Researchers propose dynamic waitlist for heart transplantation
Continually updated mortality models based on adverse events and end-organ function are better at showing which transplant candidates most urgently require a new heart, according to a study published in the Journal of the American College of Cardiology.
Currently, the United Network for Organ Sharing (UNOS) database is updated infrequently, the authors noted, and hearts are allocated based on “coarse tiers” defined by what treatment patients have received—not necessarily their changing condition.
Another recent JACC study suggested clinicians may “game the waitlist” by overtreating patients to bump them up the pecking order. Indeed, those authors pointed out the proportion of Status 1A candidates has doubled in the last decade and now more than 40 percent of candidates wait at that top designation.
Searching for a solution, Cleveland Clinic researchers studied 414 patients who were listed for heart transplantation at their center from 2008 to 2013. Seventy-seven patients died while on the waitlist. Survival rates for patients who remained on the list were 85 percent at one year and 57 percent at four years.
In building a time-varying mortality model, lead author Eugene H. Blackstone, MD, and colleagues found prelisting characteristics were no longer relevant because of subsequent adverse events and changes in laboratory values. Specifically, they noted:
- Higher serum bilirubin and creatinine values resulted in continuously updated mortality estimates across the waiting period.
- Neurological events, including stroke or intracranial hemorrhage, were associated with 13.5-fold odds of waitlist mortality.
- New requirement for dialysis boosted the odds of waitlist mortality by a factor of 3.67.
- Each episode of respiratory complications increased the chances of mortality by 79 percent.
“We found that baseline characteristics, including year of listing, were not predictive of survival when changes in end-organ function and adverse events while waiting for transplantation were taken into account,” Blackstone et al. wrote. “This finding suggests that the patient’s urgency status for transplantation should depend on objective evidence of deterioration as long as the patient remains eligible for transplantation.”
Reshuffling patient priority in this way could ultimately reduce waitlist mortality and provide a “more objective allocation” of donor hearts, the authors noted.
The study was conducted at a single institution and had a small number of adverse events. Also, the proposed model is informative of mortality risk, but shouldn’t be seen as a definitive transplant decision tool because some of the same events that bumped patients up the rankings could make them unsuitable for an operation.
Finally, national databases might not capture the events and laboratory data with the same frequency as was possible with this single-center study, so multi-institutional data may be necessary before this tool can be more widely used, Blackstone et al. acknowledged.
But in a related editorial, James K. Kirklin, MD, predicted the authors’ use of a dynamic risk model “could provide a new paradigm for allocating organs.” However, additional work needs to be done to interpret the results and apply them to clinical practice, he said.
“The decisions about how best to display the data and analytics will be an evolving process as expert clinicians digest the concepts, explore the details of the statistical methods, and assess their relevance to clinical decisions,” wrote Kirklin, with the University of Alabama at Birmingham. “Blackstone et al. have taken us on an exciting foray into a dynamic risk model, which will certainly assume its place among the most respected and useful methods for predicting patient outcomes.”
Daniel joined TriMed’s Chicago editorial team in 2017 as a Cardiovascular Business writer. He previously worked as a writer for daily newspapers in North Dakota and Indiana.