Real-time electrical bioimpedance characterization of neointimal tissue for stent applications

To follow up the restenosis in arteries stented during an angioplasty is an important current clinical problem. A new approach to monitor the growth of neointimal tissue inside the stent is proposed on the basis of electrical impedance spectroscopy (EIS) sensors and the oscillation-based test (OBT) circuit technique. A mathematical model was developed to analytically describe the histological composition of the neointima, employing its conductivity and permittivity data. The bioimpedance model was validated against a finite element analysis (FEA) using COMSOL Multiphysics software. A satisfactory correlation between the analytical model and FEA simulation was achieved in most cases, detecting some deviations introduced by the thin “double layer” that separates the neointima and the blood. It is hereby shown how to apply conformal transformations to obtain bioimpedance electrical models for stack-layered tissues over coplanar electrodes. Particularly, this can be applied to characterize the neointima in real-time. This technique is either suitable as a main mechanism for restenosis follow-up or it can be combined with proposed intelligent stents for blood pressure measurements to auto-calibrate the sensibility loss caused by the adherence of the tissue on the micro-electro-mechanical sensors (MEMSs).This work was carried out in collaboration with the Cardiology Department of the UHMV Hospital, Santander (Spain) and was funded by the Spanish Government’s “Ministerio de Economía, Industria y Competitividad” under the joint projects TEC2013-46242-C3-1-P and TEC2013-46242-C3, co-financed with FEDER

Bioimpedance real-time charazterization of neointimal tissue inside stents

It is hereby presented a new approach to monitor restenosis in arteries fitted with a stent during an angioplasty. The growth of neointimal tissue is followed up by measuring its bioimpedance with Electrical Impedance Spectroscopy (EIS). Besides, a mathematical model is derived to analytically describe the neointima’s histological composition from its bioimpedance. The model is validated by finite-element analysis (FEA) with COMSOL Multiphysics®. Satisfactory correlation between the analytical model and the FEA simulation is achieved for most of the characterization range, detecting some deviations introduced by the thin "double layer" that separates the neointima and the blood. It is shown how to apply conformal transformations to obtain bioimpedance models for stack-layered tissues over coplanar electrodes. Particularly, this is applied to characterize the neointima in real-time. This technique is either suitable as a main mechanism of restenosis follow-up or it can be combined with proposed blood-pressure-measuring intelligent stents to auto-calibrate the sensibility loss caused by the adherence of the tissue on the micro-electro-mechanical sensors (MEMS).Ministerio de Economía, Industria y Competitividad (Spain): projects TEC2013-46242-C3-1-PMinisterio de Economía, Industria y Competitividad (Spain): projects TEC2013-46242-C3-2-

Characterization of Implanted Stents through Neointimal Tissue Bioimpedance Simulations

This work describes how is possible the definition of the light hole or lumen in implanted stents affected by restenosis processes using the BioImpedance (BI) as biomarker. The main approach is based on the fact that neointimal tissues implied in restenosis can be detected and measured thanks to their respective conductivity and dielectric properties. For this goal, it is proposed a four-electrode setup for bioimpedance measurement. The influence of the several involved tissues in restenosis: fat, muscle, fiber, endothelium and blood, have been studied at several frequencies, validating the setup and illustrating the sensitivity of each one. Finally, a real example using a standard stent, has been analyzed for stable and vulnerable plaques in restenosis test cases, demonstrating that the proposed method is useful for the stent obstruction test. Bioimpedance simulation test has been performed using the electric physics module in COMSOL Multiphysics®.Junta de Andalucía 2017/TIC-17

Bioimpedance sensor and methodology for acute pain monitoring

The paper aims to revive the interest in bioimpedance analysis for pain studies in communicating and non-communicating (anesthetized) individuals for monitoring purpose. The plea for exploitation of full potential offered by the complex (bio)impedance measurement is emphasized through theoretical and experimental analysis. A non-invasive, low-cost reliable sensor to measure skin impedance is designed with off-the-shelf components. This is a second generation prototype for pain detection, quantification, and modeling, with the objective to be used in fully anesthetized patients undergoing surgery. The 2D and 3D time-frequency, multi-frequency evaluation of impedance data is based on broadly available signal processing tools. Furthermore, fractional-order impedance models are implied to provide an indication of change in tissue dynamics correlated with absence/presence of nociceptor stimulation. The unique features of the proposed sensor enhancements are described and illustrated here based on mechanical and thermal tests and further reinforced with previous studies from our first generation prototype

VAD in failing Fontan: simulation of ventricular, cavo-pulmonary and biventricular assistance in systolic/diastolic ventricular dysfunction and in pulmonary vascular resistance increase.

Aim: Due to the lack of donors, VADs could be an alternative to heart transplantation for Failing Fontan patients (PTs). Considering the complex physiopathology and the type of VAD connection, a numerical model (NM) could be useful to support clinical decisions. The aim of this work is to test a NM simulating the VADs effects on failing Fontan for systolic dysfunction (SD), diastolic dysfunction (DD) and pulmonary vascular resistance increase (PRI). Methods: Data of 10 Fontan PTs were used to simulate the PTs baseline using a dedicated NM. Then, for each PTs a SD, a DD and a PRI were simulated. Finally, for each PT and for each pathology, the VADs implantation was simulated. Results: NM can well reproduce PTs baseline. In the case of SD, LVAD increases the cardiac output (CO) (35%) and the arterial systemic pressure (ASP) (25%). With cavo-pulmonary assistance (RVAD) a decrease of inferior vena cava pressure (IVCP) (39%) was observed with 34% increase of CO. With the BIVAD an increase of ASP (29%) and CO (37%) was observed. In the case of DD, the LVAD increases CO (42%), the RVAD decreases the IVCP. In the case of PRI, the highest CO (50%) and ASP (28%) increase is obtained with an RVAD together with the highest decrease of IVCP (53%). Conclusions: The use of NM could be helpful in this innovative field to evaluate the VADs implantation effects on specific PT to support PT and VAD selection

Progress in Hemodialysis

Hemodialysis (HD) represents the first successful long-term substitutive therapy with an artificial organ for severe failure of a vital organ. Because HD was started many decades ago, a book on HD may not appear to be up-to-date. Indeed, HD covers many basic and clinical aspects and this book reflects the rapid expansion of new and controversial aspects either in the biotechnological or in the clinical field. This book revises new technologies and therapeutic options to improve dialysis treatment of uremic patients. This book consists of three parts: modeling, methods and technique, prognosis and complications

Contribuciones de las técnicas machine learning a la cardiología. Predicción de reestenosis tras implante de stent coronario

[ES]Antecedentes: Existen pocos temas de actualidad equiparables a la posibilidad de la tecnología actual para desarrollar las mismas capacidades que el ser humano, incluso en medicina. Esta capacidad de simular los procesos de inteligencia humana por parte de máquinas o sistemas informáticos es lo que conocemos hoy en día como inteligencia artificial. Uno de los campos de la inteligencia artificial con mayor aplicación a día de hoy en medicina es el de la predicción, recomendación o diagnóstico, donde se aplican las técnicas machine learning. Asimismo, existe un creciente interés en las técnicas de medicina de precisión, donde las técnicas machine learning pueden ofrecer atención médica individualizada a cada paciente. El intervencionismo coronario percutáneo (ICP) con stent se ha convertido en una práctica habitual en la revascularización de los vasos coronarios con enfermedad aterosclerótica obstructiva significativa. El ICP es asimismo patrón oro de tratamiento en pacientes con infarto agudo de miocardio; reduciendo las tasas de muerte e isquemia recurrente en comparación con el tratamiento médico. El éxito a largo plazo del procedimiento está limitado por la reestenosis del stent, un proceso patológico que provoca un estrechamiento arterial recurrente en el sitio de la ICP. Identificar qué pacientes harán reestenosis es un desafío clínico importante; ya que puede manifestarse como un nuevo infarto agudo de miocardio o forzar una nueva resvascularización del vaso afectado, y que en casos de reestenosis recurrente representa un reto terapéutico. Objetivos: Después de realizar una revisión de las técnicas de inteligencia artificial aplicadas a la medicina y con mayor profundidad, de las técnicas machine learning aplicadas a la cardiología, el objetivo principal de esta tesis doctoral ha sido desarrollar un modelo machine learning para predecir la aparición de reestenosis en pacientes con infarto agudo de miocardio sometidos a ICP con implante de un stent. Asimismo, han sido objetivos secundarios comparar el modelo desarrollado con machine learning con los scores clásicos de riesgo de reestenosis utilizados hasta la fecha; y desarrollar un software que permita trasladar esta contribución a la práctica clínica diaria de forma sencilla. Para desarrollar un modelo fácilmente aplicable, realizamos nuestras predicciones sin variables adicionales a las obtenidas en la práctica rutinaria. Material: El conjunto de datos, obtenido del ensayo GRACIA-3, consistió en 263 pacientes con características demográficas, clínicas y angiográficas; 23 de ellos presentaron reestenosis a los 12 meses después de la implantación del stent. Todos los desarrollos llevados a cabo se han hecho en Python y se ha utilizado computación en la nube, en concreto AWS (Amazon Web Services). Metodología: Se ha utilizado una metodología para trabajar con conjuntos de datos pequeños y no balanceados, siendo importante el esquema de validación cruzada anidada utilizado, así como la utilización de las curvas PR (precision-recall, exhaustividad-sensibilidad), además de las curvas ROC, para la interpretación de los modelos. Se han entrenado los algoritmos más habituales en la literatura para elegir el que mejor comportamiento ha presentado. Resultados: El modelo con mejores resultados ha sido el desarrollado con un clasificador extremely randomized trees; que superó significativamente (0,77; área bajo la curva ROC a los tres scores clínicos clásicos; PRESTO-1 (0,58), PRESTO-2 (0,58) y TLR (0,62). Las curvas exhaustividad sensibilidad ofrecieron una imagen más precisa del rendimiento del modelo extremely randomized trees que muestra un algoritmo eficiente (0,96) para no reestenosis, con alta exhaustividad y alta sensibilidad. Para un umbral considerado óptimo, de 1,000 pacientes sometidos a implante de stent, nuestro modelo machine learning predeciría correctamente 181 (18%) más casos en comparación con el mejor score de riesgo clásico (TLR). Las variables más importantes clasificadas según su contribución a las predicciones fueron diabetes, enfermedad coronaria en 2 ó más vasos, flujo TIMI post-ICP, plaquetas anormales, trombo post-ICP y colesterol anormal. Finalmente, se ha desarrollado una calculadora para trasladar el modelo a la práctica clínica. La calculadora permite estimar el riesgo individual de cada paciente y situarlo en una zona de riesgo, facilitando la toma de decisión al médico en cuanto al seguimiento adecuado para el mismo. Conclusiones: Aplicado inmediatamente después de la implantación del stent, un modelo machine learning diferencia mejor a aquellos pacientes que presentarán o no reestenosis respecto a los discriminadores clásicos actuales