but it is not known whether this is true also for immunocompromised acute leukemia patients. Finally, endocan is associated with increased early nonrelapse mortality in (acute leukemia) patients receiving allogeneic stem cell transplantation, and this adverse prognostic impact seems to be independent of the adverse impact of excessive fluid overload. Systemic endocan levels may also become important to predict cytokine release syndrome after immunotherapy/haploidentical transplantation, and in the long-term follow-up of acute leukemia survivors with regard to cardiovascular risk. Therapeutic targeting of endocan is now possible, and the possible role of endocan in acute leukemia should be further investigated to clarify whether the therapeutic strategy should also be considered.

Evaluation of Cardiac Function of Pregnant Women with High Blood Pressure during Gestation Period and Coupling of Hearts with Peripheral Vessels by Ultrasonic Cardiogram under Artificial Intelligence Algorithm

The research was aimed at analyzing the value of the optimized eXtreme Gradient Boosting (XGBoost) algorithm-based ultrasound cardiogram images in the diagnosis of pregnant hypertension patients. A total of 145 pregnant women (85 cases suffered from hypertension disease during pregnancy and 60 other normal women were healthy) were selected as the reference to the comparison and analysis of ultrasound cardiac function parameter, common carotid artery parameter, and the coupling relationship between hearts and cervical vessels of pregnant hypertension patients. The results demonstrated ultrasound cardiac function parameter of pregnant hypertension patients as follows. The maximum volume of the left atrium (LAVmax) was 35.65 mm, left ventricular end-systolic volume (LVESV) was 31.07 mm, and left ventricular end-diastolic volume (LVEDV) was 88.73 mm. All the above indexes were obviously higher than those of the normal control group (P < 0.05). Besides, intima-media thickness (IMT) of common carotid artery (465.84 μm), pulse wave velocity (PWV) (8.09 m/s), pressure of turning point 1 from isovolumic contraction phase to ejection phase (PT1) (126.5 mmHg), arterial enhancement pressure (AP) (6.14 mmHg), and arterial pressure enhancement index (8.58%) were all significantly higher than those of the normal control group (P < 0.05). In addition, the correlation between the coupling (E/A) of hearts and carotid artery of pregnant hypertension patients and PWV was not obvious (r = -0.08432, P > 0.05). The results of the research indicated that intima-media inside carotid artery of pregnant hypertension patients thickened obviously, and it became less elastic compared with that of normal healthy pregnant women. What is more, cardiac morphological changes were manifested mainly as the enlargement of the left atrial chamber and the thickening of the interventricular septum. Volume load and blood flow velocity both increased, and left ventricular diastolic function was damaged. XGBoost algorithm-based ultrasound cardiogram images could improve the diagnostic effects of hypertension during pregnancy effectively.

Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients

The study proposes a novel machine learning (ML) paradigm for cardiovascular disease (CVD) detection in individuals at medium to high cardiovascular risk using data from a Greek cohort of 542 individuals with rheumatoid arthritis, or diabetes mellitus, and/or arterial hypertension, using conventional or office-based, laboratory-based blood biomarkers and carotid/femoral ultrasound image-based phenotypes. Two kinds of data (CVD risk factors and presence of CVD-defined as stroke, or myocardial infarction, or coronary artery syndrome, or peripheral artery disease, or coronary heart disease) as ground truth, were collected at two-time points: (i) at visit 1 and (ii) at visit 2 after 3 years. The CVD risk factors were divided into three clusters (conventional or office-based, laboratory-based blood biomarkers, carotid ultrasound image-based phenotypes) to study their effect on the ML classifiers. Three kinds of ML classifiers (Random Forest, Support Vector Machine, and Linear Discriminant Analysis) were applied in a two-fold cross-validation framework using the data augmented by synthetic minority over-sampling technique (SMOTE) strategy. The performance of the ML classifiers was recorded. In this cohort with overall 46 CVD risk factors (covariates) implemented in an online cardiovascular framework, that requires calculation time less than 1 s per patient, a mean accuracy and area-under-the-curve (AUC) of 98.40% and 0.98 (p < 0.0001) for CVD presence detection at visit 1, and 98.39% and 0.98 (p < 0.0001) at visit 2, respectively. The performance of the cardiovascular framework was significantly better than the classical CVD risk score. The ML paradigm proved to be powerful for CVD prediction in individuals at medium to high cardiovascular risk.

LPA Genotypes and Haplotypes Are Associated with Lipoprotein(a) Levels but Not Arterial Wall Properties in Stable Post-Coronary Event Patients with Very High Lipoprotein(a) Levels

Lipoprotein(a) [Lp(a)] levels are an independent risk factor for coronary artery disease (CAD). Two single-nucleotide polymorphisms (rs10455872, rs3798220) and number of KIV-2 repeats in the gene encoding Lp(a) (LPA) are associated with Lp(a) and CAD. Our aim was to investigate whether in patients with stable CAD and high Lp(a) levels these genetic variants are associated with increased Lp(a) and arterial wall properties. Blood samples underwent biochemical and genetic analyses. Ultrasound measurements for the functional and morphological properties of arterial wall were performed. Genotypes of rs10455872 and haplotypes AT and GT showed significant association with Lp(a) levels. Patients with GG showed significantly higher Lp(a) levels compared with those with AG genotype (2180 vs. 1391 mg/L, p = 0.045). Patients with no AT haplotype had significantly higher Lp(a) compared to carriers of one AT haplotype (2158 vs. 1478 mg/L, p = 0.023) or two AT haplotypes (2158 vs. 1487 mg/L, p = 0.044). There were no significant associations with the properties of the arterial wall. Lp(a) levels significantly correlated also with number of KIV-2 repeats (r = -0.601; p < 0.0001). In our patients, these two LPA polymorphisms and number of KIV-2 repeats are associated with Lp(a), but not arterial wall properties.

Unseen Artificial Intelligence-Deep Learning Paradigm for Segmentation of Low Atherosclerotic Plaque in Carotid Ultrasound: A Multicenter Cardiovascular Study

BACKGROUND

The early detection of carotid wall plaque is recommended in the prevention of cardiovascular disease (CVD) in moderate-risk patients. Previous techniques for B-mode carotid atherosclerotic wall plaque segmentation used artificial intelligence (AI) methods on monoethnic databases, where training and testing are from the "same" ethnic group ("Seen AI"). Therefore, the versatility of the system is questionable. This is the first study of its kind that uses the "Unseen AI" paradigm where training and testing are from "different" ethnic groups. We hypothesized that deep learning (DL) models should perform in 10% proximity between "Unseen AI" and "Seen AI".

METHODOLOGY

Two cohorts from multi-ethnic groups (330 Japanese and 300 Hong Kong (HK)) were used for the validation of our hypothesis. We used a four-layered UNet architecture for the segmentation of the atherosclerotic wall with low plaque. "Unseen AI" (training: Japanese, testing: HK or vice versa) and "Seen AI" experiments (single ethnicity or mixed ethnicity) were performed. Evaluation was conducted by measuring the wall plaque area. Statistical tests were conducted for its stability and reliability.

RESULTS

When using the UNet DL architecture, the "Unseen AI" pair one (Training: 330 Japanese and Testing: 300 HK), the mean accuracy, dice-similarity, and correlation-coefficient were 98.55, 78.38, and 0.80 (p < 0.0001), respectively, while for "Unseen AI" pair two (Training: 300 HK and Testing: 330 Japanese), these were 98.67, 82.49, and 0.87 (p < 0.0001), respectively. Using "Seen AI", the same parameters were 99.01, 86.89 and 0.92 (p < 0.0001), respectively.

CONCLUSION

We demonstrated that "Unseen AI" was in close proximity (<10%) to "Seen AI", validating our DL model for low atherosclerotic wall plaque segmentation. The online system runs < 1 s.

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment.

Comparison of carotid artery ultrasound and Framingham risk score for discriminating coronary artery disease in patients with psoriatic arthritis

Objectives

This study aimed to assess the performance of carotid ultrasound (US) parameters alone or in combination with Framingham Risk Score (FRS) in discriminating patients with psoriatic arthritis (PsA) with and without coronary artery disease (CAD).

Methods

Ninety-one patients with PsA (56 males; age: 50±11 years, disease duration: 9.4±9.2 years) without overt cardiovascular (CV) diseases were recruited. Carotid intima-media thickness (cIMT), the presence of plaque and total plaque area (TPA) was determined by high-resolution US. CAD was defined as the presence of any coronary plaque on coronary CT angiography (CCTA). Obstructive-CAD (O-CAD) was defined as >50% stenosis of the lumen.

Results

Thirty-five (38%) patients had carotid plaque. Fifty-four (59%) patients had CAD (CAD+) and 9 (10%) patients had O-CAD (O-CAD+). No significant associations between the presence of carotid plaque and CAD were found. However, cIMT and TPA were higher in both the CAD+ and O-CAD+ group compared with the CAD− or O-CAD− groups, respectively. Multivariate logistic regression analysis revealed that mean cIMT was an independent explanatory variable associated with CAD and O-CAD, while maximum cIMT and TPA were independent explanatory variables associated with O-CAD after adjusting for covariates. The optimal cut-offs for detecting the presence of CAD were FRS >5% and mean cIMT at 0.62 mm (AUC: 0.71; sensitivity: 67%; specificity: 76%), while the optimal cut-offs for detecting the presence of O-CAD were FRS >10% in combination with mean cIMT at 0.73 mm (AUC: 0.71; sensitivity: 56%; specificity: 85%).

Conclusion

US parameters including cIMT and TPA may be considered in addition to FRS for CV risk stratification in patients with PsA.

Preclinical carotid atherosclerosis as an indicator of polyvascular disease: a narrative review

Carotid atherosclerotic lesions are correlated with atherosclerotic deterioration of the arterial wall in other vascular territories and with cardiovascular events. The detection of pre-symptomatic carotid lesions like intima-media thickness (IMT) and asymptomatic carotid plaques is possible by non-invasive ultrasound duplex scanning. Current measurement guidelines suggest an average measurement of IMT within 10 mm of the segment of the common carotid artery. The thickening of intima-media appears in a long subclinical period of atherosclerosis. Therefore, the determination of IMT has emerged as one of the methods for determining early structural deterioration of the arterial wall. A close interrelationship was shown between IMT and risk factors of atherosclerosis, their duration, and intensity. Different studies demonstrated that increased IMT is a powerful predictor of coronary, cerebrovascular, and peripheral arterial occlusive disease and their complication. A recent meta-analysis indicated a minimal improvement in the risk estimation of cardiovascular events after adding IMT to the Framingham Risk Score. These findings influenced the latest ACC/AHA guidelines which again recommend the use of carotid IMT measurement for individual risk assessment. The presence of atherosclerotic plaques indicates that the atherosclerotic process is already ongoing. The findings of different studies are equivocal that carotid plaques independently predict cardiovascular events and improve risk predictions for coronary artery disease when added to the Framingham Risk Score. However, besides the size of plaque and grade of stenosis, the structure of plaque calcification, vascularization, lipid core, and the surface of plaques are important indicators of related risks for cardiovascular events.

H2 FPEF score predicts atherosclerosis presence in patients with systemic connective tissue disease

BACKGROUND

Cardiovascular diseases are common cause of morbidity and mortality in patients with systemic connective tissue diseases (SCTD) due to accelerated atherosclerosis which couldn't be explained by traditional risk factors (CVDRF).

HYPOTHESIS

We hypothesized that recently developed score predicting probability of heart failure with preserved ejection fraction (H₂ FPEF), as well as a measure of right ventricular-pulmonary vasculature coupling [tricuspid annular plane systolic excursion (TAPSE)/pulmonary artery systolic pressure (PASP) ratio], are predictive of atherosclerosis in SCTD.

METHODS

203 patients (178 females) diagnosed with SCTD underwent standard and stress-echocardiography (SE) with TAPSE/PASP and left ventricular (LV) diastolic filling pressure (E/e') measurements, carotid ultrasound and computed tomographic coronary angiography. Patients who were SE positive for ischemia underwent coronary angiography (34/203). The H₂ FPEF score was calculated according to age, body mass index, presence of atrial fibrillation, ≥2 antihypertensives, E/e' and PASP.

RESULTS

Mean LV ejection fraction was 66.3 ± 7.1%. Atherosclerosis was present in 150/203 patients according to: 1) intima-media thickness>0.9 mm; and 2) Agatstone score > 300 or Syntax score ≥ 1. On binary logistic regression analysis, including CVDRF prevalence, echocardiographic parameters and H₂ FPEF score, only H₂ FPEF score remained significant for the prediction of atherosclerosis presence (χ² = 19.3, HR 2.6, CI 1.5-4.3, p < 0.001), and resting TAPSE/PASP for the prediction of a SE positive for ischemia (χ² = 10.4, HR 0.01, CI = 0.01-0.22, p = 0.004). On ROC analysis, the optimal threshold value for identifying patients with atherosclerosis was a H₂ FPEF score ≥2 (Sn 60.4%, Sp 69.4%, area 0.67, SE = 0.05, p < 0.001).

CONCLUSIONS

H₂ FPEF score and resting TAPSE/PASP demonstrated clinical value for an atherosclerosis diagnosis in patients diagnosed with SCTD.

Ten things to know about ten imaging studies: A preventive cardiology perspective ("ASPC top ten imaging")

Knowing the patient's current cardiovascular disease (CVD) status, as well as the patient's current and future CVD risk, helps the clinician make more informed patient-centered management recommendations towards the goal of preventing future CVD events. Imaging tests that can assist the clinician with the diagnosis and prognosis of CVD include imaging studies of the heart and vascular system, as well as imaging studies of other body organs applicable to CVD risk. The American Society for Preventive Cardiology (ASPC) has published "Ten Things to Know About Ten Cardiovascular Disease Risk Factors." Similarly, this "ASPC Top Ten Imaging" summarizes ten things to know about ten imaging studies related to assessing CVD and CVD risk, listed in tabular form. The ten imaging studies herein include: (1) coronary artery calcium imaging (CAC), (2) coronary computed tomography angiography (CCTA), (3) cardiac ultrasound (echocardiography), (4) nuclear myocardial perfusion imaging (MPI), (5) cardiac magnetic resonance (CMR), (6) cardiac catheterization [with or without intravascular ultrasound (IVUS) or coronary optical coherence tomography (OCT)], (7) dual x-ray absorptiometry (DXA) body composition, (8) hepatic imaging [ultrasound of liver, vibration-controlled transient elastography (VCTE), CT, MRI proton density fat fraction (PDFF), magnetic resonance spectroscopy (MRS)], (9) peripheral artery / endothelial function imaging (e.g., carotid ultrasound, peripheral doppler imaging, ultrasound flow-mediated dilation, other tests of endothelial function and peripheral vascular imaging) and (10) images of other body organs applicable to preventive cardiology (brain, kidney, ovary). Many cardiologists perform cardiovascular-related imaging. Many non-cardiologists perform applicable non-cardiovascular imaging. Cardiologists and non-cardiologists alike may benefit from a working knowledge of imaging studies applicable to the diagnosis and prognosis of CVD and CVD risk - both important in preventive cardiology.

Role of artificial intelligence in cardiovascular risk prediction and outcomes: comparison of machine-learning and conventional statistical approaches for the analysis of carotid ultrasound features and intra-plaque neovascularization

The aim of this study was to compare machine learning (ML) methods with conventional statistical methods to investigate the predictive ability of carotid plaque characteristics for assessing the risk of coronary artery disease (CAD) and cardiovascular (CV) events. Focused carotid B-mode ultrasound, contrast-enhanced ultrasound, and coronary angiography were performed on 459 participants. These participants were followed for 30 days. Plaque characteristics such as carotid intima-media thickness (cIMT), maximum plaque height (MPH), total plaque area (TPA), and intraplaque neovascularization (IPN) were measured at baseline. Two ML-based algorithms-random forest (RF) and random survival forest (RSF) were used for CAD and CV event prediction. The performance of these algorithms was compared against (i) univariate and multivariate analysis for CAD prediction using the area-under-the-curve (AUC) and (ii) Cox proportional hazard model for CV event prediction using the concordance index (c-index). There was a significant association between CAD and carotid plaque characteristics [cIMT (odds ratio (OR) = 1.49, p = 0.03), MPH (OR = 2.44, p < 0.0001), TPA (OR = 1.61, p < 0.0001), and IPN (OR = 2.78, p < 0.0001)]. IPN alone reported significant CV event prediction (hazard ratio = 1.24, p < 0.0001). CAD prediction using the RF algorithm reported an improvement in AUC by ~ 3% over the univariate analysis with IPN alone (0.97 vs. 0.94, p < 0.0001). Cardiovascular event prediction using RSF demonstrated an improvement in the c-index by ~ 17.8% over the Cox-based model (0.86 vs. 0.73). Carotid imaging phenotypes and IPN were associated with CAD and CV events. The ML-based system is superior to the conventional statistically-derived approaches for CAD prediction and survival analysis.

Application of cardiovascular risk scales to identify carotid atherosclerosis in patients with rheumatoid arthritis

Aim. To evaluate the cardiovascular risk (CVR) and analyze its relationship with detection of early carotid artery atherosclerotic lesion in patients with rheumatoid arthritis (RA). Materials and methods. One hundred and nine RA patients aged 45 to 60 without established cardiovascular diseases (CVD) were included in the study. The median age was 52 [48; 54] years, duration of RA was 120 [36; 204] months, DAS28 was 4.7 [3.5; 5.6] points. CVD risk was calculated with mSCORE, Reynolds Risk Score (RRS), ASSIGN, QRISK3, ERS-RA scales and Carotid Artery Doppler Ultrasound Exam was performed for all patients. Results. High risk was found in 5, 5, 14, 6, and 38% of patients according to mSCORE, RRS, ASSIGN, QRISK3, ERS-RA scales, respectively. Atherosclerotic plaques of carotid arteries were found in 30% of patients. It was found that carotid intima-media thickness is correlated to all CVR calculators, age, systolic and diastolic blood pressure, cholesterol, erythrocyte sedimentation rate, interleukin-6 levels. The sensitivity and specificity of the CVR algorithms in prognostication of atherosclerotic carotid artery lesions were 73 and 67% for mSCORE, 64 and 63% for RRS, 64 and 56% for ASSIGN, 73 and 49% for QRISK3, respectively, p0.05 in all cases, 67 and 50% for ERS-RA, p=0.06. Conclusion. RRS, mSCORE, ASSIGN, QRISK3 calculators equally predict atherosclerotic carotid artery damage in RA patients. The optimal ratio of specificity and sensitivity is shown for the mSCORE scale. Stratification of CVR in RA patients should include assessment of the carotid intima-media thickness. To identify CVR in RA patients, the most informative methods are mSCORE calculation and carotid intima-media thickness determination.

Bidirectional link between diabetes mellitus and coronavirus disease 2019 leading to cardiovascular disease: A narrative review

Coronavirus disease 2019 (COVID-19) is a global pandemic where several comorbidities have been shown to have a significant effect on mortality. Patients with diabetes mellitus (DM) have a higher mortality rate than non-DM patients if they get COVID-19. Recent studies have indicated that patients with a history of diabetes can increase the risk of severe acute respiratory syndrome coronavirus 2 infection. Additionally, patients without any history of diabetes can acquire new-onset DM when infected with COVID-19. Thus, there is a need to explore the bidirectional link between these two conditions, confirming the vicious loop between "DM/COVID-19". This narrative review presents (1) the bidirectional association between the DM and COVID-19, (2) the manifestations of the DM/COVID-19 loop leading to cardiovascular disease, (3) an understanding of primary and secondary factors that influence mortality due to the DM/COVID-19 loop, (4) the role of vitamin-D in DM patients during COVID-19, and finally, (5) the monitoring tools for tracking atherosclerosis burden in DM patients during COVID-19 and "COVID-triggered DM" patients. We conclude that the bidirectional nature of DM/COVID-19 causes acceleration towards cardiovascular events. Due to this alarming condition, early monitoring of atherosclerotic burden is required in "Diabetes patients during COVID-19" or "new-onset Diabetes triggered by COVID-19 in Non-Diabetes patients".

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging

Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients.

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.

Ultrasound-based stroke/cardiovascular risk stratification using Framingham Risk Score and ASCVD Risk Score based on "Integrated Vascular Age" instead of "Chronological Age": a multi-ethnic study of Asian Indian, Caucasian, and Japanese cohorts

Background

Vascular age (VA) has recently emerged for CVD risk assessment and can either be computed using conventional risk factors (CRF) or by using carotid intima-media thickness (cIMT) derived from carotid ultrasound (CUS). This study investigates a novel method of integrating both CRF and cIMT for estimating VA [so-called integrated VA (IVA)]. Further, the study analyzes and compares CVD/stroke risk using the Framingham Risk Score (FRS)-based risk calculator when adapting IVA against VA.

Methods

The system follows a four-step process: (I) VA using cIMT based using linear-regression (LR) model and its coefficients; (II) VA prediction using ten CRF using a multivariate linear regression (MLR)-based model with gender adjustment; (III) coefficients from the LR-based model and MLR-based model are combined using a linear model to predict the final IVA; (IV) the final step consists of FRS-based risk stratification with IVA as inputs and benchmarked against FRS using conventional method of CA. Area-under-the-curve (AUC) is computed using IVA and benchmarked against CA while taking the response variable as a standardized combination of cIMT and glycated hemoglobin.

Results

The study recruited 648 patients, 202 were Japanese, 314 were Asian Indian, and 132 were Caucasians. Both left and right common carotid arteries (CCA) of all the population were scanned, thus a total of 1,287 ultrasound scans. The 10-year FRS using IVA reported higher AUC (AUC =0.78) compared with 10-year FRS using CA (AUC =0.66) by ~18%.

Conclusions

IVA is an efficient biomarker for risk stratifications for patients in routine practice.

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.