A new algorithm has been developed that can alert physicians to patients in the earliest stages of chronic kidney disease by scouring their electronic health records.
Chronic kidney disease is often undetected until it causes irreversible damage. Now, its diagnosis could become fully automated thanks to a new algorithm that has been developed by researchers at Columbia University Vagelos College of Physicians and Surgeons, which automatically searches through a patient’s electronic health records for results of blood and urine tests.
Using a mix of established equations and machine learning to process the data, the algorithm is able alert physicians to patients in the earliest stages of the disease.
A study of the algorithm has been published in the journal npj Digital Medicine.
Utilising algorithms for diagnosis
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.
“Identifying kidney disease early is of paramount importance because we have treatments that can slow disease progression before the damage becomes irreversible,” says 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-recognised and undertreated.
“The interpretation of these tests is not always straightforward. Many patient characteristics, including age, sex, body mass, or nutritional status, need to be considered, and this is frequently underappreciated by primary care physicians.”
The new algorithm tackles these problems by automatically scanning electronic medical records for test results, performing the calculations that indicate kidney function and damage, staging the patient’s disease, and alerting physicians to the trouble – performing nearly as well as experienced nephrologists.
When tested using electronic health records from 451 patients, the algorithm correctly diagnosed kidney disease in 95% of the kidney patients that were identified by two experienced nephrologists, and correctly ruled out kidney disease in 97% of the healthy controls.
The algorithm can be used on different types of electronic health record systems, including those with millions of patients, and could easily be incorporated into a clinical decision support system.
“Our analysis also confirmed that a mild degree of kidney dysfunction is often present in blood relatives of patients with kidney disease,” says 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.”
Because the algorithm empowers genetic analyses of millions of people to discover new kidney genes, Kiryluk says, the algorithm could be used in the future to better understand the inherited risk of chronic kidney disease.