A clinical and proteomics approach to predict the presence of obstructive peripheral arterial disease: From the Catheter Sampled Blood Archive in Cardiovascular Diseases (CASABLANCA) Study

Unsupervised classification of multi-contrast magnetic resonance histology of peripheral arterial disease lesions using a convolutional variational autoencoder with a Gaussian mixture model in latent space: A technical feasibility study

PURPOSE

To investigate the feasibility of a deep learning algorithm combining variational autoencoder (VAE) and two-dimensional (2D) convolutional neural networks (CNN) for automatically quantifying hard tissue presence and morphology in multi-contrast magnetic resonance (MR) images of peripheral arterial disease (PAD) occlusive lesions.

METHODS

Multi-contrast MR images (T2-weighted and ultrashort echo time) were acquired from lesions harvested from six amputated legs with high isotropic spatial resolution (0.078 mm and 0.156 mm, respectively) at 9.4 T. A total of 4014 pseudo-color combined images were generated, with 75% used to train a VAE employing custom 2D CNN layers. A Gaussian mixture model (GMM) was employed to classify the latent space data into four tissue classes: I) concentric calcified (c), II) eccentric calcified (e), III) occluded with hard tissue (h) and IV) occluded with soft tissue (s). Test image probabilities, encoded by the trained VAE were used to evaluate model performance.

RESULTS

GMM component classification probabilities ranged from 0.92 to 0.97 for class (c), 1.00 for class (e), 0.82-0.95 for class (h) and 0.56-0.93 for the remaining class (s). Due to the complexity of soft-tissue lesions reflected in the heterogeneity of the pseudo-color images, more GMM components (n=17) were attributed to class (s), compared to the other three (c, e and h) (n=6).

CONCLUSION

Combination of 2D CNN VAE and GMM achieves high classification probabilities for hard tissue-containing lesions. Automatic recognition of these classes may aid therapeutic decision-making and identifying uncrossable lesions prior to endovascular intervention.

Applications of artificial intelligence for patients with peripheral artery disease

OBJECTIVE

Applications of artificial intelligence (AI) have been reported in several cardiovascular diseases but its interest in patients with peripheral artery disease (PAD) has been so far less reported. The aim of this review was to summarize current knowledge on applications of AI in patients with PAD, to discuss current limits, and highlight perspectives in the field.

METHODS

We performed a narrative review based on studies reporting applications of AI in patients with PAD. The MEDLINE database was independently searched by two authors using a combination of keywords to identify studies published between January 1995 and December 2021. Three main fields of AI were investigated including natural language processing (NLP), computer vision and machine learning (ML).

RESULTS

NLP and ML brought new tools to improve the screening, the diagnosis and classification of the severity of PAD. ML was also used to develop predictive models to better assess the prognosis of patients and develop real-time prediction models to support clinical decision-making. Studies related to computer vision mainly aimed at creating automatic detection and characterization of arterial lesions based on Doppler ultrasound examination or computed tomography angiography. Such tools could help to improve screening programs, enhance diagnosis, facilitate presurgical planning, and improve clinical workflow.

CONCLUSIONS

AI offers various applications to support and likely improve the management of patients with PAD. Further research efforts are needed to validate such applications and investigate their accuracy and safety in large multinational cohorts before their implementation in daily clinical practice.

Artificial intelligence in atherosclerotic disease: Applications and trends

Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death globally. Increasing amounts of highly diverse ASCVD data are becoming available and artificial intelligence (AI) techniques now bear the promise of utilizing them to improve diagnosis, advance understanding of disease pathogenesis, enable outcome prediction, assist with clinical decision making and promote precision medicine approaches. Machine learning (ML) algorithms in particular, are already employed in cardiovascular imaging applications to facilitate automated disease detection and experts believe that ML will transform the field in the coming years. Current review first describes the key concepts of AI applications from a clinical standpoint. We then provide a focused overview of current AI applications in four main ASCVD domains: coronary artery disease (CAD), peripheral arterial disease (PAD), abdominal aortic aneurysm (AAA), and carotid artery disease. For each domain, applications are presented with refer to the primary imaging modality used [e.g., computed tomography (CT) or invasive angiography] and the key aim of the applied AI approaches, which include disease detection, phenotyping, outcome prediction, and assistance with clinical decision making. We conclude with the strengths and limitations of AI applications and provide future perspectives.

The Ability of Clinical Decision Rules to Detect Peripheral Arterial Disease: A Narrative Review

Peripheral arterial disease (PAD) is a common cause of lower extremity wound. Consequently, PAD leads to a cause of leg amputation nowadays, especially in diabetic patients. In general practice (GP), confrontation with PAD prevention is a challenge. In general, ankle-brachial index (ABI) measurement can be used as a PAD diagnostic tool, but this takes some time. The tool is not generally available and this need to train healthcare workers to perform. Multiple independent predictors developed the diagnostic prediction model known as clinical decision rules (CDRs) to identify patients with high-risk PAD. This might therefore limit the number of patients (only high-risk patients) to refer for ABI evaluation. This narrative review summarized existing CDRs for PAD.

Leveraging Machine Learning and Artificial Intelligence to Improve Peripheral Artery Disease Detection, Treatment, and Outcomes

Peripheral artery disease is an atherosclerotic disorder which, when present, portends poor patient outcomes. Low diagnosis rates perpetuate poor management, leading to limb loss and excess rates of cardiovascular morbidity and death. Machine learning algorithms and artificially intelligent systems have shown great promise in application to many areas in health care, such as accurately detecting disease, predicting patient outcomes, and automating image interpretation. Although the application of these technologies to peripheral artery disease are in their infancy, their promises are tremendous. In this review, we provide an introduction to important concepts in the fields of machine learning and artificial intelligence, detail the current state of how these technologies have been applied to peripheral artery disease, and discuss potential areas for future care enhancement with advanced analytics.

Gaining Efficiency in Clinical Trials With Cardiac Biomarkers: JACC Review Topic of the Week

The momentum of cardiovascular drug development has slowed dramatically. Use of validated cardiac biomarkers in clinical trials could accelerate development of much-needed therapies, but biomarkers have been used less for cardiovascular drug development than in therapeutic areas such as oncology. Moreover, there are inconsistences in biomarker use in clinical trials, such as sample type, collection times, analytical methods, and storage for future research. With these needs in mind, participants in a Cardiac Safety Research Consortium Think Tank proposed the development of international guidance in this area, together with improved quality assurance and analytical methods, to determine what biomarkers can reliably show. Participants recommended the development of systematic methods for sample collection, and the archiving of samples in all cardiovascular clinical trials (including creation of a biobank or repository). The academic and regulatory communities also agreed to work together to ensure that published information is fully and clearly expressed.

The Role of Circulating Biomarkers in Peripheral Arterial Disease

Peripheral arterial disease (PAD) of the lower extremities is a chronic illness predominantly of atherosclerotic aetiology, associated to traditional cardiovascular (CV) risk factors. It is one of the most prevalent CV conditions worldwide in subjects >65 years, estimated to increase greatly with the aging of the population, becoming a severe socioeconomic problem in the future. The narrowing and thrombotic occlusion of the lower limb arteries impairs the walking function as the disease progresses, increasing the risk of CV events (myocardial infarction and stroke), amputation and death. Despite its poor prognosis, PAD patients are scarcely identified until the disease is advanced, highlighting the need for reliable biomarkers for PAD patient stratification, that might also contribute to define more personalized medical treatments. In this review, we will discuss the usefulness of inflammatory molecules, matrix metalloproteinases (MMPs), and cardiac damage markers, as well as novel components of the liquid biopsy, extracellular vesicles (EVs), and non-coding RNAs for lower limb PAD identification, stratification, and outcome assessment. We will also explore the potential of machine learning methods to build prediction models to refine PAD assessment. In this line, the usefulness of multimarker approaches to evaluate this complex multifactorial disease will be also discussed.

Study of three novel biomarkers, MR-proADM, midkine, and stromelysin2, and peripheral atherosclerosis in a Chinese Han population: A case-control study

Midregional pro-adrenomedullin (MR-proADM), midkine, and stromelysin2 (ST2) are novel cardiac biomarkers associated with heart failure and atherosclerotic diseases like stable ischemic disease and acute coronary syndrome. The potential association between these three biomarkers and peripheral artery disease (PAD) remains unclear. The aim of this study was to assess the correlation between these three biomarkers and their association with PAD in the Chinese Han population. This study included 224 patients suspected of having coronary artery disease (CAD). All subjects underwent coronary angiography and carotid and subclavian ultrasound assessment for detection of coronary and peripheral atherosclerosis and were divided into two groups according to whether they had PAD or not. Pearson’s correlation coefficient r was calculated, and multivariable logistic regression analysis was conducted to represent the associations of these biomarkers and PAD. The study included 133 patients with PAD and 91 non-PAD controls and these two groups had similar values for age, ST2, hematocrit, hemoglobin, red blood cell counts, creatinine and CAD ratio, smoking, and type 2 diabetes (all p > 0.05). Compared with non-PAD controls, patients with PAD had lower levels of MR-proADM and midkine and higher levels of TC, LDL-C, and fasting blood sugar (FBS) (all p < 0.05). MR-proADM was positively and ST2 negatively correlated with midkine (all p < 0.05). Compared with females, male patients had higher values of MR-proADM ( p < 0.05) and similar levels of ST2 and midkine (all p > 0.05). Multivariable regression analysis identified FBS as a risk predictor (OR: 1.163, 95% CI: 1.108–1.401, p = 0.014) and MR-proADM as a protective factor (OR: 0.720, 95% CI: 0.529–0.920, p = 0.037) of PAD. Three novel biomarkers, MR-proADM, midkine, and ST2, are internally related, and MR-proADM is gender-specific and a protective factor of peripheral atherosclerosis in the Chinese Han population studied. Clinical Trial: ChiCTR-DDD-17013908

Role of neuronal nitric oxide synthase on cardiovascular functions in physiological and pathophysiological states

This review describes and summarizes the role of neuronal nitric oxide synthase (nNOS) on the central nervous system, particularly on brain regions such as the ventrolateral medulla (VLM) and the periaqueductal gray matter (PAG), and on blood vessels and the heart that are involved in the regulation and control of the cardiovascular system (CVS). Furthermore, we shall also review the functional aspects of nNOS during several physiological, pathophysiological, and clinical conditions such as exercise, pain, cerebral vascular accidents or stroke and hypertension. For example, during stroke, a cascade of molecular, neurochemical, and cellular changes occur that affect the nervous system as elicited by generation of free radicals and nitric oxide (NO) from vulnerable neurons, peroxide formation, superoxides, apoptosis, and the differential activation of three isoforms of nitric oxide synthases (NOSs), and can exert profound effects on the CVS. Neuronal NOS is one of the three isoforms of NOSs, the others being endothelial (eNOS) and inducible (iNOS) enzymes. Neuronal NOS is a critical homeostatic component of the CVS and plays an important role in regulation of different systems and disease process including nociception. The functional and physiological roles of NO and nNOS are described at the beginning of this review. We also elaborate the structure, gene, domain, and regulation of the nNOS protein. Both inhibitory and excitatory role of nNOS on the sympathetic autonomic nervous system (SANS) and parasympathetic autonomic nervous system (PANS) as mediated via different neurotransmitters/signal transduction processes will be explored, particularly its effects on the CVS. Because the VLM plays a crucial function in cardiovascular homeostatic mechanisms, the neuroanatomy and cardiovascular regulation of the VLM will be discussed in conjunction with the actions of nNOS. Thereafter, we shall discuss the up-to-date developments that are related to the interaction between nNOS and cardiovascular diseases such as hypertension and stroke. Finally, we shall focus on the role of nNOS, particularly within the PAG in cardiovascular regulation and neurotransmission during different types of pain stimulus. Overall, this review focuses on our current understanding of the nNOS protein, and provides further insights on how nNOS modulates, regulates, and controls cardiovascular function during both physiological activity such as exercise, and pathophysiological conditions such as stroke and hypertension.

Performance of a clinical/proteomic panel to predict obstructive peripheral artery disease in patients with and without diabetes mellitus

Background

Patients with diabetes mellitus (DM) are at substantial risk of developing peripheral artery disease (PAD). We recently developed a clinical/proteomic panel to predict obstructive PAD. In this study, we compare the accuracy of this panel for the diagnosis of PAD in patients with and without DM.

Methods and results

The HART PAD panel consists of one clinical variable (history of hypertension) and concentrations of six biomarkers (midkine, kidney injury molecule-1, interleukin-23, follicle-stimulating hormone, angiopoietin-1 and eotaxin-1). In a prospective cohort of 354 patients undergoing peripheral and/or coronary angiography, performance of this diagnostic panel to detect ≥50% stenosis in at least one peripheral vessel was assessed in patients with (n=94) and without DM (n=260). The model had an area under the receiver operating characteristic curve (AUC) of 0.85 for obstructive PAD. At optimal cut-off, the model had 84% sensitivity, 75% specificity, positive predictive value (PPV) of 84% and negative predictive value (NPV) of 75% for detection of PAD among patients with DM, similar as in those without DM. In those with DM, partitioning the model into five levels resulted in a PPV of 95% and NPV of 100% in the highest and lowest levels, respectively. Abnormal scores were associated with a shorter time to revascularisation during 4.3 years of follow-up.

Conclusion

A clinical/biomarker model can predict with high accuracy the presence of PAD among patients with DM.

Trial registration number

NCT00842868.

A clinical and proteomics approach to predict the presence of obstructive peripheral arterial disease: From the Catheter Sampled Blood Archive in Cardiovascular Diseases (CASABLANCA) Study

BACKGROUND

Peripheral arterial disease (PAD) is a global health problem that is frequently underdiagnosed and undertreated. Noninvasive tools to predict the presence and severity of PAD have limitations including inaccuracy, cost, or need for intravenous contrast and ionizing radiation.

HYPOTHESIS

A clinical/biomarker score may offer an attractive alternative diagnostic method for PAD.

METHODS

In a prospective cohort of 354 patients referred for diagnostic peripheral and/or coronary angiography, predictors of ≥50% stenosis in ≥1 peripheral vessel (carotid/subclavian, renal, or lower extremity arteries) were identified from >50 clinical variables and 109 biomarkers. Machine learning identified variables predictive of obstructive PAD; a score derived from the final model was developed.

RESULTS

The score consisted of 1 clinical variable (history of hypertension) and 6 biomarkers (midkine, kidney injury molecule-1, interleukin-23, follicle-stimulating hormone, angiopoietin-1, and eotaxin-1). The model had an in-sample area under the receiver operating characteristic curve of 0.85 for obstructive PAD and a cross-validated area under the curve of 0.84; higher scores were associated with greater severity of angiographic stenosis. At optimal cutoff, the score had 65% sensitivity, 88% specificity, 76% positive predictive value (PPV), and 81% negative predictive value (NPV) for obstructive PAD and performed consistently across vascular territories. Partitioning the score into 5 levels resulted in a PPV of 86% and NPV of 98% in the highest and lowest levels, respectively. Elevated score was associated with shorter time to revascularization during 4.3 years of follow-up.

CONCLUSIONS

A clinical/biomarker score demonstrates high accuracy for predicting the presence of PAD.