TECHNOLOGY

A new formula could help black patients take care of their kidneys


Doctors for decades And the hospital looked at kidney patients differently in their race. An ideal equation for estimating kidney function applies a correction for black patients that makes their health brighter, preventing access to transplants and other treatments.

On Thursday, a task force convened by the two leading kidney care societies said the practice was unfair and should be stopped.

The group, in collaboration with the National Kidney Foundation and the American Society of Nephrology, has recommended the use of a new formula that is not a factor in patient running. In a statement, Paul Palevsky, president of the foundation, “called on all laboratories and healthcare systems across the country to adopt this new approach as soon as possible.” That call is significant because the recommendations and guidelines of professional medical societies play a powerful role in caring for patients.

A study published in a study of 5,000 people in Massachusetts in 2020 found that one-third of black patients would classify their disease as more severe if evaluated using the same version of the formula as white patients. Kidney Therapeutic Kidney Calculation was a class of medical algorithms and calculators that have recently come under fire for the care of ethnic patients, not a social biological class.

A review published last year listed more than a dozen tools in areas such as cardiology and cancer care. It helped raise awareness against the practice of various groups, including medical students and lawmakers, including Senator Elizabeth Warren (D-Massachusetts) and President of the House Ways and Means Committee, Representative Richard Neal (D-Massachusetts).

There are signs of a recent tidal wave. The University of Washington discontinued the use of race in kidney counts after a revision of the practice following student protests last year. Mass General Brigham and Vanderbilt Hospitals also abandoned the practice in 2020.

In May, a tool used to predict that a woman who previously had a caesarean section could safely deliver through a vaginal delivery was updated so that lower scores could not be automatically assigned to black and Hispanic women. A calculator that estimates the child’s chances of urinary tract infection was updated to reduce the score of blackened patients.

The previous formula for diagnosing kidney disease is known as CKD-EPI and was introduced in 2009, updating the formula 1 which was used by the same race. It converts the levels of a product called creatinine in a person’s blood into a measure of the overall functioning of the kidneys called the approximate glomerular filtration rate or EGFR. Doctors use EGFR to help classify the severity of an individual’s illness and determine if they are eligible for a variety of treatments, including replacement. Healthy kidneys produce higher scores.

The design of the equation based on a person’s age and gender however increased the score of any patient classified as Black by 15.9 percent. This feature was included for the statistical patterns seen in the patient data used to inform the design of the CKD-EPI, which had a relatively small population compared to blacks or other ethnic minorities. But it does mean that a person’s perceived race can change how their disease is measured or treated. Individuals with a history of both black and white, for example, may reverse the classification of a health system for their illness depending on how their doctor sees them or identifies them.

Nawamaka Enia, an assistant professor at the University of Pennsylvania and a member of the task force behind Thursday’s recommendation, said she knew a semen patient with severe kidney disease who, after learning how the equation worked, asked him to whitewash her to be enrolled in advanced care. Enya says it is too late to move away from the established equation. “It’s completely wrong to use someone’s skin color to show someone’s clinical path – when you do, you show racial bias in treatment,” he said.



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