A Scalable Approach to Determine Intracardiac Pressure From Mechanical Circulatory Support Device Signals

Validity and Accuracy of the Derived Left Ventricular End-Diastolic Pressure in Impella 5.5

BACKGROUND

Consensus regarding on-support evaluation and weaning concepts from Impella 5.5 support is scarce. The derived left ventricular end-diastolic pressure (dLVEDP), estimated by device algorithms, is a rarely reported tool for monitoring the weaning process. Its validation and clinical accuracy have not been studied in patients. We assess dLVEDP's accuracy in predicting pulmonary capillary wedge pressure (PCWP) and propose a corrective equation.

METHODS

We included 29 consecutive patients treated with Impella 5.5: 12 in a generation cohort and 17 in a validation cohort. dLVEDP and PCWP were measured 5-fold every 8 hours during support, totaling 698 series with 3490 measurements. Variables such as Impella 5.5 performance level, heart rhythm, pacemaker settings, sex, mechanical ventilation, and body mass index were recorded. Linear regression was used to correct dLVEDP-PCWP discrepancies. Analysis included Bland-Altman plots, linear regression, histograms, and violin plots.

RESULTS

The raw dLVEDP and PCWP data did not coincide satisfactorily. The Impella 5.5 dLVEDP overestimation was 3.5±1.5 mm Hg (mean±SD), increasing with higher pressures and unaffected by cardiac rhythm, mechanical ventilation, and performance levels. Statistical correction using the formula modified dLVEDP=-0.457+(1-sex[1=male, 0=female])×0.719-0.0496× body mass index+1.015×body surface area+0.811×dLVEDP significantly reduced the overestimation (P<0.01) to 0.0±1.2 mm Hg.

CONCLUSIONS

dLVEDP, calculated by the Impella 5.5 Smart Algorithm, is a feasible and effective tool for continuously monitoring PCWP at performance levels 3 to 9. Correction of dLVEDP by using the described equation further enhances its accuracy. Hence, hemodynamic surveillance via dLVEDP may aid in managing and weaning temporary microaxial support, potentially reducing the need for continuous monitoring with a Swan-Ganz catheter.

Dynamic load modulation predicts right heart tolerance of left ventricular cardiovascular assist in a porcine model of cardiogenic shock

Ventricular assist devices (VADs) offer mechanical support for patients with cardiogenic shock by unloading the impaired ventricle and increasing cardiac outflow and subsequent tissue perfusion. Their ability to adjust ventricular assistance allows for rapid and safe dynamic changes in cardiac load, which can be used with direct measures of chamber pressures to quantify cardiac pathophysiologic state, predict response to interventions, and unmask vulnerabilities such as limitations of left-sided support efficacy due to intolerance of the right heart. We defined hemodynamic metrics in five pigs with dynamic peripheral transvalvular VAD (pVAD) support to the left ventricle. Metrics were obtained across a spectrum of disease states, including left ventricular ischemia induced by titrated microembolization of a coronary artery and right ventricular strain induced by titrated microembolization of the pulmonary arteries. A sweep of different pVAD speeds confirmed mechanisms of right heart decompensation after left-sided support and revealed intolerance. In contrast to the systemic circulation, pulmonary vascular compliance dominated in the right heart and defined the ability of the right heart to adapt to left-sided pVAD unloading. We developed a clinically accessible metric to measure pulmonary vascular compliance at different pVAD speeds that could predict right heart efficiency and tolerance to left-sided pVAD support. Findings in swine were validated with retrospective hemodynamic data from eight patients on pVAD support. This methodology and metric could be used to track right heart tolerance, predict decompensation before right heart failure, and guide titration of device speed and the need for biventricular support.

Hysteretic device characteristics indicate cardiac contractile state for guiding mechanical circulatory support device use

Background

Acute heart failure and cardiogenic shock remain highly morbid conditions despite prompt medical therapy in critical care settings. Mechanical circulatory support (MCS) is a promising therapy for these patients, yet remains managed with open-loop control. Continuous measure of cardiac function would support and optimize MCS deployment and weaning. The nature of indwelling MCS provides a platform for attaining this information. This study investigates how hysteresis modeling derived from MCS device signals can be used to assess contractility changes to provide continuous indication of changing cardiac state. Load-dependent MCS devices vary their operation with cardiac state to yield a device–heart hysteretic interaction. Predicting and examining this hysteric relation provides insight into cardiac state and can be separated by cardiac cycle phases. Here, we demonstrate this by predicting hysteresis and using the systolic portion of the hysteresis loop to estimate changes in native contractility. This study quantified this measurement as the enclosed area of the systolic portion of the hysteresis loop and correlated it with other widely accepted contractility metrics in animal studies (n = 4) using acute interventions that alter inotropy, including a heart failure model. Clinical validation was performed in patients (n = 8) undergoing Impella support.

Results

Hysteresis is well estimated from device signals alone (r = 0.92, limits of agreement: − 0.18 to 0.18). Quantified systolic area was well correlated in animal studies with end-systolic pressure–volume relationship (r = 0.84), preload recruitable stroke work index (r = 0.77), and maximum slope of left ventricular pressure (dP/dt_max) (r = 0.95) across a range of inotropic conditions. Comparable results were seen in patients with dP/dt_max (r = 0.88). Diagnostic capability from ROC analysis yielded AUC measurements of 0.92 and 0.90 in animal and patients, respectively.

Conclusions

Mechanical circulatory support hysteretic behavior can be well modeled using device signals and used to estimate contractility changes. Contractility estimate is correlated with other accepted metrics, captures temporal trends that elucidate changing cardiac state, and is able to accurately indicate changes in inotropy. Inherently available during MCS deployment, this measure will guide titration and inform need for further intervention.