Kidney Week

Abstract: FR-PO0043

Development of a Cloud-Based mHealth Platform for the Scalable Implementation of the KDIGO CKD Guidelines

Session Information

• Artificial Intelligence: Clinical Prediction, Decision Support, and Digital Kidney Care
  October 23, 2026 | Location: Exhibit Hall A, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Artificial Intelligence, Digital Health, and Data Science

• 300 Artificial Intelligence, Digital Health, and Data Science

Authors

• Bowden, Adam, Saint James's Hospital, Dublin, Ireland

Adam Bowden, MBChB

• Roy, Gargi, Saint James's Hospital, Dublin, Ireland

Gargi Roy, MBBS, MBChB

• Murphy, Mairead M., Saint James's Hospital, Dublin, Ireland

Mairead M. Murphy, RN

• Brennan, Laura, Saint James's Hospital, Dublin, Ireland

Laura Brennan, RD

• Yerushalmy, David, Saint James's Hospital, Dublin, Ireland

David Yerushalmy, MBChB

• Griffin, Brenda, Saint James's Hospital, Dublin, Ireland

Brenda Griffin, MBChB

• Jayawardene, Satish, Saint James's Hospital, Dublin, Ireland

Satish Jayawardene, MBBS

• Kennedy, Claire, Saint James's Hospital, Dublin, Ireland

Claire Kennedy, MBChB

• McAnallen, Susan Marie, Saint James's Hospital, Dublin, Ireland

Susan Marie McAnallen, MBChB

• Sexton, Donal J., Saint James's Hospital, Dublin, Ireland

Donal J. Sexton, MD, MBChB, PhD

Background

The 2024 KDIGO CKD Guidelines represent the international gold standard holistic care for patients with CKD. Global implementation of these guidelines in clinical practice is hampered by manual and ad hoc processes which result in delays and missed opportunities in the commencement of disease modifying treatments.

Methods

We developed a cloud-based platform and accompanying patient smart phone application to aid in more automated implementation of the guidelines in our CKD cohort, which automatically imports updated laboratory results from linked electronic health records (EHR), alongside patient inputted data at home and clinician entered data. We developed dashboards and applied filters aligned with KDIGO guidance, to identify patients eligible for ACEi/ARBs, SGLT2 inhibitors and statins and risk triage using the Kidney Failure Risk Equation (KFRE). Dietary and lifestyle recommendations were facilitated through locally developed and validated educational content on the patient application.

Results

3865 CKD patients have been onboarded to the cloud-based platform, of whom 2052 patients have downloaded the mobile application. Using in-platform filtering, 1048 met KDIGO recommended indications for ACEi/ARB, 1062 for SGLT2i, 1189 for statins. Of 1620 patients with remote monitoring BP value readings inputted, 974 (60%) did not meet the KDIGO recommended BP threshold (</=120 sys). The KFRE was calculated in 721 patients. 891 patients meeting criteria for renal dietetic input were identified through automated digital triage.

Conclusion

Implementation of a cloud-based mHealth platform enabled systematic identification of CKD patients eligible for guideline-directed disease modifying CKD therapies and highlighted gaps in blood pressure control. These findings demonstrate the potential of integrated digital health platforms to support more widespread KDIGO guideline adherence and optimize CKD management. Further work will focus on ensuring appropriate therapy initiation to align care with KDIGO recommendations.

Acknowledgment

Technology partners for this solution were MyPatientSpace LTD

Funding

• Government Support – Non-U.S.