Clinical Decision Support Tool Automates CKD Risk Assessment in Primary Care

Delayed recognition can affect decisions about testing, blood pressure control, cardiovascular risk reduction, and specialist referral. At the same time, primary care teams need tools that fit into existing workflows without adding substantial burden. To support earlier and more consistent management, researchers have developed a clinical decision support tool for CKD detection and treatment at the point of care.

The tool, called Treat CKD, was developed and validated by investigators at the University of California, San Francisco (UCSF). It is integrated with electronic health record (EHR) data and is designed to identify CKD during routine clinical care. The system classifies risk using Kidney Disease: Improving Global Outcomes criteria.

Treat CKD also calculates kidney failure risk using the Kidney Failure Risk Equation. It estimates cardiovascular risk using American Heart Association PREVENT equations. Based on these assessments, the tool provides recommendations for albuminuria testing, blood pressure management, cholesterol-lowering therapy, cardio-kidney therapies, and nephrology referral.

When incorporated into the University of California, San Francisco’s system, primary care health care professionals described the tool as easy to use and clinically relevant. They also reported that it was well integrated into workflows without adding substantial workflow burden. Validation showed that Treat CKD accurately confirmed Kidney Disease: Improving Global Outcomes–based CKD risk classification and was fully concordant with manually calculated Kidney Failure Risk Equation and PREVENT risk scores.

The work was presented at ASN Kidney Week 2026, held October 22–25. The findings support further evaluation of Treat CKD as a scalable approach for CKD detection and treatment in primary care. The tool’s reported performance focused on risk classification, kidney failure risk estimation, cardiovascular risk estimation, and guideline-concordant treatment recommendations.

“Treat CKD accurately automates CKD identification and risk classification, estimates risk of cardiovascular events and progression to kidney failure, and provides guideline-concordant treatment recommendations for primary care, while being perceived by clinicians as easy to use, acceptable and clinically appropriate for routine use in primary care, supporting its further evaluation as a scalable CKD detection and treatment strategy,” said Ivan Villasenor, an M.D. candidate at the University of California San Francisco, School of Medicine.

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