CKD Risk Calculators

Below are two tools, one for calculating chronic kidney disease (CKD) risk in adults and one for calculating the risk of progression of CKD to kidney failure among those who already have CKD. The intended audience is medical researchers and clinicians; the calculators should only be used by laypersons to begin dialog with their care provider. These equations should not be used for self-diagnosis.

The first calculator (Bang, et al.) estimates the probability of having kidney disease based on individual characteristics, such as age, sex, and health status. The second calculator (Tangri, et al.) estimates the predicted probability of reaching kidney failure within the next five years based on individuals characteristics and kidney function tests (four variable equation) or with the addition of four serum laboratory tests (calcium, phosphate, bicarbonate, and albumin – eight variable equation).

The CKD Risk Calculators are designed for use on devices with larger displays. Please visit this page on a tablet, laptop, or desktop device.

Probability of CKD

This calculator returns the probability (expressed as a percentage from 0 to 100%) of having Stage 3-5 chronic kidney disease, defined as an estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73m2, based on nine input variables1. If one or more of the values is unknown, separately entering various values for the unknowns will give a range of possible estimates.

Identifying the probability of having CKD may encourage at-risk individuals to be screened for reduced kidney function, leading to earlier treatment to slow the progression of kidney disease.

Details of the risk equation development (Bang et al, 2007) are available here:

Characteristic This Person U.S. National
Enter Age and Sex:
Enter any of the following characteristics that may increase the probability of CKD:
Dabetes Description Help
Cardiovascular Disease (CVD):
Congestive Heart Failure (CHF):
Peripheral Vascular Disease (PVD):

Probability of Stages 3-5 Chronic Kidney Disease (CKD):

In order to show a chart of the calculations, the following are required:
  • Age
  • Sex
1 Probability (CKD) = 1/[1 + exp(-β’ x X)], where β’ x X = -5.4 + 1.55 x |(age of 50-59 years) + 2.31 x |(age of 60-69 years) + 3.23 x |(age ≥ 70 years) + 0.29 x |(female) + 0.93 x |(anemia) + 0.45 x |(hypertension) + 0.44 x |(DM) + 0.59 x |(history of CVD) + 0.45 x |(history of CHF) + 0.74 x |(PVD) + 0.83 x |(proteinuria), where |(a) is an indicator taking 1 for event a and 0 otherwise. In this equation, β and X denote vectors of β-coefficients and risk factors used in this equation, respectively.

* Derived by adding together micro- and macroalbuminuria (overall) into a single number (8.7% + 1.3% = 10%).

References and Sources:

Bang H, Vupputuri S, Shoham DA, Klemmer PJ, Falk RJ, Mazumdar M, Gipson D, Colindres RE, Kshirsagar AV. SCreening for Occult REnal Disease (SCORED): A simple prediction model for chronic kidney disease. Arch Intern Med. 2007 Feb 26;167(4):374-81.
Progression of CKD

For patients with chronic kidney disease (i.e., those with estimated glomerular filtration rate (eGFR), less than 60 mL/min/1.73m2), this calculator returns an estimated probability (expressed as a percentage or range of percentages from 0 to 100%) of having kidney failure in the next five years.

This probability may be useful for patient and provider communication, triage and management of nephrology referrals and timing of dialysis access placement and living related kidney transplant.

Two calculators are provided, one with four input variables, and one with eight input variables. Using more variables, when available, will yield a more precise estimate. Details of risk equation development (Tangri et al, 2011) are available here:


Reset eGFR slider values to original

Reset Albumin/Creatinine slider values to original

*Field is required for a result

Estimated probability of progression to end-stage renal disease (ESRD) in CKD patients at 2 and 5 years:

In order to show a chart of the calculations, the following are required:
  • Age
  • Sex

References and Sources:

Tangri, Navdeep, et al. Multinational assessment of accuracy of equations for predicting risk of kidney failure: a meta-analysis JAMA 315.2(2016):164-174. supplement

Tangri N, Stevens LA, Griffith J, Tighiouart H, Djurdjev O, Naimark D, Levin A, Levey AS. A predictive model for progression of chronic kidney disease to kidney failure. JAMA. 2011 Apr 20;305(15):1553-9. doi: 10.1001/jama.2011.451. Epub 2011 Apr 11.