A Clinical Decision Support Platform for the Risk Stratification, Diagnosis, and Prediction of Coronary Artery Disease Evolution

Sakellarios, Antonis I.; Siogkas, Panagiotis K.; Georga, Eleni I.; Tachos, Nikolaos S.; Kigka, Vassiliki I.; Tsompou, Panagiota; Andrikos, Ioannis; Karanasiou, Georgia S.; Rocchiccioli, Silvia; Correia, J.; Pelosi, G; Stofella, Paolo; Filipović, Nenad; Parodi, Oberdan; Fotiadis, Dimitrios I.

doi:10.1109/embc.2018.8513131

Cited by 10 publications

(9 citation statements)

References 10 publications

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“…The SMARTool (Simulation Modeling of coronary ARTery disease: a tool for clinical decision support, Horizon 2020) project, is a prospective, international, multicenter study with the aim of integrating clinical, molecular, cellular and imaging data to provide a patient-specific risk stratification model exploitable for clinical decision support in stable CAD management [ 24 , 25 ]. Patients who had undergone a coronary CTA at baseline for suspected CAD were prospectively included and subsequently underwent a follow-up coronary CTA.…”

Section: Methodsmentioning

confidence: 99%

Sex differences in coronary plaque changes assessed by serial computed tomography angiography

Mahdiui

Smit

Rosendael

et al. 2021

Int J Cardiovasc Imaging

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Long-term data on sex-differences in coronary plaque changes over time is lacking in a low-to-intermediate risk population of stable coronary artery disease (CAD). The aim of this study was to evaluate the role of sex on long-term plaque progression and evolution of plaque composition. Furthermore, the influence of menopause on plaque progression and composition was also evaluated. Patients that underwent a coronary computed tomography angiography (CTA) were prospectively included to undergo a follow-up coronary CTA. Total and compositional plaque volumes were normalized using the vessel volume to calculate a percentage atheroma volume (PAV). To investigate the influence of menopause on plaque progression, patients were divided into two groups, under and over 55 years of age. In total, 211 patients were included in this analysis, 146 (69%) men. The mean interscan period between baseline and follow-up coronary CTA was 6.2 ± 1.4 years. Women were older, had higher HDL levels and presented more often with atypical chest pain. Men had 434 plaque sites and women 156. On a per-lesion analysis, women had less fibro-fatty PAV compared to men (β -1.3 ± 0.4%; p < 0.001), with no other significant differences. When stratifying patients by 55 years age threshold, fibro-fatty PAV remained higher in men in both age groups (p < 0.05) whilst women younger than 55 years demonstrated more regression of fibrous (β -0.8 ± 0.3% per year; p = 0.002) and non-calcified PAV (β -0.7 ± 0.3% per year; p = 0.027). In a low-to-intermediate risk population of stable CAD patients, no significant sex differences in total PAV increase over time were observed. Fibro-fatty PAV was lower in women at any age and women under 55 years demonstrated significantly greater reduction in fibrous and non-calcified PAV over time compared to age-matched men. (ClinicalTrials.gov number, NCT04448691.)

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Section: Methodsmentioning

confidence: 99%

Sex differences in coronary plaque changes assessed by serial computed tomography angiography

Mahdiui

Smit

Rosendael

et al. 2021

Int J Cardiovasc Imaging

Self Cite

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“…The Horizon 2020 funded SMARTool (Simulation Modeling of coronary Artery disease: a tool for clinical decision support) Project is a prospective, multicenter study in patients who underwent serial coronary CTA. 16 White patients were included by 7 centers from 5 European countries. The study protocol was approved by all local ethical committees, all patients gave their written informed consent to participate in the study and the procedures followed were in accordance with institutional guidelines.…”

Section: Methodsmentioning

confidence: 99%

Impact of Clinical Characteristics and Statins on Coronary Plaque Progression by Serial Computed Tomography Angiography

Smit

Rosendael

Mahdiui

et al. 2020

Circ: Cardiovascular Imaging

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Background Progression of coronary artery disease using serial coronary computed tomography angiography (CTA) is of clinical interest. Our primary aim was to prospectively assess the impact of clinical characteristics and statin use on quantitatively assessed coronary plaque progression in a low-risk study population during long-term follow-up. Methods Patients who previously underwent coronary CTA for suspected coronary artery disease were prospectively included to undergo follow-up coronary CTA. The primary end point was coronary artery disease progression, defined as the absolute annual increase in total, calcified, and noncalcified plaque volume by quantitative CTA analysis. Results In total, 202 patients underwent serial coronary CTA with a mean interscan period of 6.2±1.4 years. On a per-plaque basis, increasing age (β=0.070; P =0.058) and hypertension (β=1.380; P =0.075) were nonsignificantly associated with annual total plaque progression. Male sex (β=1.676; P =0.009), diabetes mellitus (β=1.725; P =0.012), and statin use (β=1.498; P =0.046) showed an independent association with annual progression of calcified plaque. While hypertension (β=2.259; P =0.015) was an independent determinant of noncalcified plaque progression, statin use (β=−2.178; P =0.050) was borderline significantly associated with a reduced progression of noncalcified plaque. Conclusions Statin use was associated with an increased progression of calcified coronary plaque and a reduced progression of noncalcified coronary plaque, potentially reflecting calcification of the noncalcified plaque component. Whereas hypertension was the only modifiable risk factor predictive of noncalcified plaque progression, diabetes mellitus mainly led to an increase in calcified plaque. These findings could yield the need for intensified preventive treatment of patients with diabetes mellitus and hypertension to slow and stabilize coronary artery disease progression and improve clinical outcome.

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“…The mobile-based CVD systems follow both ML and non-ML approaches [197][198][199][200][201][202][203][204][205][206][207]. The classifier techniques used for the mobile-based ML systems were k-NN [208], SVM [201,209], CNN [201,202], NB [204], DT [207], and RF [207]. The number of outcomes for the mobile-based CVD systems [197] varies between 1 and 2, basically CVD and diabetes.…”

Section: Cvd Risk Assessment Through Mobile E-health and Cloud Techni...mentioning

confidence: 99%

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

Suri¹,

Bhagawati

Sauseng

et al. 2022

Diagnostics

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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.

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A Clinical Decision Support Platform for the Risk Stratification, Diagnosis, and Prediction of Coronary Artery Disease Evolution

Cited by 10 publications

References 10 publications

Sex differences in coronary plaque changes assessed by serial computed tomography angiography

Sex differences in coronary plaque changes assessed by serial computed tomography angiography

Impact of Clinical Characteristics and Statins on Coronary Plaque Progression by Serial Computed Tomography Angiography

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

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