MDCalc Statement on Race

MDCalc Statement on Race in Medical Calculators and Risk Estimates

Every segment of our society is taking a hard look at race and racism, and medicine is no exception. Just like all institutions, there are numerous examples of racism in medicine in the past and today, and we must continue to challenge, question, and change them.

As the world's most frequently-used medical calculator resource, we too have investigated where MDCalc intersects with race, especially after the publication of Vyas et al's piece in the New England Journal of Medicine, Hidden in Plain Sight. Our goal for MDCalc will always be to provide the best data and evidence to help medical providers care for patients. Above all else, supporting excellent patient care is at the center of MDCalc.

Easy it is to have our True North be excellent patient care,yet hard it is to know the "right" way to handle calculators that include inputs based on race.

Our Complicated Concerns over "Race"

• It is very obvious that "race" is a terrible surrogate for what we in medicine often care about: one's unique genetic makeup, and how that code impacts diseases we may or may not be at risk for, and even what medicines may or may not treat our bodies most effectively.
• It is difficult to determine why race ends up being a risk factor. Is it due to socioeconomic risk? A surrogate for geography? Access to healthcare? True genetic difference? Some may try to argue that the reason is inconsequential, so long as the correlation exists. But when we ask the clinician to apply these blunt instrument tools to the unique individual in front of them, it becomes crucially important that they understand the reasoning: how do my patients race, geography, and socioeconomic background interact?
• This is the same core issue behind one of the main pitfalls of AI and Big Data, which many developers seem to ignore: when applying the blunt tools of evidence-based medicine to unique patients, correlation in a vacuum is not enough.
• We know that these broad, vague categories of "race" are a poor substitute for the diversity and complexity of human beings around the world. They require us to pigeonhole patients into particular boxes, when life exists so much more on a complex scale. The variability of individuals — and genetics — within a race is enormous.
• We know that the United States, for example, is very racially diverse, yet many studies only use "Black" or "Non-black" as their racial categories. Potential reasons for this over-simplification may be ignorance, racism, study cost, or even simple statistical mathematics of power and effect size; regardless, these studies are hard for physicians to interpret if a patient does not fit into one of those boxes.
• Similarly, "White/Caucasian/European ancestry" is often the "default" option in these studies, which makes sense given that the majority of people in the United States identify as Non-hispanic white." However this "default" raises questions about normalcy as well.
• Importantly, it is hard to know how to handle these situations. Is it wrong, today, using the data currently available, to use race in these models? If we remove the race input from any one of the eGFR calculators, are we damning some patients to a "diseased" state by lowering their eGFR and recommending dialysis earlier than necessary, or properly diagnosing them so they are treated the same? (NephJC has an outstanding analysis on this topic.) Similarly with breast cancer's Gail Model: are there different incidences in breast cancer in women by race? How can we even know the "Truth"?
• Finally, the problem we are highlighting is not unique to race as an input, albeit more emotionally charged. Many inputs discovered through statistical approaches can have unclear causes, and the cause is critical to understand. Do patients in different countries express “shortness of breath” differently? Or, a classic often head-scratching input from the TIMI NSTEMI Score: taking aspirin turns out to be a risk factor for heart attack — because so many patients who take aspirin daily already have risk, or because aspirin "should" be preventing heart attack itself.

Our Recommendations and Approach

Right now, we do not (and we may never) know "The Truth": are there biological differences that can be simplified down to race, or are there not? (Obviously there are small biological differences based on genetics.) We hope that in the future it doesn't matter: we want better studies and better technology to prevail, and allow us to understand patients' genetics, instead of their race.

And yet, we have patients who need our care now.

Because we cannot easily say if including race is helpful or harmful, we have decided to:

• Briefly summarize how race impacts the results of a particular score, so clinicians can be better-informed about the score they’re using.
• Make race an optional input when possible to still provide accurate information, so clinicians may opt in or out from including it, and allow them to make that informed decision (the “art” of medicine).
• Specifically draw attention to race when it exists in a calculator in the "Instructions" sections as well as our "Evidence" content to provide clearer, transparent information for our users.

In the future:

• We hope to see more datasets including more extensive data, especially when something like "race" appears to have a statistically significant impact on a research hypothesis.
• We hope that when race is shown to correlate with an outcome, researchers will not stop, but will dig further.
• We hope that improved awareness and understanding will help us find markers and information that may be even more relevant and useful than the blunt instrument of "race."

When there is ever doubt or uncertainty, above all, decisions should be always driven by what is best for the individual patient.

Sincerely,

- The MDCalc Team