The tool then layers two additional risk models onto that classification. In parallel, it estimates cardiovascular risk with the American Heart Association PREVENT equations, extending the assessment beyond the kidneys to the cardiovascular complications closely tied to kidney disease. Based on these combined calculations, Treat CKD generates specific clinical recommendations.

The system prompts primary care health professionals to consider albuminuria testing, blood pressure management, cholesterol-lowering therapy, cardio-kidney therapies, and nephrology referral, aligning suggested actions with the risk profile calculated for each individual patient. When the tool was incorporated into the University of California, San Francisco's electronic health record system, primary care health professionals who used it described it as easy to use, clinically relevant, and well-integrated into their existing workflows. Notably, the clinicians reported that the system did not add substantial workflow burden, a practical consideration for any decision support tool intended for routine primary care use.

"Treat CKD automates CKD risk classification and delivers guideline-concordant treatment recommendations within primary care workflows," the development team reported in their conference presentation. The investigators described the tool as accurately automating risk classification, estimating the likelihood of both kidney failure progression and cardiovascular events, and supporting treatment decisions without disrupting clinical routines. The reported evaluation reflects the tool's performance within a single health system's electronic health record environment and draws on clinician perceptions of usability and integration.

The authors themselves frame the results as supporting further evaluation of the approach as a scalable CKD detection and treatment strategy, rather than as evidence of the tool's effect on long-term patient outcomes. The presentation took place at ASN Kidney Week 2026, the American Society of Nephrology's annual meeting, which brings together more than 12,000 kidney professionals from across the globe. The conference, held in Denver with early programs beginning October 21 and the annual meeting running October 22–25, serves as a major venue for presenting new research in nephrology, including innovations in clinical care delivery such as embedded decision support systems.

By combining CKD identification, KDIGO risk classification, kidney failure risk prediction, cardiovascular risk estimation, and guideline-based treatment prompts in a single record-embedded system, Treat CKD illustrates how electronic health record infrastructure can be used to automate complex clinical calculations. The next phase of the work, according to the investigators, will focus on further evaluating whether this automated approach can be extended as a scalable strategy for detecting and treating chronic kidney disease in primary care settings.

Subject of Research: Clinical decision support tool for chronic kidney disease detection and treatment in primary care

Article Title: Clinical support tool helps primary care health professionals detect and treat chronic kidney disease

Article References: Clinical support tool helps primary care health professionals detect and treat chronic kidney disease. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: chronic kidney disease, clinical decision support, electronic health records, primary care, KDIGO, Kidney Failure Risk Equation, PREVENT equations, ASN Kidney Week 2026, nephrology, University of California San Francisco, Clinical, support