Assessment of cardiac function during mechanical circulatory support: the quest for a suitable clinical index.

A new index to assess left ventricular (LV) function in patients implanted with continuous flow left-ventricular assist devices (LVADs) is proposed. Derived from the pump flow signal, this index is defined as the coefficient (k) of the semilogarithmic relationship between "pseudo-ejection" fraction (pEF) and the volume discharged by the pump in diastole, (V d). pEF is defined as the ratio of the "pseudo-stroke volume" (pSV) to V d. The pseudo-stroke volume is the difference between V d and the volume discharged by the pump in systole (V s), both obtained by integrating pump flow with respect to time in a cardiac cycle. k was compared in-vivo with others two indices: the LV pressure-based index, M(TP), and the pump flow-based index, I(Q). M(TP) is the slope of the linear regression between the "triple-product" and end-diastolic pressure, EDP. The triple-product, TP = LV SP.dP/dt(max). HR, is the product of LV systolic pressure, maximum time-derivative of LV pressure, and heart rate. I(Q) is the slope of the linear regression between maximum time-derivative of pump flow, dQ/dt(max), and pump flow peak-to-peak amplitude variation, Q(P2P). To test the response of k to contractile state changes, contractility was altered through pharmacological interventions. The absolute value of k decreased from 1.354 ± 0.25 (baseline) to 0.685 ± 0.21 after esmolol infusion. The proposed index is sensitive to changes in inotropic state, and has the potential to be used clinically to assess contractile function of patients implanted with VAD.</p

A Classification Approach for Risk Prognosis of Patients on Mechanical Ventricular Assistance.

The identification of optimal candidates for ventricular assist device (VAD) therapy is of great importance for future widespread application of this life-saving technology. During recent years, numerous traditional statistical models have been developed for this task. In this study, we compared three different supervised machine learning techniques for risk prognosis of patients on VAD: Decision Tree, Support Vector Machine (SVM) and Bayesian Tree-Augmented Network, to facilitate the candidate identification. A predictive (C4.5) decision tree model was ultimately developed based on 6 features identified by SVM with assistance of recursive feature elimination. This model performed better compared to the popular risk score of Lietz et al. with respect to identification of high-risk patients and earlier survival differentiation between high- and low- risk candidates.</p

Development of a hybrid decision support model for optimal ventricular assist device weaning.

BACKGROUND: Despite the small but promising body of evidence for cardiac recovery in patients that have received ventricular assist device (VAD) support, the criteria for identifying and selecting candidates who might be weaned from a VAD have not been established. METHODS: A clinical decision support system was developed based on a Bayesian Belief Network that combined expert knowledge with multivariate statistical analysis. Expert knowledge was derived from interviews of 11 members of the Artificial Heart Program at the University of Pittsburgh Medical Center. This was supplemented by retrospective clinical data from the 19 VAD patients considered for weaning between 1996 and 2004. Artificial Neural Networks and Natural Language Processing were used to mine these data and extract sensitive variables. RESULTS: Three decision support models were compared. The model exclusively based on expert-derived knowledge was the least accurate and most conservative. It underestimated the incidence of heart recovery, incorrectly identifying 4 of the successfully weaned patients as transplant candidates. The model derived exclusively from clinical data performed better but misidentified 2 patients: 1 weaned successfully, and 1 that needed a cardiac transplant ultimately. An expert-data hybrid model performed best, with 94.74% accuracy and 75.37% to 99.07% confidence interval, misidentifying only 1 patient weaned from support. CONCLUSIONS: A clinical decision support system may facilitate and improve the identification of VAD patients who are candidates for cardiac recovery and may benefit from VAD removal. It could be potentially used to translate success of active centers to those less established and thereby expand use of VAD therapy.</p

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 ofextremal 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.</p

Aortic arch morphogenesis and flow modeling in the chick embryo.

Morphogenesis of the "immature symmetric embryonic aortic arches" into the "mature and asymmetric aortic arches" involves a delicate sequence of cell and tissue migration, proliferation, and remodeling within an active biomechanical environment. Both patient-derived and experimental animal model data support a significant role for biomechanical forces during arch development. The objective of the present study is to quantify changes in geometry, blood flow, and shear stress patterns (WSS) during a period of normal arch morphogenesis. Composite three-dimensional (3D) models of the chick embryo aortic arches were generated at the Hamburger-Hamilton (HH) developmental stages HH18 and HH24 using fluorescent dye injection, micro-CT, Doppler velocity recordings, and pulsatile subject-specific computational fluid dynamics (CFD). India ink and fluorescent dyes were injected into the embryonic ventricle or atrium to visualize right or left aortic arch morphologies and flows. 3D morphology of the developing great vessels was obtained from polymeric casting followed by micro-CT scan. Inlet aortic arch flow and cerebral-to-lower body flow split was obtained from 20 MHz pulsed Doppler velocity measurements and literature data. Statistically significant variations of the individual arch diameters along the developmental timeline are reported and correlated with WSS calculations from CFD. CFD simulations quantified pulsatile blood flow distribution from the outflow tract through the aortic arches at stages HH18 and HH24. Flow perfusion to all three arch pairs are correlated with the in vivo observations of common pharyngeal arch defect progression. The complex spatial WSS and velocity distributions in the early embryonic aortic arches shifted between stages HH18 and HH24, consistent with increased flow velocities and altered anatomy. The highest values for WSS were noted at sites of narrowest arch diameters. Altered flow and WSS within individual arches could be simulated using altered distributions of inlet flow streams. Thus, inlet flow stream distributions, 3D aortic sac and aortic arch geometries, and local vascular biologic responses to spatial variations in WSS are all likely to be important in the regulation of arch morphogenesis.</p

Prognosis of right ventricular failure in patients with left ventricular assist device based on decision tree with SMOTE.

<p>Right ventricular failure is a significant complication following implantation of a left ventricular assist device (LVAD), which increases morbidity and mortality. Consequently, researchers have sought predictors that may identify patients at risk. However, they have lacked sensitivity and/or specificity. This study investigated the use of a decision tree technology to explore the preoperative data space for combinatorial relationships that may be more accurate and precise. We retrospectively analyzed the records of 183 patients with initial LVAD implantation at the Artificial Heart Program, University of Pittsburgh Medical Center, between May 1996 and October 2009. Among those patients, 27 later required a right ventricular assist device (RVAD+) and 156 remained on LVAD (RVAD-) until the time of transplantation or death. A synthetic minority oversampling technique (SMOTE) was applied to the RVAD+ group to compensate for the disparity of sample size. Twenty-one resampling levels were evaluated, with decision tree model built for each. Among these models, the top six predictors of the need for an RVAD were transpulmonary gradient (TPG), age, international normalized ratio (INR), heart rate (HR), aspartate aminotransferase (AST), prothrombin time, and right ventricular systolic pressure. TPG was identified to be the most predictive variable in 15 out of 21 models, and constituted the first splitting node with 7 mmHg as the breakpoint. Oversampling was shown to improve the senstivity of the models monotonically, although asymptotically, while the specificity was diminished to a lesser degree. The model built upon 5X synthetic RVAD+ oversampling was found to provide the best compromise between sensitivity and specificity, included TPG (layer 1), age (layer 2), right atrial pressure (layer 3), HR (layer 4,7), INR (layer 4, 9), alanine aminotransferase (layer 5), white blood cell count (layer 5,6, &7), the number of inotrope agents (layer 6), creatinine (layer 8), AST (layer 9, 10), and cardiac output (layer 9). It exhibited 85% sensitivity, 83% specificity, and 0.87 area under the receiver operating characteristic curve (RoC), which was found to be greatly improved compared to previously published studies.</p

Decision tree for adjuvant right ventricular support in patients receiving a left ventricular assist device.

<p>BACKGROUND: Right ventricular (RV) failure is a significant complication after implantation of a left ventricular assist device (LVAD). It is therefore important to identify patients at risk a priori. However, prognostic models derived from multivariate analyses have had limited predictive power.</p> <p>METHODS: This study retrospectively analyzed the records of 183 LVAD recipients between May 1996 and October 2009; of these, 27 later required a RVAD (RVAD(+)) and 156 remained on LVAD only (RVAD(-)) until transplant or death. A decision tree model was constructed to represent combinatorial non-linear relationships of the pre-operative data that are predictive of the need for RVAD support.</p> <p>RESULTS: An optimal set of 8 pre-operative variables were identified: transpulmonary gradient, age, right atrial pressure, international normalized ratio, heart rate, white blood cell count, alanine aminotransferase, and the number of inotropic agents. The resultant decision tree, which consisted of 28 branches and 15 leaves, identified RVAD(+) patients with 85% sensitivity, RVAD(-) patients with 83% specificity, and exhibited an area under the receiver operating characteristic curve of 0.87.</p> <p>CONCLUSIONS: The decision tree model developed in this study exhibited several advantages compared with existing risk scores. Quantitatively, it provided improved prognosis of RV support by encoding the non-linear, synergic interactions among pre-operative variables. Because of its intuitive structure, it more closely mimics clinical reasoning and therefore can be more readily interpreted. Further development with additional multicenter, longitudinal data may provide a valuable prognostic tool for triage of LVAD therapy and, potentially, improve outcomes.</p

Critical Transitions in Early Embryonic Aortic Arch Patterning and Hemodynamics

<p>Transformation from the bilaterally symmetric embryonic aortic arches to the mature great vessels is a complex morphogenetic process, requiring both vasculogenic and angiogenic mechanisms. Early aortic arch development occurs simultaneously with rapid changes in pulsatile blood flow, ventricular function, and downstream impedance in both invertebrate and vertebrate species. These dynamic biomechanical environmental landscapes provide critical epigenetic cues for vascular growth and remodeling. In our previous work, we examined hemodynamic loading and aortic arch growth in the chick embryo at Hamburger-Hamilton stages 18 and 24. We provided the first quantitative correlation between wall shear stress (WSS) and aortic arch diameter in the developing embryo, and observed that these two stages contained different aortic arch patterns with no inter-embryo variation. In the present study, we investigate these biomechanical events in the intermediate stage 21 to determine insights into this critical transition. We performed fluorescent dye microinjections to identify aortic arch patterns and measured diameters using both injection recordings and high-resolution optical coherence tomography. Flow and WSS were quantified with 3D computational fluid dynamics (CFD). Dye injections revealed that the transition in aortic arch pattern is not a uniform process and multiple configurations were documented at stage 21. CFD analysis showed that WSS is substantially elevated compared to both the previous (stage 18) and subsequent (stage 24) developmental time-points. These results demonstrate that acute increases in WSS are followed by a period of vascular remodeling to restore normative hemodynamic loading. Fluctuations in blood flow are one possible mechanism that impacts the timing of events such as aortic arch regression and generation, leading to the variable configurations at stage 21. Aortic arch variations noted during normal rapid vascular remodeling at stage 21 identify a temporal window of increased vulnerability to aberrant aortic arch morphogenesis with the potential for profound effects on subsequent cardiovascular morphogenesis.</p