Advances in computational modelling for personalised medicine after myocardial infarction

Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners

Moving Domain Computational Fluid Dynamics to Interface with an Embryonic Model of Cardiac Morphogenesis

Peristaltic contraction of the embryonic heart tube produces time- and spatial-varying wall shear stress (WSS) and pressure gradients (∇P) across the atrioventricular (AV) canal. Zebrafish (Danio rerio) are a genetically tractable system to investigate cardiac morphogenesis. The use of Tg(fli1a:EGFP)y1 transgenic embryos allowed for delineation and two-dimensional reconstruction of the endocardium. This time-varying wall motion was then prescribed in a two-dimensional moving domain computational fluid dynamics (CFD) model, providing new insights into spatial and temporal variations in WSS and ∇P during cardiac development. The CFD simulations were validated with particle image velocimetry (PIV) across the atrioventricular (AV) canal, revealing an increase in both velocities and heart rates, but a decrease in the duration of atrial systole from early to later stages. At 20-30 hours post fertilization (hpf), simulation results revealed bidirectional WSS across the AV canal in the heart tube in response to peristaltic motion of the wall. At 40-50 hpf, the tube structure undergoes cardiac looping, accompanied by a nearly 3-fold increase in WSS magnitude. At 110-120 hpf, distinct AV valve, atrium, ventricle, and bulbus arteriosus form, accompanied by incremental increases in both WSS magnitude and ∇P, but a decrease in bi-directional flow. Laminar flow develops across the AV canal at 20-30 hpf, and persists at 110-120 hpf. Reynolds numbers at the AV canal increase from 0.07±0.03 at 20-30 hpf to 0.23±0.07 at 110-120 hpf (p< 0.05, n=6), whereas Womersley numbers remain relatively unchanged from 0.11 to 0.13. Our moving domain simulations highlights hemodynamic changes in relation to cardiac morphogenesis; thereby, providing a 2-D quantitative approach to complement imaging analysis. © 2013 Lee et al

Impact of diabetes mellitus on ventricular structure, arterial stiffness, and pulsatile hemodynamics in heart failure with preserved ejection fraction

Background-Heterogeneity in the underlying processes that contribute to heart failure with preserved ejection fraction (HFpEF) is increasingly recognized. Diabetes mellitus is a frequent comorbidity in HFpEF, but its impact on left ventricular and arterial structure and function in HFpEF is unknown. Methods and Results-Weassessed the impact of diabetesmellitus on left ventricular cellular and interstitial hypertrophy (assessedwith cardiacmagnetic resonance imaging, including T1mapping pregadolinium and postgadolinium administration), arterial stiffness (assessed with arterial tonometry), and pulsatile arterial hemodynamics (assessed with in-office pressure-flow analyses and 24-hour ambulatory monitoring) among 53 subjects with HFpEF (32 diabetic and 21 nondiabetic subjects). Despite few differences in clinical characteristics, diabetic subjects with HFpEF exhibited a markedly greater left ventricular mass index (78.1 [95% CI, 70.4-85.9] g versus 63.6 [95% CI, 55.8-71.3] g; P=0.0093) and indexed extracellular volume (23.6 [95% CI, 21.2-26.1] mL/m(2) versus 16.2 [95% CI, 13.1-19.4] mL/m(2); P=0.0008). Pronounced aortic stiffening was also observed in the diabetic group (carotid-femoral pulse wave velocity, 11.86 [95% CI, 10.4-13.1] m/s versus 8.8 [95% CI, 7.5-10.1] m/s; P=0.0027), with an adverse pulsatile hemodynamic profile characterized by increased oscillatory power (315 [95% CI, 258-373] mWversus 190 [95% CI, 144-236] mW; P=0.0007), aortic characteristic impedance (0.154 [95% CI, 0.124-0.183] mmHg/mL per second versus 0.096 [95% CI, 0.072-0.121] mm Hg/mL per second; P=0.0024), and forward (59.5 [95% CI, 52.8-66.1] mm Hg versus 40.1 [95% CI, 31.6-48.6] mm Hg; P=0.0010) and backward (19.6 [95% CI, 16.2-22.9] mm Hg versus 14.1 [95% CI, 10.9-17.3] mm Hg; P=0.0169) wave amplitude. Abnormal pulsatile hemodynamics were also evident in 24-hour ambulatory monitoring, despite the absence of significant differences in 24-hour systolic blood pressure between the groups. Conclusions-Diabetes mellitus is a key determinant of left ventricular remodeling, arterial stiffness, adverse pulsatile hemodynamics, and ventricular-arterial interactions in HFpEF

The Relationship of Left Ventricular Trabeculation to Ventricular Function and Structure Over a 9.5-Year Follow-Up The MESA Study

Left ventricular (LV) trabeculation is highly variable among individuals and is increased in some diseases (e.g., congenital heart disease or cardiomyopathies), but its significance in population-representative individuals is unknown

Characterization and interpretation of cardiovascular and cardiorespiratory dynamics in cardiomyopathy patients

Aplicat embargament des de la data de defensa fins el dia 20/5/2022The main objective of this thesis was to study the variability of the cardiac, respiratory and vascular systems through electrocardiographic (ECG), respiratory flow (FLW) and blood pressure (BP) signals, in patients with idiopathic (IDC), dilated (DCM), or ischemic (ICM) disease. The aim of this work was to introduce new indices that could contribute to characterizing these diseases. With these new indices, we propose methods to classify cardiomyopathy patients (CMP) according to their cardiovascular risk or etiology. In addition, a new tool was proposed to reconstruct artifacts in biomedical signals. From the ECG, BP and FLW signals, different data series were extracted: beat to beat intervals (BBI - ECG), systolic and diastolic blood pressure (SBP and DBP - BP), and breathing duration (TT - FLW). -Firstly, we propose a novel artifact reconstruction method applied to biomedical signals. The reconstruction process makes use of information from neighboring events while maintaining the dynamics of the original signal. The method is based on detecting the cycles and artifacts, identifying the number of cycles to reconstruct, and predicting the cycles used to replace the artifact segments. The reconstruction results showed that most of the artifacts were correctly detected, and physiological cycles were incorrectly detected as artifacts in fewer than 1% of the cases. The second part is related to the cardiac death risk stratification of patients based on their left ventricular ejection (LVEF), using the Poincaré plot analysis, and classified as low (LVEF > 35%) or high (LVEF = 35%) risk. The BBI, SBP, and IT series of 46 CMP patients were applied. The linear discriminant analysis and support vector machines (SVM) classification methods were used. When comparing low risk vs high risk, an accuracy of 98 12% was obtained. Our results suggest that a dysfunction in the vagal activity could prevent the body from correctly maintaining circulatory homeostasis Next, we studied cardio-vascular couplings based on heart rate (HRV) and blood pressure (BPV) variability analyses in order to introduce new indices for noninvasive risk stratification in IDC patients. The ECG and BP signals of 91 IDC patients, and 49 healthy subjects were used. The patients were stratified by their sudden cardiac death risk as: high risk (IDCHR), when after two years the subject either died or suffered complications, or low risk (IDCLR) otherwise. Several indices were extracted from the BBI and SBP, and analyzed using the segmented Poincaré plot analysis, the high-resolution joint symbolic dynamics, and the normalized short time partial directed coherence methods. SVM models were built to classify these patients based on their sudden cardiac death risk. The SVM IDCLR vs IDCHR model achieved 98 9% accuracy with an area under the curve (AUC) of 0.96. Our results suggest that IDCHR patients have decreased HRV and increased BPV compared to both the IDCLR patients and the control subjects, suggesting a decrease in their vagal activity and the compensation of sympathetic activity. Lastly, we analyzed the cardiorespiratory interaction associated with the systems related to ICM and DCM disease. We propose an analysis based on vascular activity as the input and output of the baroreflex response. The aim was to analyze the suitability of cardiorespiratory and vascular interactions for the classification of ICM and DCM patients. We studied 41 CMP patients and 39 healthy subjects. Three new sub-spaces were defined: 'up' for increasing values, 'down' for decreasing values, and 'no change' otherwise, and a three-dimensional representation was created for each sub-space that was characterized statistically and morphologically. The resulting indices were used to classify the patients by their etiology through SVM models achieving 92.7% accuracy for ICM vs DCM patients comparison. The results reflected a more pronounced deterioration of the autonomous regulation in DCM patients.El objetivo de esta tesis fue estudiar la variabilidad de los sistemas cardíaco, respiratorio y vascular a través de señales electrocardiográficas (ECG), de flujo respiratorio (FLW) y de presión arterial (BP), en pacientes con cardiopatía idiopática (IDC). dilatada (DCM) o isquémica (ICM). El objetivo de este trabajo fue introducir nuevos indices que contribuyan a caracterizar estas enfermedades. Proponemos métodos para clasificar pacientes con cardiomiopatía (CMP) de acuerdo con su riesgo cardiovascular o etiología. Además, se propuso una nueva herramienta para reconstruir artefactos en señales biomédicas. De las señales de ECG, BP y FLW, se extrajeron diferentes series temporales: intervalos latido-a-latido (BBI - ECG), presión arterial sistólica y diastólica (SBP y DBP - BP) y la duración de la respiración (TT - FLW). En primer lugar, proponemos un método de reconstrucción de artefactos aplicado a señales biomédicas. El proceso de reconstrucción usa la información de eventos vecinos manteniendo la dinámica de la señal. El método se basa en detectar ciclos y artefactos, en identificar el número de ciclos a reconstruir y en predecir los ciclos utilizados para reemplazar los artefactos. La mayoría de los artefactos probados fueron detectados y reconstruidos correctamente y los ciclos fisiológicos fueron detectados incorrectamente como artefactos en menos del 1% de los casos, La segunda parte está relacionada con la estratificación de riesgo de muerte cardiovascular en función de la fracción de eyección ventricular izquierda (FEVI), mediante el análisis de Poincaré, en bajo (FEVI > 35%) y alto riesgo (FEVI 5 35%). Se utilizaron las series BBI, SBP y TT de 46 pacientes con CMP. Se utilizaron para la clasificación el análisis discriminante lineal y las máquinas de soporte vectorial (SVM). Al comparar los pacientes de bajo y alto riesgo, se obtuvo una exactitud del 98%. Los resultados sugieren la disfunción de la actividad vagal en pacientes de alto riesgo. A continuación, estudiamos los acoplamientos cardiovasculares basados en el análisis de la variabilidad de la frecuencia cardiaca (HRV) y la presión arterial (BPV) para introducir nuevos índices de estratificación de riesgo en pacientes con IDC. Se utilizaron las señales de ECG y BP de 91 pacientes con IDC y 49 sujetos sanos. Los pacientes fueron estratificados por su riesgo cardíaco como: alto riesgo (IDCHR), cuando después de dos años el sujeto murió, o bajo riesgo (IDCLR) en otro caso. Se extrajeron indices utilizando el análisis de Poincaré segmentado, la dinámica simbólica articulada de alta resolución y la coherencia parcial dirigida a corto plazo normalizada. Se construyeron modelos SVM para clasificar a estos pacientes en función de su riesgo cardiovascular. El modelo IDCLR vs IDCHR logró una exactitud del 98% con un área bajo la curva de 0.96. Los resultados sugieren que los pacientes IDCHR tienen sus HRV y BPV disminuidos en comparación con los pacientes IDCLR, lo que sugiere una disminución en su actividad vagal y la compensación de la actividad simpática. Finalmente, analizamos la interacción cardiorrespiratoria asociada con los sistemas relacionados con ICM y DCM. Proponemos un análisis basado en la actividad vascular como entrada y salida de la respuesta baroreflectora. El objetivo fue analizar la capacidad de las interacciones cardiorrespiratorias y vasculares para la clasificación de pacientes con ICM y DCM. Estudiamos 41 pacientes con CMP y 39 sujetos sanos. Se definieron tres sub-espacios: 'up' para valores crecientes, 'down' para los decrecientes, y 'no-change' en otro caso, y se creó una representación tridimensional que se caracterizó estadística y morfológicamente. Los indices resultantes se usaron para clasificar a los pacientes por su etiología con modelos SVM que lograron una exactitud de 92% cuando los pacientes ICM y DCM fueron comparados. Los resultados reflejaron un deterioro más pronunciado de la regulación autónoma en pacientes con DCM.Postprint (published version

The anthropometric, environmental and genetic determinants of right ventricular structure and function

BACKGROUND Measures of right ventricular (RV) structure and function have significant prognostic value. The right ventricle is currently assessed by global measures, or point surrogates, which are insensitive to regional and directional changes. We aim to create a high-resolution three-dimensional RV model to improve understanding of its structural and functional determinants. These may be particularly of interest in pulmonary hypertension (PH), a condition in which RV function and outcome are strongly linked. PURPOSE To investigate the feasibility and additional benefit of applying three-dimensional phenotyping and contemporary statistical and genetic approaches to large patient populations. METHODS Healthy subjects and incident PH patients were prospectively recruited. Using a semi-automated atlas-based segmentation algorithm, 3D models characterising RV wall position and displacement were developed, validated and compared with anthropometric, physiological and genetic influences. Statistical techniques were adapted from other high-dimensional approaches to deal with the problems of multiple testing, contiguity, sparsity and computational burden. RESULTS 1527 healthy subjects successfully completed high-resolution 3D CMR and automated segmentation. Of these, 927 subjects underwent next-generation sequencing of the sarcomeric gene titin and 947 subjects completed genotyping of common variants for genome-wide association study. 405 incident PH patients were recruited, of whom 256 completed phenotyping. 3D modelling demonstrated significant reductions in sample size compared to two-dimensional approaches. 3D analysis demonstrated that RV basal-freewall function reflects global functional changes most accurately and that a similar region in PH patients provides stronger survival prediction than all anthropometric, haemodynamic and functional markers. Vascular stiffness, titin truncating variants and common variants may also contribute to changes in RV structure and function. CONCLUSIONS High-resolution phenotyping coupled with computational analysis methods can improve insights into the determinants of RV structure and function in both healthy subjects and PH patients. Large, population-based approaches offer physiological insights relevant to clinical care in selected patient groups.Open Acces

Microvascular resistance predicts myocardial salvage and infarct characteristics in ST-elevation myocardial infarction

&lt;b&gt;Background:&lt;/b&gt; The pathophysiology of myocardial injury and repair in patients with ST‐elevation myocardial infarction is incompletely understood. We investigated the relationships among culprit artery microvascular resistance, myocardial salvage, and ventricular function.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Methods and Results:&lt;/b&gt; The index of microvascular resistance (IMR) was measured by means of a pressure‐ and temperature‐sensitive coronary guidewire in 108 patients with ST‐elevation myocardial infarction (83% male) at the end of primary percutaneous coronary intervention. Paired cardiac MRI (cardiac magnetic resonance) scans were performed early (2 days; n=108) and late (3 months; n=96) after myocardial infarction. T2‐weighted‐ and late gadolinium–enhanced cardiac magnetic resonance delineated the ischemic area at risk and infarct size, respectively. Myocardial salvage was calculated by subtracting infarct size from area at risk. Univariable and multivariable models were constructed to determine the impact of IMR on cardiac magnetic resonance–derived surrogate outcomes. The median (interquartile range) IMR was 28 (17–42) mm Hg/s. The median (interquartile range) area at risk was 32% (24%–41%) of left ventricular mass, and the myocardial salvage index was 21% (11%–43%). IMR was a significant multivariable predictor of early myocardial salvage, with a multiplicative effect of 0.87 (95% confidence interval 0.82 to 0.92) per 20% increase in IMR; P&#60;0.001. In patients with anterior myocardial infarction, IMR was a multivariable predictor of early and late myocardial salvage, with multiplicative effects of 0.82 (95% confidence interval 0.75 to 0.90; P&#60;0.001) and 0.92 (95% confidence interval 0.88 to 0.96; P&#60;0.001), respectively. IMR also predicted the presence and extent of microvascular obstruction and myocardial hemorrhage.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Conclusion:&lt;/b&gt; Microvascular resistance measured during primary percutaneous coronary intervention significantly predicts myocardial salvage, infarct characteristics, and left ventricular ejection fraction in patients with ST‐elevation myocardial infarction.&lt;p&gt;&lt;/p&gt

Characterization and classification of patients with different levels of cardiac death risk by using Poincaré plot analysis

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksCardiac death risk is still a big problem by an important part of the population, especially in elderly patients. In this study, we propose to characterize and analyze the cardiovascular and cardiorespiratory systems using the Poincaré plot. A total of 46 cardiomyopathy patients and 36 healthy subjets were analyzed. Left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF > 35%, 16 patients), and high risk (HR: LVEF = 35%, 30 patients) of heart attack. RR, SBP and T Tot time series were extracted from the ECG, blood pressure and respiratory flow signals, respectively. Parameters that describe the scatterplott of Poincaré method, related to short- and long-term variabilities, acceleration and deceleration of the dynamic system, and the complex correlation index were extracted. The linear discriminant analysis (LDA) and the support vector machines (SVM) classification methods were used to analyze the results of the extracted parameters. The results showed that cardiac parameters were the best to discriminate between HR and LR groups, especially the complex correlation index (p = 0.009). Analising the interaction, the best result was obtained with the relation between the difference of the standard deviation of the cardiac and respiratory system (p = 0.003). When comparing HR vs LR groups, the best classification was obtained applying SVM method, using an ANOVA kernel, with an accuracy of 98.12%. An accuracy of 97.01% was obtained by comparing patients versus healthy, with a SVM classifier and Laplacian kernel. The morphology of Poincaré plot introduces parameters that allow the characterization of the cardiorespiratory system dynamicsPeer ReviewedPostprint (author's final draft