Prediction of clinical response to cardiac resynchronization therapy

Insilicomed, Inc., La Jolla CA

Investigators

Abstract

ABSTRACT Cardiac resynchronization therapy (CRT) is currently used extensively for treatment of congestive heart failure patients with underlying electrical conduction defects. Up to 30% of patients receiving CRT do not respond to the therapy for unknown reasons. This proposal addresses the need to identify whether a patient will respond to CRT before the surgical procedures are performed. This need will be addressed by development of patient-specific electromechanical computational models of the failing human heart that are able to predict the acute effects of CRT, and hence long-term prognosis. Insilicomed, Inc. is marketing unique computational software for multi-scale modeling in biomechanics and other areas, and is the exclusive licensee of software developed at UC San Diego for multi-scale cardiovascular modeling. The present proposal will use established methods for modeling ventricular function for the analysis of CRT patient outcomes for use in clinical centers. The proposal combines extensive experimental, theoretical and business experience of the founders and advisors of Insilicomed in developing commercial novel integrative computational models of cardiac electrophysiology and biomechanics. The specific aims are to (1) Develop a generic computational model of failing dyssynchronous human heart electromechanics based on detailed anatomic and functional measurements available in public databases. Existing, validated models of failing canine and normal human hearts will be modified for failing human hearts. (2) Develop methods to apply the model of Aim 1 to patients using only limited clinical measurements as inputs. Automated methods will be developed to morph the detailed generic heart model into a patientspecific individualized model based on available pre-CRT implant input measurements from our clinical collaborators. (3) Use the computational model of a failing patient-specific heart to predict the acute response to CRT. The models will be tested and validated based upon pre- and post-CRT cardiac function in a pilot clinical study. Biventricular pacing will be applied to individualized models employing clinical pacing protocols. We will compare the model-based predictions for patients who did and did not respond to CRT. Clinical assessment of acute improvement in systolic function at followup is used to validate model-based predictions of CRT efficacy. This predictive method for determining the response to CRT, if successful, will enable Insilicomed to provide physicians with a clinical tool to identify those patients who are most likely to have a positive long-term response CRT based on their predicted acute response, and will set the stage for a large-scale clinical study.