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Overview

CKD poses a global health issue²compounded by specialist shortage³

Several studies indicate that CKD patients are referred to specialized nephrology care just before the need for dialysis, which is often too late.^4,5 Once late-stage CKD is reached, few opportunities remain to delay further progression and avoid complications.⁶ On top, treating people with kidney diseases, particularly those that reach end-stage kidney disease, imposes a heavy financial burden on healthcare budgets.⁷

High CKD prevalence

CKD prevalence is estimated at 700 million globally.^8,9

CKD diagnosis often happens late

As many as 9 out of 10 adults with CKD are unaware of their condition.⁸

High financial burden

The number of people receiving dialysis globally exceeds 2.5 million and is projected to double to 5.4 million by 2030⁸, which will subsequently place a burden on health-care costs.¹⁰

Kidney KFRE Algorithm enables personalized and quality care for CKD patients

Kidney KFRE Algorithm is a risk score predicting the likelihood of an individual patient to reach end-stage kidney disease within 2 and 5 years. It is applied to diagnosed patients with chronic kidney disease (CKD) in stages G3 to G5. Kidney Failure Risk Equation (KFRE) has been validated in >1 million patients in >30 countries worldwide.¹¹

Simplicity to calculate risk score

Kidney KFRE Algorithm relies on a minimum of only 4 standard lab parameters and demographics (Age, Sex, eGFR, ACR).¹

Focus on right patient at right time

Kidney KFRE Algorithm personalizes patient management so that a patient is seen by the specialists at the right time and in line with their progression risk.

In line with CKD guidelines

KDIGOs global Clinical Practice Guidelines recommend that for all people with CKD Stages G3-G5 an externally validated risk equation is used to estimate the absolute risk of kidney failure.¹¹

Two computer screens showing a user interface for calculating and displaying kidney failure risk scores.

The Kidney Failure Risk Equation (KFRE) has changed the management of CKD from an eGFR-based paradigm to a risk-based paradigm. The Roche Kidney KFRE Algorithm enables physicians to seamlessly integrate the tool into their workflow, improving patient care delivery and adherence to KDIGO clinical practice guidelines.
Navdeep Tangri, MD, PhD, FRCP(C) | Attending Physician and Professor of Nephrology at the University of Manitoba; Scientific Director of the Chronic Disease Innovation Center at Seven Oaks Hospital, Winnipeg. Professor Tangri has developed and validated the Kidney Failure Risk Equation.

Image of Navdeep Tangri, MD, PhD, FRCP(C) | Attending Physician and Professor of Nephrology at the University of Manitoba; Scientific Director of the Chronic Disease Innovation Center at Seven Oaks Hospital, Winnipeg.

Interpreting the KFRE Score

KDIGOs global Clinical Practice Guidelines recommend that for all people with CKD Stages G3-G5 an externally validated risk equation is used to estimate the absolute risk of kidney failure.¹¹

3-5%risk of 5-year kidney failure

Can be used to determine need for nephrology referral in addition to criteria based on estimated glomerular filtration (eGFR) or urine albumin-to-creatinine ratio (ACR) and other clinical considerations.¹¹

>10%risk of 2-year kidney failure

Can be used to determine the timing of multidisciplinary care, in addition to eGFR-based criteria and other clinical considerations.¹¹

>40%risk of 2-year kidney failure

Can be used to determine timing of modality education and timing of preparation for kidney replacement therapy (including vascular access planning or referral for kidney transplantation), in addition to eGFR-based criteria and other clinical considerations.¹¹

Benefits

Key advantages of Kidney KFRE Algorithm

Kidney KFRE Algorithm offers several significant benefits that enhance patient care and clinical decision-making. Below are four key advantages that demonstrate how this tool supports referral decisions, individualizes patient risk assessments, identifies fast progressors and facilitates clear communication of risk, ultimately improving patient care.

Assistant for referral decisions

Reduced unnecessary referrals while helping to ensure high-risk cases reach specialists.^11-14

From population to individual patient

Individualized risk scores aid nephrologists to decide personalized intervention.^12,15,16

Identification of fast progressors

On time identification of fast progressing patients can aid timely preparation of kidney transplant and dialysis initiation.^11,12

Clear communication of risk

Potentially improved patient adherence by enabling clinicians to convey disease risk clearly.^3,17

Teaser image of navify Algorithm Suite brochure.

Kidney KFRE Algorithm seamless workflow integration via navify Algorithm Suite

Kidney KFRE Algorithm integrates directly into the current workflow. navify Algorithm Suite is a digital cloud-based platform that onboards clinical algorithms.

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Image of a document: Kidney Failure Risk Equation (KFRE) in KDIGO guidelines.

In accordance with KDIGO 2024 Clinical Practice Guidelines

Since 2024, KDIGOs global Clinical Practice Guidelines recommend that for all people with CKD Stages G3-G5 an externally validated risk equation is used to estimate the absolute risk of kidney failure.¹¹

Additionally, Kidney Failure Risk Equation (KFRE) is listed as the most externally validated risk equation for predicting kidney failure in the general (CKD G3–G5) population.¹¹

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Integration

Integration via navify Algorithm Suite

Experience a single integrated platform designed for healthcare providers and laboratories that simplifies IT complexity while reducing the risk of vulnerabilities. With integrated Roche and partner medical algorithms, we provide a comprehensive solution that streamlines processes and enhances collaboration.

Flow chart illustrating the integration via navify Algorithm Hub, connecting patients, healthcare providers and laboratory systems.

A laptop screen displaying a centralized customer suppor user interface.

Single point of contact for customer support

Roche offers centralized customer support for all algorithms in our portfolio to ensure consistency and reliability. We manage issues that arise for all hosted algorithms, streamlining the process and eliminating the need for customers to engage with individual providers.

Security and data privacy

Security and data privacy are central to Roche's operations, founded on a "Security and Privacy by Design" philosophy and ISO/IEC 27001 certification. Our dedicated technical team performs ongoing risk assessments, penetration tests, and network monitoring to minimize IT complexity and vulnerabilities, prioritizing data confidentiality to protect patient information across all partners.

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FAQs

Frequently asked questions

If you don’t find answers to your questions here, we’re happy to provide more information and discuss your needs in detail.

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For whom is the Kidney KFRE Algorithm intended?

Kidney KFRE Algorithm is a quantitative risk score predicting the likelihood of an individual patient to reach end stage kidney disease within 2 and 5 years. This is a publicly available regression based risk score. It can be applied to diagnosed patients with chronic kidney disease (CKD) in stages G3 to G5.

The risk score is intended to be reviewed by qualified physicians in a clinical setting for prognosis purposes to make informed decisions on patient management.

The Kidney KFRE Algorithm score is not intended to be used as the primary diagnosis. The KFRE score results shall be used in conjunction with the review of the basis by a qualified physician in the context of the patient's medical history and other diagnostic test results.¹

What clinical evidence and studies exist that evaluated and discussed the value of the Kidney Failure Risk Equation?

Kidney Failure Risk Equation (KFRE) is an established CKD risk score with validated performance in 30+ countries and >1 million patients.¹¹ KFRE was initially developed in 2011 using data from 2 independent Canadian cohorts of patients with CKD stages G3-G5¹⁸ and was subsequently validated in 2016 using data from more than 30 countries spanning 4 continents.¹⁹ Since then, evidence from across the globe has been growing for the use of the KFRE in different patient populations and settings.^12-14,20,21

KDIGOs global Clinical Practice Guidelines recommend that for all people with CKD Stages G3-G5 externally validated risk equation is used to estimate the absolute risk of kidney failure.¹¹

On top, UK NICE guidelines recommend using the 4-variable KFRE equation in CKD patients as one of the nephrology-referral criteria.²²

How does Kidney KFRE Algorithm benefit my practice?

The Kidney Failure Risk Equation (KFRE) has changed the management of CKD from an eGFR-based paradigm to a risk-based paradigm. It aims to support physicians in decision-making to send the patient at the right time to the nephrologist,^12–14 to manage patients based on absolute risk level of end-stage renal disease^13,20,23 and, last but not least, to communicate recommended actions, aiming for higher adherence.¹⁷

Can Kidney KFRE Algorithm be integrated with existing laboratory information systems (LIS) and/or electronic health record (EHR) systems?

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References and notes

KFRE Intended Use. 2024. Material number 09891986001.
International Society of Nephrology. New global kidney health report sheds light on current capacity around the world to deliver kidney care [Internet; published 2023 Mar 30; cited 2023 Sep]. Available from: https://www.theisn.org/blog/2023/03/30/new-global-kidney-health-report-sheds-light-on-current-capacity-around-the-world-to-deliver-kidney-care/.
Smekal MD, et al. Patient and provider experience and perspectives of a risk-based approach to multidisciplinary chronic kidney disease care: a mixed methods study. BMC Nephrol. 2019 Mar;20(1):110. DOI:10.1186/s12882-019-1269-2.
Nissenson, A. R., Collins, A. J., Hurley, J., Petersen, H., Pereira, B. J., & Steinberg, E. P. (2001). Opportunities for improving the care of patients with chronic renal insufficiency: current practice patterns. Journal of the American Society of Nephrology, 12(8), 1713-1720.
Cass, A., Cunningham, J., Snelling, P., & Ayanian, J. Z. (2003). Late referral to a nephrologist reduces access to renal transplantation. American Journal of Kidney Diseases, 42(5), 1043-1049.
Fishbane S, et al. Challenges and opportunities in late-stage chronic kidney disease. Clin Kidney J 2015;8:54–60. DOI:10.1093/ckj/sfu128.
Francis A, et al. Chronic kidney disease and the global public health agenda: an international consensus. Nature Reviews Nephrology 20. 2024; 473–485. https://doi.org/10.1038/s41581-024-00820-6
GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet (London, England). 2020; 395(10225), 709–733. https://doi.org/10.1016/S0140-6736(20)30045-3
Shlipak MG, et al. The case for early identification and intervention of chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney international. 2021;99(1), 34–47. https://doi.org/10.1016/j.kint.2020.10.012
Wouters OJ, et al. Early chronic kidney disease: diagnosis, management and models of care. Nat Rev Nephrol. 2015 Aug;11(8):491-502. DOI:10.1038/nrneph.2015.85.
Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 2024 Apr;105(4S):S117-S314. DOI:10.1016/j.kint.2023.10.018.
Wojciechowski P, et al. Risk Prediction in CKD: The rational alignment of health care resources in CKD 4/5 care. Adv Chronic Kidney Dis. 2016 Jul;23(4):227-230. DOI:10.1053/j.ackd.2016.04.002.
Major RW, et al. The kidney failure risk equation for prediction of end stage renal disease in UK primary care: an external validation and clinical impact projection cohort study. PLoS Med. 2019 Nov;16(11):e1002955. DOI:10.1371/journal.pmed.1002955.
Duggal V, et al. Nephrology referral based on laboratory values, kidney failure risk, or both: a study using veterans affairs health system data. Am J Kidney Dis. 2022 Mar;79(3):347-353. DOI:10.1053/j.ajkd.2021.06.028.
Reaven NL, et al. Association of the kidney failure risk equation with high health care costs. Kidney Int Rep. 2023 Mar;8(6):1183-1191.
Prasad B, et al. Kidney failure risk equation and cost of care in patients with chronic kidney disease. Clin J Am Soc Nephrol. 2022 Jan;17(1):17-26. DOI:10.2215/CJN.06770521.
Sparkes D, et al. Patient perspectives on integrating risk prediction into kidney care: opinion piece. Can J Kidney Health Dis. 2022 Mar;9:20543581221084522. DOI:10.1177/20543581221084522.
Tangri N, et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA. 2011;305:1553–1559. DOI:10.1001/jama.2011.451.
Tangri N, et al. Multinational assessment of accuracy of equations for predicting risk of kidney failure: a metaanalysis. JAMA. 2016 Jan;315:164–174. DOI:10.1001/jama.2015.18202.
Ali I, et al. A validation study of the kidney failure risk equation in advanced chronic kidney disease according to disease aetiology with evaluation of discrimination, calibration and clinical utility. BMC Nephrol. 2021 May;22(1):194. DOI:10.1186/s12882-021-02402-1.
Hingwala J, et al. Risk-based triage for nephrology referrals using the kidney failure risk equation. Can J Kidney Health Dis. 2017 Aug;4:2054358117722782. DOI:10.1177/2054358117722782.
National Institute for Health and Care Excellence. Chronic kidney disease assessment and management, 2021. [Internet; updated 2021 Nov 24; cited 2025 Mar]. Available from: https://www.nice.org.uk/guidance/ng20
Potok OA, et al. Patients,’ nephrologists,’ and predicted estimations of ESKD risk compared with 2-year incidence of ESKD. CLin J Am Soc Nephrol. 2019 Feb;14(2):206-212. DOI:10.2215/CJN.07970718.

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