Candidate: Dr. Strom received an MD from Harvard Medical School (HMS), and has completed clinical training in internal medicine (MGH), cardiology (BIDMC), and non-invasive cardiac imaging (BIDMC). Additionally, he completed an MSc in Epidemiology program at the Harvard T.H. Chan School of Public Health, in May, 2018. He is now a junior faculty member at BIDMC with 75% protected time to conduct clinical research. Through this proposed 5-year program, Dr. Strom will pursue additional training in advanced prediction modeling, machine learning, patient-oriented research, and metabolomics. The candidate?s long- term goal is to become an R01-funded investigator in the area of applied outcomes research. Environment: The candidate will be mentored by Robert W. Yeh (Primary Mentor), Associate Professor of Medicine at HMS and Director of the Smith Center for Outcomes Research in Cardiology, Robert E. Gerszten (Co-Mentor), Professor of Medicine at HMS and Chief of Cardiovascular Medicine at BIDMC, and Changyu Shen (Co-Mentor), upcoming Associate Professor of Medicine at HMS and Lead Biostatistician for the Smith Center. Dr. Yeh has a track record of leading practice-changing studies and successful mentorship of clinician investigators. Dr. Gerszten is an internationally recognized expert in molecular phenotyping of cardiovascular diseases using metabolomics and proteomics and has mentored several K-award and R01 funded clinical investigators. Dr. Shen has a long track record of NIH funding, expertise in evaluating heterogeneity of treatment effect in cardiovascular diseases, and has mentored multiple prior trainees. Research: The optimal therapy for a given individual with aortic stenosis remains uncertain. This proposal seeks to define the clinical and biologic components of frailty that modify risk after aortic valve replacement (AVR) and alter treatment benefit.
For Aims 1 -2, we will leverage the unique linkage of Medicare data to the US CoreValve Pivotal trials, a collection of clinical trials that randomized individuals with severe aortic stenosis to transcatheter AVR (TAVR) with a self-expanding bioprosthesis vs. surgical AVR (SAVR).
In Aim 1, we will we will identify which variables, related to in-person assessments of frailty and candidate frailty codes, best predict adverse outcomes after AVR.
In Aim 2, we will identify if novel variables identified in Aim 1, identify a heterogeneous treatment response in individuals undergoing AVR.
In Aim 3, we will prospectively enroll patients undergoing TAVR at BIDMC with concurrent frailty phenotyping to identify whether metabolites associated with longevity correlate with frailty and predict adverse outcomes, independent of age and comorbidities. The research will identify the mechanisms by which frailty confers adverse risk and differential treatment benefit in AVR, providing insights that may personalize treatment selection and improve patient care.
While new technologies have permitted the use of transcatheter aortic valve replacement (TAVR) as an alternative to surgical aortic valve replacement (SAVR) for patients with severe aortic stenosis, which is fatal without treatment, deciding on the optimal treatment for a given patient is often difficult, especially as these decisions are often made towards the end of life and there are significant risks concomitant with each treatment. In this setting, frailty, a state of debility that impairs recovery from illness, has emerged as a significant risk factor for adverse outcomes but the mechanisms by which frailty confers risk and the components of frailty that are most important are unknown. In this proposal, we seek to define the clinical and biological components of frailty that contribute risk in aortic valve replacement using a combination of novel data sources and methodologic techniques, including linkage of clinical trials to administrative data, machine learning, and metabolite profiling, in order to personalize treatments and optimize patient health and medical decision making.