In the U.S., around 16,000 kidney transplants are performed each year. Art: Mimi Phan
Nearly 100,000 Americans are on the waiting list for a kidney donation. Doctors usually have little time to decide whether to accept or reject a kidney from a recently deceased donor for a patient on the transplant waiting list—and those decisions are often based on experience and intuition, not scientific data. As a result, they are subject to inaccuracy.
An upcoming paper (abstract) co-authored by two MIT Sloan professors shows how the use of an analytics-based decision support application can help surgeons and patients alike decide whether to accept a donor kidney from a deceased donor or wait for a higher-quality organ to become available.
“It’s a question you have to address in a limited amount of time,” said Nikos Trichakis PhD ’11, an assistant professor of operations management at MIT Sloan, who contributed to the paper. He previously focused on the topic of kidney transplantation in his doctoral thesis.
“Some patients are very sick, and if they reject an organ, they might get even sicker. But if they accept a low-quality organ, that has implications for their health as well.”
A fast, intuitive decision In a typical scenario, a surgeon receives a phone call in the middle of the night and finds out that a kidney from a registered organ donor who recently died is being offered to her patient. The surgeon must make a decision whether to accept or decline the offer in as little as an hour, noted the paper's co-author Dimitris Bertsimas SM ’87, PhD ‘88, a professor of operations research at MIT Sloan.
“A surgeon may ask some questions—‘Where is the kidney? How far away is it?’—and learn some aspects of the kidney’s quality, then make an intuitive decision,” said Bertsimas.
The key question is how soon an organ with a higher kidney donor profile index (KDPI) rating might become available to that particular patient. Knowing that would let both the surgeon and the patient make an educated decision about whether to accept or decline the existing offer of a lower-quality kidney.
Predictions from historical data Using a random forest sampling methodology, Bertsimas and Trichakis developed an application that applies a machine learning model to historical data about all kidney transplants that took place over the last 10 years.
The model considers 14 independent factors—ranging from a patient’s blood type to his sensitivity to antibodies to his time on the waitlist so far—to determine the probability of obtaining a higher-quality kidney in the next three, six, or 12 months for a patient’s specific geographic area.
When Bertsimas and Trichakis ran a number of scenarios through the application, predictive models it produced closely matched the observed probability of any given situation from the historical data set. And the predictive modeling will only improve as the data set grows, the authors say.
Decision support tool Improving the success rate of kidney transplants is no small matter. Depending on where they live, those on the transplant waiting list could wait more than five years to receive a new kidney—and the longer they have to wait, the lower the odds of successful recovery.
At the same time, additional research shows that the discard rate for kidneys from deceased donors has risen steadily over the last three decades. Nearly 20 percent of kidneys recovered for transplant are eventually discarded, and the rate is closer to 50 percent for low-quality kidneys.
Bertsimas and Trichakis are working with a team of surgeons at Massachusetts General Hospital to create a support tool that will help them decide whether to accept the current offer of a deceased-donor kidney or wait for another offer.
Read the full article at the MIT Sloan School of Management newsroom.