Research to Predict Pre- and Post-Transplant Survival in Heart Failure Lands $2.8M NIH Grant

Will use machine learning and more to gauge survival disparities

The National Institutes of Health has awarded a research team led by Cleveland Clinic cardiologist Eileen Hsich, MD, $2.8 million to examine disparities in survival among heart failure patients before and after heart transplantation. The four-year grant also supports development of new data tools, including machine learning methods, to optimize outcomes in this population.

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“Disparities in survival exist among patients during their time on the waiting list for heart transplant and also following transplantation,” says Dr. Hsich, Associate Medical Director of Cleveland Clinic’s Cardiac Transplant Program and co-principal investigator (PI) on the grant. “The research funded by this award will identify factors contributing to these population differences, with the aim of improving survival and minimizing organ wastage.”

She adds that the goal is to lay the groundwork for ultimate development of “a dynamic and improved way to allocate donor hearts in the future,” although the current grant does not cover work to create an allocation score.

Three interrelated research projects

The grant will support three research projects focused on the following:

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  • Applying machine learning statistical methods to the national Scientific Registry of Transplant Recipients (SRTR) to identify major risk factors and quantify how their interactions affect differences in survival before and after transplant. Among the factors to be assessed are sex, race, type of heart disease, socioeconomic status, presence/absence of mechanical circulatory support and U.S. region.
  • Using data from Cleveland Clinic and four other U.S. medical centers to develop the first method to dynamically update risk of death on the national heart transplant waiting list so that clinicians are alerted to patients’ changing conditions. The researchers will use variables not currently collected by the SRTR — such as tests of sodium, albumin and natriuretic peptides — as well as serial clinical assessments reflecting changes in condition. The four other participating centers are Northwestern University, University of Pennsylvania, University of Pittsburgh and Duke University Medical Center.
  • Using data from the first two projects to develop statistical models that simultaneously estimate risk of waiting list mortality, time to transplantation and risk of post-transplant death. The models will base risk estimates on patient characteristics and health while on the waiting list. The aim will be to improve estimates of optimal timing for transplant as a patient’s condition evolves, along the lines of risk prediction models currently used in lung, liver and kidney transplantation.

Accounting for more variables

“The existing heart allocation system is based on tiers that prioritize patients by risk of death mainly by need for mechanical circulatory support or certain medications, but it doesn’t account for how their condition changes, their likelihood of getting transplanted or their risk of death after transplant,” explains Dr. Hsich, who has published extensively on disparities among heart transplant candidates (see this prior Consult QD story).

Dr. Hsich will collaborate closely with her co-PI, Hemant Ishwaran, PhD, Director of Statistical Methodology at the University of Miami Health System, who formerly worked at Cleveland Clinic. She says the statistical models to be developed will be more sophisticated than the dynamic model currently used for liver allocation and should ultimately be applicable to allocation of donor organs beyond the heart.

“Our approach is innovative because it uses new mathematical approaches and seeks to shift current heart failure research and clinical practice paradigms by taking into account population differences rather than basing decisions solely on ejection fraction, presence of coronary artery disease and stages of disease,” Dr. Hsich observes.