Quantitative Vascular Evaluation: From Laboratory Experiments to Point-of-Care Patient (Experimental Approach)

Armentano, Ricardo L.; Cymberknop, Leandro J.

doi:10.2174/1573402114666180423110658

Cited by 9 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Previously, CVD risk was only assessed by the presence of CVD risk factors such as old age, hypertension, obesity, high cholesterol and low HDL [ 45 ]. There is some experimental evidence against the use of only CVD risk factors in identifying individuals at high risk from the normal ones [ 46 , 47 ]. It was reported that traditional CVD risk factors alone explain only 60–65% of the CVD risk, and the presence of one or more risk factors is common even among individuals who may not develop a clinical disease [ 48 ].…”

Section: Discussionmentioning

confidence: 99%

The optimal WC cut-off points for the prediction of subclinical CVD as measured by carotid intima-media thickness among African adults: a cross-sectional study

Ringane

Choma

2021

BMC Cardiovasc Disord

View full text Add to dashboard Cite

Background Increased waist circumference (WC) is one of the cardiovascular disease (CVD) risk factors used to predict cardiovascular events. Waist circumference cut-off values for predicting metabolic syndrome and other cardiovascular risks have been previously studied. Carotid intima-media thickness (CIMT) is one of the cardiovascular risk factor recently described and reported to be suitable as it is a direct measurement of vascular quality. Hence the aim of the present study was to determine the optimal WC cut-off point for the prediction of subclinical CVD. Methods The study was a cross-sectional study using quantitative methods, conducted among 1318 adults aged between 40 and 60 years old, residing in a rural Black population in Limpopo province. Carotid Intima-Media Thickness measurements were performed using a LOGIQ ultrasound system (GE Healthcare, CT, USA). Waist Circumference (WC) (cm) was measured to the nearest 0.1 cm. Bivariate correlation, logistic regression and receiver operating characteristic were analysed using the statistical package for social sciences version 26.0 software. Results Among the total population, 69% were women and 31% men with a mean age of 53 ± 7 years. Among women, WC at a cut-off value of 95 cm gave the highest sensitivity of 57%, the specificity of 55% and an area under the curve (AUC) of 0.588. In men, an optimum WC cut-off point of 82 cm yielded the highest sensitivity and specificity at 72% and 70% respectively, with an AUC of 0.767 p < 0.001. Conclusion The traditional waist circumference cut-off points (94 cm for women and 80 cm for men) that are currently used for the diagnosis of metabolic syndrome might not be suitable in the prediction of an increased CIMT.

show abstract

Section: Discussionmentioning

confidence: 99%

The optimal WC cut-off points for the prediction of subclinical CVD as measured by carotid intima-media thickness among African adults: a cross-sectional study

Ringane

Choma

2021

BMC Cardiovasc Disord

View full text Add to dashboard Cite

show abstract

“…In order to validate the proposed methodology and open new lines of research, in vivo P-D measurements such as those detailed in [ 29 , 30 ] should be performed. It is noteworthy that the virtual subjects considered in this study were simulated assuming a healthy condition.…”

Section: Discussionmentioning

confidence: 99%

Central Arterial Dynamic Evaluation from Peripheral Blood Pressure Waveforms Using CycleGAN: An In Silico Approach

Aguirre

Cymberknop

Grall‐Maës

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Arterial stiffness is a major condition related to many cardiovascular diseases. Traditional approaches in the assessment of arterial stiffness supported by machine learning techniques are limited to the pulse wave velocity (PWV) estimation based on pressure signals from the peripheral arteries. Nevertheless, arterial stiffness can be assessed based on the pressure–strain relationship by analyzing its hysteresis loop. In this work, the capacity of deep learning models based on generative adversarial networks (GANs) to transfer pressure signals from the peripheral arterial region to pressure and area signals located in the central arterial region is explored. The studied signals are from a public and validated virtual database. Compared to other works in which the assessment of arterial stiffness was performed via PWV, in the present work the pressure–strain hysteresis loop is reconstructed and evaluated in terms of classical machine learning metrics and clinical parameters. Least-square GAN (LSGAN) and Wasserstein GAN with gradient penalty (WGAN-GP) adversarial losses are compared, yielding better results with LSGAN. LSGAN mean ± standard deviation of error for pressure and area pulse waveforms are 0.8 ± 0.4 mmHg and 0.1 ± 0.1 cm2, respectively. Regarding the pressure–strain elastic modulus, it is achieved a mean absolute percentage error of 6.5 ± 5.1%. GAN-based deep learning models can recover the pressure–strain loop of central arteries while observing pressure signals from peripheral arteries.

show abstract

“…La PA puede analizarse como el resultado de la combinación de una onda de presión anterógrada (que proviene del corazón) y una onda retrógrada (que vuelve de sitios específicos caracterizados por coeficientes de reflexión específicos). Ambas ondas se propagan (en direcciones opuestas) a una velocidad de fase dependiente de la rigidez arterial, definida como -Velocidad de la Onda de Pulso Arterial‖ (VOP) [3]. Debe tenerse en consideración que las alteraciones en las grandes arterias constituyen un importante factor de morbilidad y mortalidad cardiovascular.…”

Section: Introductionunclassified

Plataforma IoMT Basada en Inteligencia Computacional Para la Evaluación del Riesgo Cardiovascular

Armentano

View full text Add to dashboard Cite

NTRODUCCIÓN: El cuidado de la salud está apuntado hacia un enfoque caracterizado por tecnologías preventivas y predictivas. Los sistemas basados en “Internet de las Cosas Médicas” (IoMT), generan un formidable caudal de información, que debe ser abordado de manera integradora por metodologías derivadas de la Inteligencia Computacional (IC). El envejecimiento y la presencia de factores de riesgo cardiovascular (RCV) están asociados con una disminución en las propiedades elásticas arteriales (aumento de la Velocidad de la Onda del Pulso, VOP) y un engrosamiento de la pared arterial (aumento del “espesor íntima-media”, EIM). OBJETIVO: Consolidar una plataforma de caracterización de grupos de RCV basada en mediciones IoMT de parámetros biomecánicos arteriales (VOP y EIM), sustentada en herramientas inteligentes de ML. MÉTODOS: Se tomó una muestra de 377 pacientes constituida por variables para determinación del RCV a 10 años, junto con parámetros como VOP, IMT y SCC. Se aplicó el análisis de conglomerados sobre variables antropométricas y hemodinámicas, de modo de determinar la existencia de grupos diferenciados ante la inclusión posterior de parámetros biomecánicos (VOP e EIM). RESULTADOS: El método de conglomerados arrojó dos grupos diferenciados, resultando una mayor distinción del RCV al incorporar medidas biomecánicas como VOP y EIM. CONCLUSIÓN: Este tipo de abordaje permite concebir un sistema de soporte predictivo a la decisión clínica de tipo personalizado, basado en IoMT, IC y variables biomecánicas, aplicable a la clasificación subclínica de la enfermedad CV.

show abstract

Quantitative Vascular Evaluation: From Laboratory Experiments to Point-of-Care Patient (Experimental Approach)

Cited by 9 publications

References 47 publications

The optimal WC cut-off points for the prediction of subclinical CVD as measured by carotid intima-media thickness among African adults: a cross-sectional study

The optimal WC cut-off points for the prediction of subclinical CVD as measured by carotid intima-media thickness among African adults: a cross-sectional study

Central Arterial Dynamic Evaluation from Peripheral Blood Pressure Waveforms Using CycleGAN: An In Silico Approach

Plataforma IoMT Basada en Inteligencia Computacional Para la Evaluación del Riesgo Cardiovascular

Contact Info

Product

Resources

About