Determination of physiologic states during mechanical circulatory support through characterization of device-organ interactions

Organ transplantation is a valuable treatment for organ failure; however, it is limited by an increasing shortage of donor organs. Because of this, mechanical support has emerged as an effective therapy to promote recovery of native organ function, especially in the setting of acute heart failure. Acute heart failure is increasingly prominent and inadequately treated by traditional medical therapy. Mechanical circulatory support (MCS) devices unload the heart by offering a range of support that reduces mortality and promotes cardiac recovery when correctly used. The challenge in use of these devices is the lack of metric-driven control for the level of support currently manually determined by a clinician. We hypothesize that optimization of device use requires novel insights in physiology and definition of organ state through an understanding of device-organ interconnectivity in support devices that are coupled with residual organ function. Thus, the goals of this work are to leverage the interaction between support device and organ to assess the state of the organ and then use this information towards improved device control and understanding of organ pathophysiology. The research program used an integrated approach of bench-top testing, animal models, and retrospective patient data to determine advanced markers of cardiac function using the Abiomed Impella as a paradigmatic device. We developed a mock circulatory loop to identify how MCS devices operate over the cardiac cycle during changing cardiovascular states. Parametric analysis revealed a hysteretic state-responsive relationship between the device and subject physiology. Since device operation is characterized using the MCL, unaccounted hysteresis changes can be attributed to variation in the cardiac state. We utilized this model to predict novel metrics of cardiac dynamics and easily-validated parameters of cardiac state in both acute animal models and retrospective patient data in which we accurately differentiated disease states and clinical outcomes. Finally, we investigated how MCS can affect downstream vascular response in animals and patients by analyzing arterial pressure waveforms with known device performance to quantify vascular state and device-vascular coupling.

Department dc:contributor.department

Harvard--MIT Program in Health Sciences and Technology.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Chang, Brian Yale

Advisor dc:contributor.advisor

• Elazer R. Edelman.

• Harvard--MIT Program in Health Sciences and Technology.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/118030

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/118030