Clinical risk factors and features on computed tomography angiography in high-risk carotid artery plaque in patients with type 2 diabetes

BACKGROUND

High-risk carotid artery plaque (HPR) is associated with a markedly increased risk of ischemic stroke. The aims of this study were: 1) to examine the prevalence of HRP in a cohort of asymptomatic adults with type 2 diabetes (T2D); 2) to investigate the relationship between HRP, established cardiovascular risk factors and computed tomography angiography (CTA) profile; and 3) to assess whether the presence of HRP is associated with an increased risk of major adverse cardiovascular events (MACE).

METHODS

This was a retrospective cohort study of T2D asymptomatic patients who underwent carotid endarterectomy (CEA) from January 2018 to July 2021. The carotid atherosclerotic plaque (CAP) was assessed for the presence of ulceration, the presence of lipids, fibrosis, thrombotic deposits, hemorrhage, neovascularization, and inflammation. A CAP presenting at least five of these histological features was defined as a HRP (Group A); in all other cases it was defined as a mild to moderate heterogeneous plaque and no-HRP (Group B). CTA features included the presence of rim sign consisting of thin peripheral adventitial calcification (<2 mm) and internal soft plaque (≥2 mm), NASCET percent diameter stenosis, maximum plaque thickness, ulceration, calcification, and intraluminal thrombus were recorded. Binary logistic regression with Uni- and Multivariate was used to evaluate possible predictors for HRP while multivariable Cox Proportional Hazards was used to assess independent predictors for MACE.

RESULTS

One hundred eighty-five asymptomatic patients (mean age 73±8 years, 131 men), undergoing carotid endarterectomy, were included. Of these, 124 (67%) had HRP, and the 61 (33%) did not. Diabetic complications (OR 2.4, 95% CI: 1.1-5.1, P=0.01), NASCET stenosis ≥75% (OR 2.4, 95% CI: 1.2-3.7, P=0.02) and carotid RIM sign (OR 4.3, 95% CI: 3.9-7.3, P<0.001) were independently associated with HRP. However, HRP was not associated with a higher risk of MACE (freedom from MACE at 5 years: HRP 83.4% vs. non HRP 87.8%, P=0.72) or a reduction of survival (5-year survival estimates: HRP 96.4% vs. non HRP: 94.6%, P=0.76).

CONCLUSIONS

A high prevalence of HRP (67%) was observed in asymptomatic and elderly T2D patients. Independent predictors of HRP were diabetic complications, NASCET stenosis ≥75% and carotid RIM sign (OR 4.3, 95% CI: 3.9-7.3, P<0.001). HRP was not associated with an increased risk of MACE during a mean follow-up of 39±24 years.

Discovery and validation of circulating stroke metabolites by NMR-based analyses using patients from the MISS and UK Biobank

BACKGROUND

Stroke is a significant health issue in the United States, and identifying biomarkers for the prevention and functional recovery after an acute stroke remains the highest priority. This study aims to identify circulating metabolite signatures that may be associated with stroke pathophysiology by performing discovery and validation studies.

METHODS

We performed targeted metabolomics profiling of 420 participants of the discovery dataset of Metabolome in an Ischemic Stroke Study (MISS) using high-throughput nuclear magnetic resonance (NMR) spectroscopy. A validation study of significantly altered metabolites was conducted using an independent cohort of 117,988 participants from the UK Biobank, whose metabolomics profiles were generated using the same NMR technology.

RESULTS AND CONCLUSION

Our study identified 16 metabolites to be significantly perturbed during acute stroke. Amino acid phenylalanine was significantly increased, while glutamine and histidine were significantly lowered in stroke. Serum levels of apolipoprotein A-1, HDL particles, small HDL particles, essential fatty acids, and phosphatidylcholine were reduced, while ketone bodies like 3-hydroxybutyrate and acetoacetate were markedly increased in stroke. Based on the robust validation in a large independent UK Biobank dataset, some of these analytes may become clinically meaningful biomarkers to predict or prevent stroke in humans.

Prediction of Outcomes Through Cystatin C and cTnI in Elderly Type 2 Myocardial Infarction Patients

Background

Chronic kidney disease (CKD) and coronary artery disease (CAD) are strongly associated. Cystatin C (Cys C) is a more sensitive marker of early renal insufficiency. This study aimed to evaluate the prognostic implications of combined of Cys C and cardiac troponin I (cTnI) on 90-day outcomes in elderly patients with type 2 myocardial infarction (MI).

Methods

The data of consecutive type 2 MI patients aged 80 years and older who received Cys C and cTnI measurements within 24 h of admission were retrospectively reviewed. The endpoint was a 90-day all-cause and cardiac mortality.

Results

A total of 4326 patients were included. During the 90-day follow-up period, a higher all-cause and cardiac mortality was observed in patients with Cys C ≥ 1.49mg/L than in patients with Cys C < 1.49 mg/L (P <0.001). After the multivariate logistic regression adjustments, the higher CysC and cTnI levels remained independent predictors of the 90-day all-cause mortality and cardiac mortality. Moreover, the Kaplan-Meier all-cause and cardiac mortality event-free survival curves showed that the patients with the presence of elevated levels of both Cys C and cTnI had a significantly increased risk than those with Cys C or cTnI alone.

Conclusion

Elevated Cys C level is an independent risk factor for all-cause and cardiac mortality in the elderly type 2 MI population. The predictive ability of the combined use of Cys C and cTnI in elderly type 2 MI patients is stronger than that of Cys C or cTnI alone.

Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization

OBJECTIVE

Cardiovascular disease (CVD) is a major healthcare challenge and therefore early risk assessment is vital. Previous assessment techniques use either "conventional CVD risk calculators (CCVRC)" or machine learning (ML) paradigms. These techniques are ad-hoc, unreliable, not fully automated, and have variabilities. We, therefore, introduce AtheroEdge-MCDLAI (AE3.0DL) windows-based platform using multiclass Deep Learning (DL) system.

METHODS

Data was collected on 500 patients having both carotid ultrasound and corresponding coronary angiography scores (CAS), measured as stenosis in coronary arteries and considered as the gold standard. A total of 39 covariates were used, clubbed into three clusters, namely (i) Office-based: age, gender, body mass index, smoker, hypertension, systolic blood pressure, and diastolic blood pressure; (ii) Laboratory-based: Hyperlipidemia, hemoglobin A1c, and estimated glomerular filtration rate; and (iii) Carotid ultrasound image phenotypes: maximum plaque height, total plaque area, and intra-plaque neovascularization. Baseline characteristics for four classes (target labels) having significant (p < 0.0001) values were calculated using Chi-square and ANOVA. For handling the cohort's imbalance in the risk classes, AE3.0DL used the synthetic minority over-sampling technique (SMOTE). AE3.0DL used Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) DL models and the performance (accuracy and area-under-the-curve) was computed using 10-fold cross-validation (90% training, 10% testing) frameworks. AE3.0DL was validated and benchmarked.

RESULTS

The AE3.0DL using RNN and LSTM showed an accuracy and AUC (p < 0.0001) pairs as (95.00% and 0.98), and (95.34% and 0.99), respectively, and showed an improvement of 32.93% and 9.94% against CCVRC and ML, respectively. AE3.0DL runs in <1 s.

CONCLUSION

DL algorithms are a powerful paradigm for coronary artery disease (CAD) risk prediction and CVD risk stratification.

Development and validation of a nomogram based on the hospital information system for quantitative assessment of the risk of cardiocerebrovascular complications of diabetes

Background

Although the prevention and treatment of the cardiocerebrovascular complications (CCVCs) of diabetes have been clarified, their incidence is still high. This is largely due to the lack of predictive models to objectively assess the risk of CCVC in patients with type 2 diabetes mellitus (T2DM), reducing their treatment adherence. Despite the fact that the risk factors of CCVC in T2DM patients have been identified, no prediction model for identifying T2DM patients with the risk of CCVC is available. Therefore, the aim of this study is to establish a nomogram based on hospital information system data to quantitatively assess the risk of CCVCs in T2DM patients. This model is contributed to individualized therapeutic treatments and motivating T2DM patients to adhere to lifestyle interventions.

Methods

The medical records of 1,556 T2DM patients, comprising 1,145 cases in the training cohort and 411 in the validation cohort were retrospectively analyzed. CCVCs of diabetes, including coronary heart disease, cerebral ischemia, and intracerebral hemorrhage, were extracted from the medical records. Univariate and multivariate logistic regression analyses were performed to screen the independent correlates of CCVCs from the demographic information and laboratory test data, which were utilized to establish a nomogram for predicting the risk of CCVCs in these patients. We used internal and external validation based on the training and validation cohorts to evaluate the model performance.

Results

The incidence of CCVCs in the training cohort (26.99%) was similar to the validation cohort (25.79%). Disease duration, body mass index (BMI), systolic blood pressure (SBP), glycosylated hemoglobin (HbA1c), and uric acid (UA) levels were finally included in the established nomogram. In both the internal and external validation, the nomogram showed good discrimination [area under the curve (AUC) =0.850 and 0.825, respectively] and calibration (P=0.127 and P=0.096, respectively). Decision curve analysis showed that the nomogram produced a net benefit in both the training and validation cohorts.

Conclusions

The nomogram developed for predicting the risk of CCVC in T2DM patients may help improve treatment adherence. Further multi-center prospective investigations are required to predict the timing of CVCC in T2DM patients.

An Integrated Metabolomic Screening Platform Discovers the Potential Biomarkers of Ischemic Stroke and Reveals the Protective Effect and Mechanism of Folic Acid

Folic acid has a protective effect against ischemic stroke. However, the protective pharmacological mechanism remains unclear. The aim of this study is to explore the protective effect of folic acid on ischemic stroke animals by an integrated metabolomic biomarker screening platform. Based on ultra-performance liquid chromatography-tandem mass spectrometry (UPLC/MS) coupled with multivariate data analysis, the changes in metabolites and pathways were characterized. We found that the metabolic alteration involved a total of 37 metabolites, of which 26 biomarkers such as γ-aminobutyric acid, lysine, glutamate, ribose, and valine can be regulated by folic acid via metabolic pathways of amino acid metabolism, carbohydrate metabolism, fatty acid metabolism, citrate cycle, and pyruvate metabolism, which may be the potential therapeutic targets of folic acid against ischemic stroke. Folic acid as an emerging potential natural anti-fibrosis agent has significant activity in protecting against middle cerebral artery occlusion-induced rat ischemic stroke model by delaying pathological development, reversing the metabolic biomarkers, and mainly regulating the perturbation in amino acid metabolism, carbohydrate metabolism, fatty acid metabolism, citrate cycle, and pyruvate metabolism. It also showed that the integrated metabolic biomarker screening platform could provide a better understanding of the therapeutic effect and mechanism of drugs.

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.

Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson's Disease: A Narrative Review

Background and Motivation: Diagnosis of Parkinson's disease (PD) is often based on medical attention and clinical signs. It is subjective and does not have a good prognosis. Artificial Intelligence (AI) has played a promising role in the diagnosis of PD. However, it introduces bias due to lack of sample size, poor validation, clinical evaluation, and lack of big data configuration. The purpose of this study is to compute the risk of bias (RoB) automatically.

METHOD

The PRISMA search strategy was adopted to select the best 39 AI studies out of 85 PD studies closely associated with early diagnosis PD. The studies were used to compute 30 AI attributes (based on 6 AI clusters), using AP(ai)Bias 1.0 (AtheroPointTM, Roseville, CA, USA), and the mean aggregate score was computed. The studies were ranked and two cutoffs (Moderate-Low (ML) and High-Moderate (MH)) were determined to segregate the studies into three bins: low-, moderate-, and high-bias.

RESULT

The ML and HM cutoffs were 3.50 and 2.33, respectively, which constituted 7, 13, and 6 for low-, moderate-, and high-bias studies. The best and worst architectures were "deep learning with sketches as outcomes" and "machine learning with Electroencephalography," respectively. We recommend (i) the usage of power analysis in big data framework, (ii) that it must undergo scientific validation using unseen AI models, and (iii) that it should be taken towards clinical evaluation for reliability and stability tests.

CONCLUSION

The AI is a vital component for the diagnosis of early PD and the recommendations must be followed to lower the RoB.

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.

Emerging role of artificial intelligence in stroke imaging

Introduction: The recognition and therapy of patients with stroke is becoming progressively intricate as additional treatment choices become accessible and new associations between disease characteristics and treatment response are incessantly uncovered. Therefore, clinicians must regularly learn new skill, stay up to date with the literature and integrate advances into daily practice. The application of artificial intelligence (AI) to assist clinical decision making could diminish inter-rater variation in routine clinical practice and accelerate the mining of vital data that could expand recognition of patients with stroke, forecast of treatment responses and patient outcomes.Areas covered: In this review, the authors provide an up-to-date review of AI in stroke, analyzing the latest papers on this subject. These have been divided in two main groups: stroke diagnosis and outcome prediction.Expert opinion: The highest value of AI is its capability to merge, select and condense a large amount of clinical and imaging features of a single patient and to associate these with fitted models that have gone through robust assessment and optimization with large cohorts of data to support clinical decision making.

A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework

Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide. Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial infarction and stroke. The two primary image-based phenotypes used for monitoring the atherosclerosis burden is carotid intima-media thickness (cIMT) and plaque area (PA). Earlier segmentation and measurement methods were based on ad hoc conventional and semi-automated digital imaging solutions, which are unreliable, tedious, slow, and not robust. This study reviews the modern and automated methods such as artificial intelligence (AI)-based. Machine learning (ML) and deep learning (DL) can provide automated techniques in the detection and measurement of cIMT and PA from carotid vascular images. Both ML and DL techniques are examples of supervised learning, i.e., learn from "ground truth" images and transformation of test images that are not part of the training. This review summarizes (1) the evolution and impact of the fast-changing AI technology on cIMT/PA measurement, (2) the mathematical representations of ML/DL methods, and (3) segmentation approaches for cIMT/PA regions in carotid scans based for (a) region-of-interest detection and (b) lumen-intima and media-adventitia interface detection using ML/DL frameworks. AI-based methods for cIMT/PA segmentation have emerged for CVD/stroke risk monitoring and may expand to the recommended parameters for atherosclerosis assessment by carotid ultrasound.

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.