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STAT Wunderkind James Diao on race and clinical algorithms

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STAT Wunderkind James Diao on race and clinical algorithms

When the killing of George Floyd reignited calls for racial equality in the US in 2020, medicine was confronted with its own thorny questions about race. James Diao, then a medical student at Harvard Medical School, was one of many people who focused on one specific topic: If race is a social construct, why was it a factor in clinical tools used to determine a patient’s disease risk?

“These big questions were actually not just scientific problems, but also human and moral problems about the way values ​​are integrated into these apparently dispassionate tools that we use,” says Diao, whose articles on the impact of race and its removal from clinical calculators have been analyzed. has since played a role in policy decisions that affect millions of patients. “I became really, really obsessed with the idea of ​​what kind of assumptions we have baked into these calculators.”

As the health care system continues to grapple with the role of race in many other clinical tools, Diao’s work on this topic has modeled how to balance a quantitative approach to the problem with the perspectives of patients, advocates, and policymakers. Now a resident of Brigham and Women’s Hospital and one of STAT’s 2024 Wunderkinds, Diao’s career has been shaped by his willingness to listen.

“The more I’ve learned, the more I realize there’s more to learn,” he said.

The search for a better way to calculate kidney function

When the pandemic broke out, Diao had been working with Arjun Manrai, an assistant professor of biomedical informatics at Harvard Medical School, since he was a sophomore studying statistics and biochemistry. Manrai, who recruited Diao when he started his medical training at Harvard, studies how clinical algorithms work – and fail – when applied to different populations. While the two were cooped up at home, they began to look into the different ways hospitals at the time were tweaking their kidney function calculators, known as eGFR, to exclude race.

They zoomed in. A lot of. “I think there was a period where we were Zooming every day,” Manrai said. Diao enlisted his partner Gloria Wu, a public health researcher, in the work. (In August, Manrai officiated their wedding, where the couple showed off their ballroom dancing skills.)

Diao did not stay alone in his bubble. On Twitter, as heated debates arose about the harms of both keeping and excluding race from the kidney calculator, “I was a fly on the wall,” Diao said. ‘I’m grateful I didn’t say anything at the time. I still had so much to learn.”

To keep up, he read. “Fatal Invention,” Dorothy Roberts’ book about the fallacy of race as a biological category, was at the top of his Covid reading list. He spoke to the medical students who were advocating for changes to the race-based renal calculator in their hospitals, and attended meetings of the grassroots Institute for Healing and Justice in Medicine with other medical students who were beginning to question the clinical equations they were being presented with. learned.

As he continued his research, Diao had to carefully weigh empirical and moral arguments for including race in clinical decisions. “He had a very difficult job,” says Rohan Khazanchi, then a medical student studying health care and health equity. Diao was young and a relative outsider. He worked with teams of physicians and researchers who have been studying – and using – clinical algorithms for years.

“You have to keep the focus on the data, so that different people can use that data to inform policy decisions without feeling like you have your thumb on the scale,” says Diao. “But on the other hand, if you go too far into that, people think you don’t care about the underlying problems.”

At the end of 2020, Diao was the first author of a study in the Journal of the American Medical Association which quantified the impact on black patients if medicine were to remove race directly from the existing eGFR calculator, without changing it in any other way. Removing race would be more likely to increase access to kidney care, including transplants, he wrote with Wu, Manrai and others. But it would also prevent some patients’ access to chemotherapy or drugs with doses based on kidney function — a finding that some interpreted as a case against removing race from the calculator.

“It was very stressful having to defend ourselves,” Diao said. But when the National Kidney Foundation and the American Society of Nephrology convened a task force to reassess the role of race in eGFR, Diao was able to present his case and testify about the study, along with other medical students who had advocated for the removal of race from the eGFR. the comparisons of their hospitals.

“He mentioned some numbers,” Khazanchi recalled, “but he also talked about the challenges of talking to an individual patient and saying, ‘By the way, I’m using your race to determine the stage of your kidney disease.’ ”

The following year, Diao and others published a perspective about the New England Journal of Medicine’s quest for a better kidney function equation that went beyond simply removing race from the existing eGFR calculator.

They wanted to move beyond the “false dichotomy” that pitted these approaches against each other, said Manrai, co-author of the paper. “There are many other race-free equations, and many other ways to change this equation to remove stratification by race, that are more accurate and have different strengths and weaknesses.” Months later, the task force issued its recommendation in support of two of the race-free approaches highlighted in that document, citing them directly. In practice, that decision has led to thousands of black patients adjusting their wait time on kidney transplant lists.

James Diao

Integrating patient perspectives into clinical algorithms

Race meant one thing where Diao grew up, in the suburbs of diverse Houston, where his parents worked as engineers for oil companies. But when he visited his father’s hometown in China and passed around his school yearbook, he remembers that many people thought every dark-skinned student was of African descent.

In an effort to expand his global perspective on race, he went to the other Cambridge across the pond in 2022 to study health policy and expand his global perspective on race. “He really listens to a lot of people with different perspectives and actively seeks them out,” says Manrai. “He doesn’t just look at it from one community, from one angle. He really listens to a lot of people.”

Meanwhile, Diao continued his work on another race-based clinical algorithm. The American Thoracic Society, spurred by the racial reckoning of 2020, had begun to rethink its approach to lung function testing, which had long assumed that Black patients had lower baseline lung volumes than white patients.

Diao’s approach was similar to his work on the kidney function calculator, said Khazanchi, who worked on the project: “For all patients, what are the positive and negative implications of shifting from a race-based lung function testing algorithm to a non- racial algorithm? based algorithm?”

By the time Diao published another first article in the New England Journal of Medicine this year, the ATS had made up its mind: it would moving away from race-based pulmonary function testing. But the study provided crucial context as health care systems figured out how to implement that recommendation.

Moving away from race-based tools would likely increase Black patients’ eligibility for workers’ compensation benefits. With more accurate estimates of lung function, they could gain access to treatments and therapies. But better diagnosis could lead to more invasive, potentially risky interventions, or even take certain surgeries off the table. After the study was published, the Department of Veterans Affairs launched a study on the impact of the change on disability benefits, anticipating a smaller effect than the study predicted.

Thanks to his work, Diao recently became the 22nd student in Harvard Medical School history to graduate summa cum laude. With Khazanchi, he dives straight into a residency in Boston, where he is drawn to cardiology.

“Something clicked” about the specialty, Diao said. He loves the high stakes that come with helping a patient having a heart attack. But cardiology is also a particularly data-driven specialty – a place where he could see his computer science and statistics making an impact. “The field is developing very, very quickly and using all this data that is being collected for the benefit of the patient,” Diao said. “This is something I can really contribute to and be a part of.”

For now, he’s focusing on helping patients prevent cardiovascular disease. In one of his latest publications in JAMA, Diao projected the impact on statin eligibility with the likely adoption of another new race-free tool, which was used to predict the likelihood of strokes and heart attacks. That new calculator, PREVENT, was built by the American Heart Association as a major overhaul of the previous tool, aiming to make it more accurate by incorporating patients’ BMI and kidney function.

It also, in a first for a U.S. tool of this size, seeks to refine its predictions by integrating the social determinants of a patient’s health, using zip code to estimate factors such as income, education and housing status. Diao played with the new tool and tried different zip codes in his hospital. “Boston has some of the largest disparities in life expectancy among the neighborhoods of any city in the U.S.,” he said. By plugging in different zip codes in the area, “you get these really dramatic differences in the forecast.”

As the AHA considers whether and how to incorporate PREVENT into its clinical guidelines, the question is whether these zip code-based predictions are more accurate – “and if so, how do patients feel about that?” Diao said. If patients are uncomfortable with their race being used to determine their risk for disease, how would they feel about their estimated income being included?

In a study he is now working on, Diao and his colleagues “just ask people,” he said: “Ask people what they feel comfortable with having it used under their care, and when they feel comfortable having it under their care.” their care is used.”

He’s still listening.

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