Expanded question: What are the different kidney failure risk assessment tools, equations, and algorithms?

Answered on September 9, 2024
Several kidney failure risk assessment tools, equations, and algorithms are currently used in clinical practice to predict the risk of progression to end-stage kidney disease (ESKD) in patients with chronic kidney disease (CKD). These tools are essential for guiding clinical decision-making and optimizing patient care.
1. Kidney Failure Risk Equation (KFRE): The KFRE is one of the most widely validated and utilized tools. It uses variables such as age, sex, estimated glomerular filtration rate (eGFR), and albumin-to-creatinine ratio (ACR) to predict the risk of treated kidney failure over 2 to 5 years. The KFRE has been validated in diverse populations across more than 30 countries and is known for its high discrimination and calibration.[1-2]
2. CKD-EPI Equations: The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations are used to estimate GFR based on serum creatinine and/or cystatin C levels. The CKD-EPI creatinine equation is more accurate than the older Modification of Diet in Renal Disease (MDRD) equation, particularly at higher GFR levels. The CKD-EPI creatinine-cystatin C equation is currently the most accurate method for estimating GFR.[3-5]
3. Kaiser Permanente Northwest (KPNW) Model: This model includes variables such as age, sex, eGFR, albuminuria, systolic blood pressure, antihypertensive use, diabetes, and diabetes complications. It has shown excellent discrimination and calibration for predicting kidney failure over a 5-year period in the Kaiser Permanente Health System.[1][6]
4. Z6 Score: Developed in the German CKD study, the Z6 score uses variables including serum creatinine, albumin, cystatin C, urea, hemoglobin, and ACR. It has been validated in multiple European cohorts and shows good discrimination for predicting kidney failure over 5 years.[1]
5. Veterans Affairs Model: This model is another validated tool that uses routinely collected data from electronic medical records (EMRs) to predict kidney failure risk. It has been validated in various populations, including those in North America.[1]
These tools are integral to the management of CKD, allowing for early identification of patients at high risk for progression to ESKD and enabling timely interventions. The Kidney Disease: Improving Global Outcomes (KDIGO) guidelines recommend the use of these validated risk equations to estimate the absolute risk of kidney failure in patients with CKD stages G3-G5.[1]

References

1.
KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease.

Kidney International. 2024;105(4S):S117-S314. doi:10.1016/j.kint.2023.10.018.

Leading Journal
New Research

Equation,Variable,Population,Outcome (time horizon),Discrimination and calibration,Usability
KFREwww.kidneyfailurerisk.com www.ckdpc.org/risk-models.html,"Age, sex, eGFR, ACR (4 variable) + calcium, phosphate, bicarbonate, and albumin (8 variables)",">1 million patients, >100,000 events from more than 30 countries",Treated kidney failure (2–5 yr),0.88–0.91/+,+
KPNW,"Age, sex, eGFR, albuminuria, systolic BP, antihypertensive use, diabetes, and diabetes complications","39,013 patients, 1097 events from the Kaiser Permanente Health System (United States)",Kidney failure (5 yr),0.95/+,+
Landray et al.,"Sex, SCr, albuminuria, and phosphate","595 patients, >190 events from the CRIB and East Kent cohorts in the United Kingdom",Kidney failure,0.91/+,–
Z6 score,"SCr, albumin, cystatin C, urea, hemoglobin, and ACR","7978 patients, 870 events—developed in the German CKD study, validated in 3 additional European cohorts",Kidney failure (5 yr),0.89–0.92/+,–

There is a large body of evidence to support the use of the validated risk equations to estimate the absolute risk of kidney failure requiring dialysis or transplant in people with CKD G3–G5. Risk equations using routinely collected data have been developed, externally validated, and implemented in labs, EMRs, and health systems.
Multiple systematic reviews and quality assessments of risk prediction equations have been performed in the last 10 years, with the most recent review published in 2020. This review included 35 development studies and 17 external validation studies, and described the variables included in the prediction models and provided a decision aid for selecting the best model for the prediction horizon and the underlying etiology of kidney disease. More recently, an additional externally validated model using serum cystatin C has also been developed in Germany and externally validated in 3 European cohorts. A summary of externally validated models for kidney failure is provided below and in Table 19.
We highlight here 3 validated models, The Kidney Failure Risk Equation (KFRE), the Veterans Affairs model, and the Z6 Score model. All of these use routinely collected data from labs or EMRs and have been validated in different populations, both in North America and internationally to varying degrees. Detailed review of all existing prediction models is beyond the scope of this document.
The KFRE was developed and initially validated in 8391 adults from 2 Canadian provinces, and subsequently validated in 721,357 individuals from more than 30 countries spanning 4 continents. In this large validation study, cohorts from both general populations and nephrology clinic settings were included. Discrimination was excellent (C-statistic >0.80 in 28/30 cohorts), and the use of a calibration factor improved calibration for some regions outside of North America; the validation populations now exceed 2 million individuals in more than 60 cohorts from nearly every continent. The KFRE is consistently highly accurate and has not been improved by the addition of longitudinal slopes or variability of eGFR and urine ACR, or by adding cardiovascular comorbidities.

2.
Kidney Failure Prediction Models: A Comprehensive External Validation Study in Patients With Advanced CKD.

Ramspek CL, Evans M, Wanner C, et al.

Journal of the American Society of Nephrology : JASN. 2021;32(5):1174-1186. doi:10.1681/ASN.2020071077.

Leading Journal

Background: Various prediction models have been developed to predict the risk of kidney failure in patients with CKD. However, guideline-recommended models have yet to be compared head to head, their validation in patients with advanced CKD is lacking, and most do not account for competing risks.

Methods: To externally validate 11 existing models of kidney failure, taking the competing risk of death into account, we included patients with advanced CKD from two large cohorts: the European Quality Study (EQUAL), an ongoing European prospective, multicenter cohort study of older patients with advanced CKD, and the Swedish Renal Registry (SRR), an ongoing registry of nephrology-referred patients with CKD in Sweden. The outcome of the models was kidney failure (defined as RRT-treated ESKD). We assessed model performance with discrimination and calibration.

Results: The study included 1580 patients from EQUAL and 13,489 patients from SRR. The average statistic over the 11 validated models was 0.74 in EQUAL and 0.80 in SRR, compared with 0.89 in previous validations. Most models with longer prediction horizons overestimated the risk of kidney failure considerably. The 5-year Kidney Failure Risk Equation (KFRE) overpredicted risk by 10%-18%. The four- and eight-variable 2-year KFRE and the 4-year Grams model showed excellent calibration and good discrimination in both cohorts.

Conclusions: Some existing models can accurately predict kidney failure in patients with advanced CKD. KFRE performed well for a shorter time frame (2 years), despite not accounting for competing events. Models predicting over a longer time frame (5 years) overestimated risk because of the competing risk of death. The Grams model, which accounts for the latter, is suitable for longer-term predictions (4 years).

3.
Clinical Practice Guideline for the Management of Chronic Kidney Disease in Patients Infected With HIV: 2014 Update by the HIV Medicine Association of the Infectious Diseases Society of America.

Lucas GM, Ross MJ, Stock PG, et al.

Clinical Infectious Diseases : An Official Publication of the Infectious Diseases Society of America. 2014;59(9):e96-138. doi:10.1093/cid/ciu617.

Leading Journal

Name,Equation,Comments
Cockcroft–Gault [],CrCl=[140−age]×weight(kg)72×𝑆cr×0.85(if female),"Least accurate or precise of available equations [, ]May be useful for older, cachectic patientsFDA has traditionally required this equation be used for recommended drug dose modifications in kidney disease"
"MDRD, 4-variable, using standardizeda serum creatinine concentration []",GFR=175×𝑆−1.154cr×age−0.203×1.212(ifblack)×0.742(iffemale),Widely used by clinical laboratories to estimate GFRAccurate GFR estimates at GFR <60 mL/min/1.73 m2Underestimates GFR in patients with GFR >60 mL/min/1.73 m2 []
"CKD-EPI creatinine equation, using standardizeda serum creatinine concentration [, ]","Female, Scr ≤ 0.7: GFR=144×(𝑆cr0.7)−0.329×(0.993)age×1.159(if black)","More accurate than MDRD equation, particularly at GFR >60 mL/min/1.73 m2 [, ]"
"CKD-EPI creatinine equation, using standardizeda serum creatinine concentration [, ]","Female, Scr > 0.7: GFR=144×(𝑆cr0.7)−1.209×(0.993)age×1.159(if black)","More accurate than MDRD equation, particularly at GFR >60 mL/min/1.73 m2 [, ]"
"CKD-EPI creatinine equation, using standardizeda serum creatinine concentration [, ]","Male, Scr ≤ 0.9: GFR=141×(𝑆cr0.9)−0.411×(0.993)age×1.159(if black)","More accurate than MDRD equation, particularly at GFR >60 mL/min/1.73 m2 [, ]"

4.
Performance and Pitfalls of the Tools for Measuring Glomerular Filtration Rate to Guide Chronic Kidney Disease Diagnosis and Assessment.

Gama RM, Griffiths K, Vincent RP, Peters AM, Bramham K.

Journal of Clinical Pathology. 2023;76(7):442-449. doi:10.1136/jcp-2023-208887.

Accurate diagnosis, classification and risk stratification for chronic kidney disease (CKD) allow for early recognition and delivering optimal care. Creatinine-based glomerular filtration rate (GFR), urinary albumin: creatinine ratio (UACR) and the kidney failure risk equation (KFRE) are important tools to achieve this, but understanding their limitations is important for optimal implementation.When accurate GFR is required (eg, chemotherapy dosing), GFR is measured using an exogenous filtration marker. In routine clinical practice, in contrast, estimated GFR (eGFR) from serum creatinine (SCr), calculated using the enzymatic method±UACR, is recommended. Limitations of SCr include non-GFR determinants such as muscle mass, diet and tubular handling. An alternative or additional endogenous filtration marker is cystatin C, which can be used alongside SCr for confirmatory testing of CKD. However, its role in the UK is more limited due to concerns regarding false positive results.The recommended creatinine-based eGFR equation in the UK is the CKD Epidemiology Collaboration 2009 equation. This was recently updated to a race-neutral 2021 version and demonstrated reduced bias in people of Black ethnicity, but has not been validated in the UK. Limitations are extremes of age, inaccuracy at greater GFRs and reduced generalisability to under-represented ethnicity groups.The KFRE (based on age, sex, SCr and UACR) has recently been developed to help determine 2-year and 5-year risk of progression to end-stage kidney disease. It has been validated in over 30 countries and provides meaningful quantitative information to patients. However, supporting evidence for their performance in ethnic minority groups and kidney diseases such as glomerulonephritis remains modest.In conclusion, early identification, risk stratification of kidney disease and timely intervention are important to impact kidney disease progression. However, clinician awareness of the limitations and variability of creatinine, cystatin C and the eGFR equations, is key to appropriate interpretation of results.

5.
11. Chronic Kidney Disease and Risk Management: Standards of Care in Diabetes-2024.

Diabetes Care. 2024;47(Suppl 1):S219-S230. doi:10.2337/dc24-S011.

Leading Journal
New Research

Traditionally, eGFR is calculated from serum creatinine using a validated formula (15). eGFR is routinely reported by laboratories along with serum creatinine, and eGFR calculators are available online at nkdep.nih.gov. An eGFR persistently <60 mL/min/1.73 m2 and/or an urinary albumin value of >30 mg/g creatinine is considered abnormal, though optimal thresholds for clinical diagnosis are debated in older adults over age 70 years (1,16). Historically, a correction factor for muscle mass was included in a modified equation for African American people; however, race is a social and not a biologic construct, making it problematic to apply race to clinical algorithms, and the need to advance health equity and social justice is clear. Thus, it was decided that the equation should be altered such that it applies to all. Hence, a committee was convened, resulting in the recommendation for immediate implementation of the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) creatinine equation refit without the race variable in all laboratories in the U.S. (17). The CKD-EPI Refit equation is the eGFR formula that is now recommended for everyone (18). Additionally, increased use of cystatin C (another marker of eGFR) is suggested in combination with serum creatinine because combining filtration markers (creatinine and cystatin C) is more accurate and would support better clinical decisions than either marker alone.

6.
CKD Stage-Specific Utility of Two Equations for Predicting 1-Year Risk of ESKD.

Zheng S, Parikh RV, Tan TC, et al.

PloS One. 2023;18(11):e0293293. doi:10.1371/journal.pone.0293293.

New Research

Background: The Kidney Failure Risk Equation (KFRE) and Kaiser Permanente Northwest (KPNW) models have been proposed to predict progression to ESKD among adults with CKD within 2 and 5 years. We evaluated the utility of these equations to predict the 1-year risk of ESKD in a contemporary, ethnically diverse CKD population.

Methods: We conducted a retrospective cohort study of adult members of Kaiser Permanente Northern California (KPNC) with CKD Stages 3-5 from January 2008-September 2015. We ascertained the onset of ESKD through September 2016, and calculated stage-specific estimates of model discrimination and calibration for the KFRE and KPNW equations.

Results: We identified 108,091 eligible adults with CKD (98,757 CKD Stage 3; 8,384 CKD Stage 4; and 950 CKD Stage 5 not yet receiving kidney replacement therapy), with mean age of 75 years, 55% women, and 37% being non-white. The overall 1-year risk of ESKD was 0.8% (95%

Ci: 0.8-0.9%). The KFRE displayed only moderate discrimination for CKD 3 and 5 (c = 0.76) but excellent discrimination for CKD 4 (c = 0.86), with good calibration for CKD 3-4 patients but suboptimal calibration for CKD 5. Calibration by CKD stage was similar to KFRE for the KPNW equation but displayed worse calibration across CKD stages for 1-year ESKD prediction.

Conclusions: In a large, ethnically diverse, community-based CKD 3-5 population, both the KFRE and KPNW equation were suboptimal in accurately predicting the 1-year risk of ESKD within CKD stage 3 and 5, but more accurate for stage 4. Our findings suggest these equations can be used in1-year prediction for CKD 4 patients, but also highlight the need for more personalized, stage-specific equations that predicted various short- and long-term adverse outcomes to better inform overall decision-making.