Closing the Loop: Modelling of Heart Failure Progression from Health to End-Stage Using a Meta-Analysis of Left Ventricular Pressure-Volume Loops

Introduction The American Heart Association (AHA)/American College of Cardiology (ACC) guidelines for the classification of heart failure (HF) are descriptive but lack precise and objective measures which would assist in categorising such patients. Our aim was two fold, firstly to demonstrate quantitatively the progression of HF through each stage using a meta-analysis of existing left ventricular (LV) pressure-volume (PV) loop data and secondly use the LV PV loop data to create stage specific HF models. Methods and Results A literature search yielded 31 papers with PV data, representing over 200 patients in different stages of HF. The raw pressure and volume data were extracted from the papers using a digitising software package and the means were calculated. The data demonstrated that, as HF progressed, stroke volume (SV), ejection fraction (EF%) decreased while LV volumes increased. A 2-element lumped parameter model was employed to model the mean loops and the error was calculated between the loops, demonstrating close fit between the loops. The only parameter that was consistently and statistically different across all the stages was the elastance (Emax). Conclusions For the first time, the authors have created a visual and quantitative representation of the AHA/ACC stages of LVSD-HF, from normal to end-stage. The study demonstrates that robust, load-independent and reproducible parameters, such as elastance, can be used to categorise and model HF, complementing the existing classification. The modelled PV loops establish previously unknown physiological parameters for each AHA/ACC stage of LVSD-HF, such as LV elastance and highlight that it this parameter alone, in lumped parameter models, that determines the severity of HF. Such information will enable cardiovascular modellers with an interest in HF, to create more accurate models of the heart as it fails

Cardiovascular dynamics in ischemic cardiomyopathy during exercise

Cardiac Dysfunction and Arrhythmia

Detection of dicrotic notch in arterial pressure signals

A novel algorithm to detect the di- crotic notch in arterial pressure signals is proposed. Its per- formance is evaluated using both aortic and radial artery pressure signals, and its robustness to variations in design parameters is investigated. Methods. Most previously pub- lished dicrotic notch detection algorithms scan the arterial pressure waveform for the characteristic pressure change that is associated with the dicrotic notch. Aortic valves, however, are closed by the backwards motion of aortic blood volume. We developed an algorithm that uses arterial £ow to detect the dicrotic notch in arterial pressure waveforms. Arterial £ow is calculated from arterial pressure using simulation results with a three-element windkessel model. Aortic valve closure is detected after the systolic upstroke and at the minimum of the ¢rst negative dip in the calculated £ow signal. Results. In 7 dogs ejection times were derived from a calculated aortic £ow signal and from simultaneously meas- ured aortic £ow probe data. A total of 86 beats was analyzed; the di¡erence in ejection times was ÿ0.6 ?? 5.4 ms (mean ?? SD). The algorithm was further evaluated using 6 second epochs of radial artery pressure data measured in 50 patients. Model simulations were carried out using both a linear wind- kessel model and a pressure and age dependent nonlinear windkessel model.Visual inspection by an experienced clini- cian con¢rmed that the algorithm correctly identi¢ed the dicrotic notch in 98% (49 of 50) of the patients using the linear model, and 96% (48 of 50) of the patients using the nonlinear model. The position of the dicrotic notch appeared to be less sensitive to variations in algorithm's design parame- ters when a nonlinear windkessel model was used. Conclu- sions. The detection of the dicrotic notch in arterial pressure signals is facilitated by ¢rst calculating the arterial £ow wave- form from arterial pressure and a model of arterial afterload. The method is robust and reduces the problem of detecting a dubious point in a decreasing pressure signal to the detection of a well-de¢ned minimum in a derived signal

Left ventricular wall stress normalization in chronic pressure-overloaded heart: a mathematical model study.

It is generally accepted that the left ventricle (LV) hypertrophies (LVH) to normalize systolic wall stress (σ(s)) in chronic pressure overload. However, LV filling pressure (P(v)) may be elevated as well, supporting the alternative hypothesis of end-diastolic wall stress (σ(d)) normalization in LVH. We used an LV time-varying elastance model coupled to an arterial four-element lumped-parameter model to study ventriculararterial interaction in hypertension-induced LVH. We assessed model parameters for normotensive controls and applied arterial changes as observed in hypertensive patients with LVH (resistance +40%, compliance -25%) and assumed 1) no cardiac adaptation, 2) normalization of σ(s) by LVH, and 3) normalization of σ(s) by LVH and increase in P(v), such that σ(d) is normalized as well. In patients, systolic and diastolic blood pressures increase by ~40%, cardiac output (CO) is constant, and wall thickness increases by 30-55%. In scenarios 1 and 2, blood pressure increased by only 10% while CO dropped by 20%. In scenario 2, LV wall thickness increased by only 10%. The predictions of scenario 3 were in qualitative and quantitative agreement with in vivo human data. LVH thus contributes to the elevated blood pressure in hypertension, and cardiac adaptations include an increase in P(v), normalization of σ(s), and preservation of CO in the presence of an impaired diastolic function