Development of a Primary Care Cardio-Kidney Risk Navigator for Clinical Decision Support

Area of Science:

• Nephrology
• Cardiology
• Primary Care Medicine

Background:

• Chronic kidney disease (CKD) and cardiovascular disease (CVD) share a significant, interconnected health burden, with each condition exacerbating the other.
• Despite available diagnostic tools and risk assessment methods, CKD screening is inconsistent, and kidney health is under-integrated into CVD risk stratification.
• Barriers at systemic, clinical, and patient levels hinder the adoption of guideline-recommended CKD screening.

Purpose of the Study:

• To develop a consensus-based, practical algorithm for integrating CKD screening into cardiovascular disease (CVD) risk management.
• To address inconsistencies in CKD screening and improve the integration of kidney measures into CVD risk assessment.

Main Methods:

• A structured Delphi process involving a multidisciplinary expert panel was employed to develop an evidence-informed screening algorithm.
• Iterative surveys, landscape assessments, and roundtable discussions were used to identify screening components, refine the algorithm, and assess clinical workflow feasibility.
• Consensus on the algorithm's structure, content, and applicability was achieved through structured group discussions and survey-based validation.

Main Results:

• The expert panel identified optimal triggers for CKD screening and key gaps in current clinical practices.
• The developed algorithm offers a streamlined, implementation-focused approach for consistent and earlier identification of patients at risk for CKD and CVD.
• Consensus was reached on the algorithm's structure, content, and its applicability across various clinical settings.

Conclusions:

• The Primary Care Cardio-Kidney Risk Navigator algorithm provides a practical framework for integrating CKD screening into CVD risk management within primary care.
• This tool operationalizes existing guidelines into a unified, workflow-oriented approach to support consistent implementation and earlier patient identification.
• The algorithm facilitates timely referral and prevention strategies for at-risk populations and is adaptable for EHR integration or use as a standalone decision support tool.