Simulation of dilated heart failure with continuous flow circulatory support

Lumped parameter models have been employed for decades to simulate important hemodynamic couplings between a left ventricular assist device (LVAD) and the native circulation. However, these studies seldom consider the pathological descending limb of the Frank-Starling response of the overloaded ventricle. This study introduces a dilated heart failure model featuring a unimodal end systolic pressure-volume relationship (ESPVR) to address this critical shortcoming. The resulting hemodynamic response to mechanical circulatory support are illustrated through numerical simulations of a rotodynamic, continuous flow ventricular assist device (cfVAD) coupled to systemic and pulmonary circulations with baroreflex control. The model further incorporated septal interaction to capture the influence of left ventricular (LV) unloading on right ventricular function. Four heart failure conditions were simulated (LV and bi-ventricular failure with/ without pulmonary hypertension) in addition to normal baseline. Several metrics of LV function, including cardiac output and stroke work, exhibited a unimodal response whereby initial unloading improved function, and further unloading depleted preload reserve thereby reducing ventricular output. The concept of extremal loading was introduced to reflect the loading condition in which the intrinsic LV stroke work is maximized. Simulation of bi-ventricular failure with pulmonary hypertension revealed inadequacy of LV support alone. These simulations motivate the implementation of an extremum tracking feedback controller to potentially optimize ventricular recovery. © 2014 Wang et al

2 Year Bayesian Model.

<p>Node colors: red =  class, blue =  parent, purple =  child, yellow =  spouse. Question marks identify nodes that do not have specific evidence set and use the population distribution as the prior distribution. LVEDD =  left ventricle end diastolic diameter, BNP =  B-type natriuretic peptide, LVEF =  left ventricle ejection fraction, GI =  gastrointestinal, IV =  intravenous, ICD =  implantable cardioverter defibrillator, PVD =  peripheral vascular disease, hx HIV =  history of human immunodeficiency virus, BMI =  body mass index, MCS =  mechanical circulatory support, CV =  cardiovascular.</p

1 Year Bayesian Model.

<p>Node colors: red =  class, blue =  parent, purple =  child, yellow =  spouse. Question marks identify nodes that do not have specific evidence set and use the population distribution as the prior distribution. LVEDD =  left ventricle end diastolic diameter, RVEF =  right ventricle ejection fraction, BMI =  body mass index, lim tx PH =  limitation for transplant listing due to pulmonary hypertension, IV =  intravenous, ICD =  implantable cardioverter defibrillator, CV =  cardiovascular, GI =  gastrointestinal, BP =  blood pressure, hx HIV =  history of human immunodeficiency virus, MCS =  mechanical circulatory support.</p

Summary of Bayesian Model Performance.

<p>ROC =  receiver operating characteristic curve.</p><p>Summary of Bayesian Model Performance.</p

30 Day Bayesian Model.

<p>Node colors: red =  class, blue =  parent, purple =  child. Question marks identify nodes that do not have specific evidence set and use the population distribution as the prior distribution. LVEDD =  left ventricle end diastolic diameter, ALT =  alanine transaminase, BP =  blood pressure, mRAP =  mean right atrial pressure, PCWP =  pulmonary capillary wedge pressure, VAS =  visual analog scale, BNP =  B-type natriuretic peptide, WBC =  white blood cell, NYHA =  New York Heart Association functional class, RVEF =  right ventricle ejection fraction, INR =  international normalized ratio, BMI =  body mass index, ECG =  Electrocardiography, QOL =  quality of life, hx HIV =  history of human immunodeficiency virus.</p

90 Day Bayesian Model.

<p>Node colors: red =  class, blue =  parent, purple =  child, yellow =  spouse. Question marks identify nodes that do not have specific evidence set and use the population distribution as the prior distribution. LVEDD =  left ventricle end diastolic diameter, PCWP =  pulmonary capillary wedge pressure, RVEF =  right ventricle ejection fraction, LVEF =  left ventricle ejection fraction, hx HIV =  history of human immunodeficiency virus, ICD =  implantable cardioverter defibrillator, Lim tx =  limitation for transplant listing, GI =  gastrointestinal, IV =  intravenous.</p

Mortality statistics, censored for explant and transplant.

<p>SMOTE: synthetic minority oversampling technique.</p><p>Mortality statistics, censored for explant and transplant.</p

Development of Predictive Models for Continuous Flow Left Ventricular Assist Device Patients using Bayesian Networks

Background: Existing prognostic tools for patient selection for ventricular assist devices (VADs) such as the Destination Therapy Risk Score (DTRS) and newly published HeartMate II Risk Score (HMRS) have limited predictive ability, especially with the current generation of continuous flow VADs (cfVADs). This study aims to use a modern machine learning approach, employing Bayesian Networks (BNs), which overcomes some of the limitations of traditional statistical methods. Methods: Retrospective data from 144 patients at Allegheny General Hospital and Integris Health System from 2007 to 2011 were analyzed. 43 data elements were grouped into four sets: demographics, laboratory tests, hemodynamics, and medications. Patients were stratified by survival at 90 days post LVAD. Results: The independent variables were ranked based on their predictive power and reduced to an optimal set of 10: hematocrit, aspartate aminotransferase, age, heart rate, transpulmonary gradient, mean pulmonary artery pressure, use of diuretics, platelet count, blood urea nitrogen and hemoglobin. Two BNs, Naïve Bayes (NB) and Tree-Augmented Naïve Bayes (TAN) outperformed the DTRS in identifying low risk patients (specificity: 91% and 93% vs. 78%) and outperformed HMRS predictions of high risk patients (sensitivity: 80% and 60% vs. 25%). Both models were more accurate than DTRS and HMRS (90% vs. 73% and 84%), Kappa (NB: 0.56 TAN: 0.48, DTRS: 0.14, HMRS: 0.22), and AUC (NB: 80%, TAN: 84%, DTRS: 59%, HMRS: 59%). Conclusion: The Bayesian Network models developed in this study consistently outperformed the DTRS and HMRS on all metrics. An added advantage is their intuitive graphical structure that closely mimics natural reasoning patterns. This warrants further investigation with an expanded patient cohort, and inclusion of adverse event outcomes