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Updated clinical tool assesses risk of IgA nephropathy progression over time

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In an important advance for precision medicine, researchers have refined a tool used to predict which people living with IgA nephropathy are likely to experience disease progression. The original prediction tool is used at the time when patients first receive their diagnosis, but this second version can be used at the one- or two-year mark after diagnosis to better inform ongoing treatment decisions.  

IgA nephropathy is a form of kidney disease that’s caused when an antibody called immunoglobulin A (IgA) builds up in the kidneys, causing damage. In progressive cases of the disease, the best form of treatment is immunosuppressant medications, which lower immune activity and thus damage to the kidneys. However, these medications involve adverse side effects and are only prescribed when the risk of kidney disease progression is high.

“This has always been a vexing problem – how do you identify which patients are going to progress and which ones aren’t?” says Sean Barbour, an Assistant Professor at the University of British Columbia’s Division of Nephrology who specializes in IgA nephropathy research and is chair of the BC Renal provincial Glomerulonephritis (GN) Committee. He emphasizes that clinicians do not want to prescribe immunosuppressants to patients at low risk of disease progression, who may just need less aggressive therapy such as blood pressure control and maybe even sodium-glucose co-transporter (SGLT) inhibitors.

To address this issue, Barbour, in partnership with the International IgA Nephropathy Network, developed the IgA Prediction Tool, which clinicians can use at the time of their patients’ diagnosis to identify who is at low and high risk of disease progression. In the researchers’ more recent study published in Kidney International last April, they developed and validated a second version of the tool that can be used to re-assess patients at later points in their disease, for example, after patients have had some form of treatment. 

“The model did a very good job of predicting accurately outcome events that went beyond the one- or two-year landmark time after biopsy,” Barbour notes. “The one thing we can’t do is use the prediction tool to decide who will respond to treatment or not, but we can use it to decide who is at high risk of disease progression.”

Next, his team plans to update a version of the prediction tool that was developed for children with IgA nephropathy, which could similarly be used to re-assess children in the years following their initial diagnosis.



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