Identification algorithm for systemic arterial parameters with application to total artificial heart control

Ruchti, T.L.; Brown, R.H.; Jeutter, Dean C.; Feng, Xin

doi:10.1007/bf02368178

Cited by 18 publications

(5 citation statements)

References 23 publications

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“…The development of computer models to study hemodynamics in humans started in the 1960s and 1980s (Snyder and Rideout, 1969 ; Avanzolini et al, 1985 , 1988 ; Ursino, 1998 ), with application in pediatrics developed in the 2000s for single-ventricle congenital heart disease (Pennati et al, 1997 ), Norwood physiology (Migliavacca et al, 2001 ), and systemic-to-pulmonary artery shunts (Pennati et al, 2010 ). Approaches for automatic parameter estimation date back to the late 1970s (Deswysen, 1977 ; Deswysen et al, 1980 ), ranging from two-stage Prony-Marquardt optimization (Clark et al, 1980 ) and adaptive control systems for left ventricular bypass assist devices (McInnis et al, 1985 ; Shimooka et al, 1991 ) to Kalman filters (Yu et al, 1998 , 2001 ) and recursive least squares (Ruchti et al, 1993 ). An iterative, proportional gain-based identification method is presented in Revie et al ( 2013 ) and an application to coronary artery disease is discussed in Sughimoto et al ( 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Secondary Pulmonary Hypertension in Left Ventricular Diastolic Dysfunction

et al. 2021

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Diastolic dysfunction is a common pathology occurring in about one third of patients affected by heart failure. This condition may not be associated with a marked decrease in cardiac output or systemic pressure and therefore is more difficult to diagnose than its systolic counterpart. Compromised relaxation or increased stiffness of the left ventricle induces an increase in the upstream pulmonary pressures, and is classified as secondary or group II pulmonary hypertension (2018 Nice classification). This may result in an increase in the right ventricular afterload leading to right ventricular failure. Elevated pulmonary pressures are therefore an important clinical indicator of diastolic heart failure (sometimes referred to as heart failure with preserved ejection fraction, HFpEF), showing significant correlation with associated mortality. However, accurate measurements of this quantity are typically obtained through invasive catheterization and after the onset of symptoms. In this study, we use the hemodynamic consistency of a differential-algebraic circulation model to predict pulmonary pressures in adult patients from other, possibly non-invasive, clinical data. We investigate several aspects of the problem, including the ability of model outputs to represent a sufficiently wide pathologic spectrum, the identifiability of the model's parameters, and the accuracy of the predicted pulmonary pressures. We also find that a classifier using the assimilated model parameters as features is free from the problem of missing data and is able to detect pulmonary hypertension with sufficiently high accuracy. For a cohort of 82 patients suffering from various degrees of heart failure severity, we show that systolic, diastolic, and wedge pulmonary pressures can be estimated on average within 8, 6, and 6 mmHg, respectively. We also show that, in general, increased data availability leads to improved predictions.

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Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Secondary Pulmonary Hypertension in Left Ventricular Diastolic Dysfunction

et al. 2021

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“…Computer-based adaptive control systems for left ventricular bypass assist devices are discussed in [17,18], with an extended Kalman filter approach proposed in [19,20]. A modified recursive least squares algorithm for estimating systemic arterial parameters with application to a total artificial heart control system is discussed in [21]. Identification of patient-specific model parameters using an iterative, proportional gain-based identification method and a minimal number of clinical measurements is presented in [22], while a study on seven patients undergoing coronary artery bypass grafting is discussed in [23].…”

Section: Introductionmentioning

confidence: 99%

“…Approaches for automatic parameter estimation date back to the late 1970s [16, 17], ranging from two-stage Prony-Marquardt optimization [18], to adaptive control systems for left ventricular bypass assist devices [19, 20], to Kalman filters [21, 22] and to recursive least squares [23]. An iterative, proportional gain-based identification method is presented in [24], with application to coronary artery disease in [25].…”

Section: Introductionmentioning

confidence: 99%

Predictive modeling of secondary pulmonary hypertension in left ventricular diastolic dysfunction

Harrod

Rogers²,

Feinstein

et al. 2020

Preprint

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Diastolic dysfunction is a common pathology occurring in about one third of patients affected by heart failure. This condition is not associated with a marked decrease in cardiac output or systemic pressure and therefore is more difficult to diagnose than its systolic counterpart. Compromised relaxation or increased stiffness of the left ventricle with or without mitral valve stenosis induces an increase in the upstream pulmonary pressures, and classified as secondary or group II (2018 Nice classification) pulmonary hypertension. This may result in an increase in the right ventricular afterload leading to right ventricular failure. Elevated pulmonary pressures are therefore an important clinical indicator of diastolic heart failure (sometimes referred to as "heart failure with preserved ejection fraction"), showing significant correlation with associated mortality. Accurate measurements of this quantity, however, are typically obtained through invasive catheterization, and after the onset of symptoms. In this study, we use the hemodynamic consistency of a differential-algebraic circulation model to predict pulmonary pressures in adult patients from other, possibly non-invasive, clinical data. We investigate several aspects of the problem, including the well posedness of a modeling approach for this type of disease, identifiability of its parameters, to the accuracy of the predicted pulmonary pressures. We also find that a classifier using the assimilated model parameters as features is free from the problem of missing data and is able to detect pulmonary hypertension with sufficiently high accuracy. For a cohort of 82 patients suffering from various degrees of heart failure severity we show that systolic, diastolic and wedge pulmonary pressures can be estimated on average within 8, 6 and 6 mmHg, respectively. We also show that, in general, increased data availability leads to improved predictions.

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“…Using current tools, manual tuning of these parameters is required, but is time consuming, operator dependent, and does not account for clinical data uncertainty. To overcome this difficulty, various automatic approaches to parameter estimation have been discussed in the literature (see, e.g., [9, 10, 11, 12, 13, 14, 15]), in most cases providing only point estimates for the unknown parameters.…”

Section: Introductionmentioning

confidence: 99%

Automated tuning for parameter identification and uncertainty quantification in multi-scale coronary simulations

et al. 2017

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Atherosclerotic coronary artery disease, which can result in coronary artery stenosis, acute coronary artery occlusion, and eventually myocardial infarction, is a major cause of morbidity and mortality worldwide. Non-invasive characterization of coronary blood flow is important to improve understanding, prevention, and treatment of this disease. Computational simulations can now produce clinically relevant hemodynamic quantities using only non-invasive measurements, combining detailed three dimensional fluid mechanics with physiological models in a multiscale framework. These models, however, require specification of numerous input parameters and are typically tuned manually without accounting for uncertainty in the clinical data, hindering their application to large clinical studies. We propose an automatic, Bayesian, approach to parameter estimation based on adaptive Markov chain Monte Carlo sampling that assimilates non-invasive quantities commonly acquired in routine clinical care, quantifies the uncertainty in the estimated parameters and computes the confidence in local predicted hemodynamic indicators.

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Identification algorithm for systemic arterial parameters with application to total artificial heart control

Cited by 18 publications

References 23 publications

Predictive Modeling of Secondary Pulmonary Hypertension in Left Ventricular Diastolic Dysfunction

Predictive Modeling of Secondary Pulmonary Hypertension in Left Ventricular Diastolic Dysfunction

Predictive modeling of secondary pulmonary hypertension in left ventricular diastolic dysfunction

Automated tuning for parameter identification and uncertainty quantification in multi-scale coronary simulations

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