Estimation of changes in instantaneous aortic blood flow by the analysis of arterial blood pressure

The purpose of this study was to introduce and validate a new algorithm to estimate instantaneous aortic blood flow (ABF) by mathematical analysis of arterial blood pressure (ABP) waveforms. The algorithm is based on an autoregressive with exogenous input (ARX) model. We applied this algorithm to diastolic ABP waveforms to estimate the autoregressive model coefficients by requiring the estimated diastolic flow to be zero. The algorithm incorporating the coefficients was then applied to the entire ABP signal to estimate ABF. The algorithm was applied to six Yorkshire swine data sets over a wide range of physiological conditions for validation. Quantitative measures of waveform shape (standard deviation, skewness, and kurtosis), as well as stroke volume and cardiac output from the estimated ABF, were computed. Values of these measures were compared with those obtained from ABF waveforms recorded using a Transonic aortic flow probe placed around the aortic root. The estimation errors were compared with those obtained using a windkessel model. The ARX model algorithm achieved significantly lower errors in the waveform measures, stroke volume, and cardiac output than those obtained using the windkessel model (P < 0.05)

From Inverse Problems in Mathematical Physiology to Quantitative Differential Diagnoses

The improved capacity to acquire quantitative data in a clinical setting has generally failed to improve outcomes in acutely ill patients, suggesting a need for advances in computer-supported data interpretation and decision making. In particular, the application of mathematical models of experimentally elucidated physiological mechanisms could augment the interpretation of quantitative, patient-specific information and help to better target therapy. Yet, such models are typically complex and nonlinear, a reality that often precludes the identification of unique parameters and states of the model that best represent available data. Hypothesizing that this non-uniqueness can convey useful information, we implemented a simplified simulation of a common differential diagnostic process (hypotension in an acute care setting), using a combination of a mathematical model of the cardiovascular system, a stochastic measurement model, and Bayesian inference techniques to quantify parameter and state uncertainty. The output of this procedure is a probability density function on the space of model parameters and initial conditions for a particular patient, based on prior population information together with patient-specific clinical observations. We show that multimodal posterior probability density functions arise naturally, even when unimodal and uninformative priors are used. The peaks of these densities correspond to clinically relevant differential diagnoses and can, in the simplified simulation setting, be constrained to a single diagnosis by assimilating additional observations from dynamical interventions (e.g., fluid challenge). We conclude that the ill-posedness of the inverse problem in quantitative physiology is not merely a technical obstacle, but rather reflects clinical reality and, when addressed adequately in the solution process, provides a novel link between mathematically described physiological knowledge and the clinical concept of differential diagnoses. We outline possible steps toward translating this computational approach to the bedside, to supplement today's evidence-based medicine with a quantitatively founded model-based medicine that integrates mechanistic knowledge with patient-specific information

Autonomic and circulatory alterations persist despite adequate resuscitation in a 5-day sepsis swine experiment.

Autonomic and vascular failures are common phenotypes of sepsis, typically characterized by tachycardia despite corrected hypotension/hypovolemia, vasopressor resistance, increased arterial stiffness and decreased peripheral vascular resistance. In a 5-day swine experiment of polymicrobial sepsis we aimed at characterizing arterial properties and autonomic mechanisms responsible for cardiovascular homeostasis regulation, with the final goal to verify whether the resuscitation therapy in agreement with standard guidelines was successful in restoring a physiological condition of hemodynamic profile, cardiovascular interactions and autonomic control. Twenty pigs were randomized to polymicrobial sepsis and protocol-based resuscitation or to prolonged mechanical ventilation and sedation without sepsis. The animals were studied at baseline, after sepsis development, and every 24 h during the 3-days resuscitation period. Beat-to-beat carotid blood pressure (BP), carotid blood flow, and central venous pressure were continuously recorded. The two-element Windkessel model was adopted to study carotid arterial compliance, systemic vascular resistance and characteristic time constant τ. Effective arterial elastance was calculated as a simple estimate of total arterial load. Cardiac baroreflex sensitivity (BRS) and low frequency (LF) spectral power of diastolic BP were computed to assess autonomic activity. Sepsis induced significant vascular and autonomic alterations, manifested as increased arterial stiffness, decreased vascular resistance and τ constant, reduced BRS and LF power, higher arterial afterload and elevated heart rate in septic pigs compared to sham animals. This compromised condition was persistent until the end of the experiment, despite achievement of recommended resuscitation goals by administered vasopressors and fluids. Vascular and autonomic alterations persist 3 days after goal-directed resuscitation in a clinically relevant sepsis model. We hypothesize that the addition of these variables to standard clinical markers may better profile patients' response to treatment and this could drive a more tailored therapy which could have a potential impact on long-term outcomes

Hemodynamic parameters assessment: an improvement of methodologies

A associação entre a rigidez arterial e as doenças cardiovasculares é um importante tópico de investigação com vista ao conhecimento da condição hemodinâmica dos pacientes. Vários índices podem ser um indicador da rigidez arterial, a velocidade da onda de pulso (VOP) e o índice de aumentação, são dois exemplos. Outros tópicos relacionados com as ondas reflectidas são um poderoso indicador neste contexto. Nesta tese são usados sensores piezoeléctricos para registar a forma da onda de pressão e algoritmos capazes de fornecer informação acerca de certos parâmetros hemodinâmicos, em alternativa aos dispositivos disponíveis no mercado. A principal motivação para procurar uma alternativa a estes dispositivos relaciona-se com o preço a que estes estão disponíveis. O desenvolvimento de uma bancada de teste capaz de simular as principais características da dinâmica do sistema arterial constitui uma poderosa ferramenta com vista ao desenvolvimento de sondas e validação dos algoritmos usados para a extracção de informação clinicamente relevante. O índice de aumentação foi o principal parâmetro estudado, este foi avaliado por um novo algoritmo baseado na transformada de wavelet, em comparação com outros referenciados na literatura. O seu desempenho foi testado em pulsos a partir de uma simulação realista baseadas em exponenciais, bem como em dados experimentais obtidos em testes “clínicos” com alguns voluntários

Probabilistic network models for cardiovascular monitoring

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 83-85).While treating patients during their hospital stay, physicians must frequently take into consideration massive amounts of clinical data. This data can come in many forms, such as continuous blood pressure tracings, intermittent laboratory results, or simple qualitative observations on the patient's appearance. Although access to such a rich collection of information is beneficial for making diagnoses and treatment decisions, it can sometimes be difficult for clinicians to mentally keep track of everything, especially in hectic environments such as hospital intensive care units (ICUs). In addition, there are certain physiological variables that cannot be measured noninvasively, but are critical indicators of a patient's state of health. One such example in cardiology is cardiac output - the mean flow rate of blood from the heart. In this thesis, we explore probabilistic networks as a method for integrating different types of clinical data into a single model, and as a vehicle for summarizing population statistics from medical databases. These networks can then be used to estimate unobservable variables of interest. We propose and test several networks of varying complexity on both a set of experimental porcine data, and a set of real ICU patient data. We find that continuous estimation of cardiac output is possible using probabilistic networks, and that the errors produced are comparable to those obtained from deterministic methods that employ the same in:Formation. Furthermore, since this technique is purely statistical in nature, it can be easily reformulated for applications where deterministic methods do not exist.by Shirley X. Li.M.Eng

Cardiac output estimation using arterial blood pressure waveforms

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 73-74).Cardiac output (CO) is a cardinal parameter of cardiovascular state, and a fundamental determinant of global oxygen delivery. Historically, measurement of CO has been limited to critically-ill patients, using invasive indicator-dilution methods such as thermodilution via Swan-Ganz lines, which carry risks. Over the past century, the premise that CO could be estimated by analysis of the arterial blood pressure (ABP) waveform has captured the attention of many investigators. This approach of estimating CO is minimally invasive, cheap, and can be done continuously as long as ABP waveforms are available. Over a dozen different methods of estimating CO from ABP waveforms have been proposed and some are commercialized. However, the effectiveness of this approach is nebular. Performance validation studies in the past have mostly been conducted on a small set of subjects under well-controlled laboratory conditions. It is entirely possible that there will be circumstances in real world clinical practice in which CO estimation produces inaccurate results. In this thesis, our goals are to (1) build a computational system that estimates CO using 11 of the established methods; (2) evaluate and compare the performance of the CO estimation methods on a large set clinical data, using the simultaneously available thermodilution CO measurements as gold-standard; and (3) design and evaluate an algorithm that identifies and eliminates ABP waveform segments of poor quality. Out of the 11 CO estimation methods studied, there is one method (Liljestrand method) that is clearly more accurate than the rest. Across our study population of 120 subjects, the Liljestrand method has an error distribution with a 1 standard deviation error of 0.8 L/min, which is roughly twice that of thermodilution CO. These results suggest that although CO estimation methods may not generate the most precise values, they are still useful for detecting significant (>1 L/min) changes in CO.by James Xin Sun.M.Eng

Estimation of cardiovascular indices by analysis of the arterial blood pressure signal

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 175-177).This thesis introduces novel mathematical algorithms that track changes in stroke volume (SV), cardiac output (CO), and total peripheral resistance (TPR) by analysis of the arterial blood pressure (ABP) signal. The algorithms incorporate cardiovascular physiology within the framework of a generalized Windkessel model, which is a widely accepted cardiovascular model. Algorithms to identify end systole were also developed and implemented in the new and existing SV, CO, and TPR estimation algorithms. The algorithms were validated by applying them to previously recorded Yorkshire swine data sets that include directly measured aortic blood flow (ABF), SV, CO, as well as central and peripheral ABP. Among the algorithms using the end systole identification algorithms, Parabolic Method, Modified Herd's Method, Kouchoukos Correction Method, and Corrected Impedance Method achieved low root normalized mean squared errors (RNMSEs). This thesis also introduces and validates a novel algorithm to reconstruct instantaneous ABF waveforms from the ABP signal. The algorithm utilizes an auto-regressive with exogenous input (ARX) model to describe the filter between ABF and ABP. Because ABF (the exogenous input to the peripheral circulation) is approximately zero during diastole, the diastolic ABP waveforms can be regarded as auto-regressive (AR). By the AR analysis of multiple diastolic ABP waveforms, the AR parameters are obtained. The AR parameters were applied to the ABP waveforms (both systolic and diastolic) to compute beat-to-beat ABF waveforms. The errors of skewness and kurtosis of the estimated ABF waveforms were statistically smaller than those estimated by the standard Windkessel model. The estimated ABF waveforms were further processed to estimate SV, CO, and TPR. The algorithm achieved RNMSEs of 15.3, 19.6, and 21.8% in SV estimation; 12.7, 15.2, and 15.8% in CO estimation; and 14.3, 20.9, and 19.4 % in TPR estimation derived from central, femoral, and radial ABP, respectively.by Tatsuya Arai.Ph.D