Chronic kidney disease often goes undetected until it causes irreversible damage, but a new machine learning algorithm may be able to help clinicians with an earlier diagnosis of the disease.

That’s according to study published in npj Digital Medicine which automatically scours a patient’s EHR for results of blood and urine tests and uses a mix of established equations and machine learning to process the data.

“Identifying kidney disease early is of paramount importance because we have treatments that can slow disease progression before the damage becomes irreversible,” explained study leader Krzysztof Kiryluk, MD, associate professor of medicine at Columbia University Vagelos College of Physicians and Surgeons.

“Chronic kidney disease can cause multiple serious problems, including heart disease, anemia, or bone disease, and can lead to an early death, but its early stages are frequently under-recognized and undertreated.”

According to the researchers, approximately one in every eight American adults is believed to have chronic kidney disease, which represents one of the most expensive health problems in developed countries. Only ten percent of people in the disease’s early stages are aware of their condition, however, and among those who already have severely reduced kidney function, just 40 percent are aware of their diagnosis.

One reason for the under-diagnosis, the team said, is that people in the early stages of chronic kidney disease usually have no symptoms, leading primary care physicians to prioritize more immediate patient complaints.

Moreover, two tests are needed to detect asymptomatic kidney disease: One that measures a kidney-filtered metabolite in blood, and another that measures leakage of protein in urine.

“The interpretation of these tests is not always straightforward,” Kiryluk said. “Many patient characteristics, including age, sex, body mass, or nutritional status, need to be considered, and this is frequently under-appreciated by primary care physicians.” 

In the study, the team found that the algorithm performs nearly as well as experienced nephrologists. When testing the model using EHRs from 451 patients, researchers saw that the algorithm correctly diagnosed kidney disease in 95 percent of the kidney patients identified by two experienced nephrologists.

The tool also correctly ruled out kidney disease in 97 percent of the healthy controls.

The team has already applied the model to a database of millions of Columbia patients to find previously unrecognized associations between chronic kidney disease and other conditions. For example, depression, alcohol abuse, and other psychiatric conditions were considerably more common among patients with mild kidney disease compared to patients with normal kidney function.

“Our analysis also confirmed that a mild degree of kidney dysfunction is often present in blood relatives of patients with kidney disease,” said Ning Shang, PhD, associate research scientist in the Kiryluk lab and the lead author of the paper. “These findings support strong genetic determination of kidney disease, even in its mildest form.”