Predicting Lifetime Risk of Kidney Failure Using Age and a Single eGFR Measurement.

Area of Science:

• Nephrology
• Geriatrics
• Predictive Modeling

Background:

• Chronic kidney disease (CKD) prognosis typically relies on longitudinal data, complicating initial risk assessment.
• Current clinical decisions based on estimated glomerular filtration rate (eGFR) often neglect patient age.
• A simplified predictive model is needed to address these limitations.

Purpose of the Study:

• To develop a simplified predictive model for progressive CKD incorporating patient age and eGFR.
• To quantify the impact of clinical interventions like smoking cessation and pharmacotherapies on CKD progression.
• To evaluate the clinical utility of the age + eGFR model compared to conventional methods.

Main Methods:

• Utilized a historical dataset from the pre-renin-angiotensin system inhibitor (RASi) and pre-sodium-glucose cotransporter 2 inhibitor (SGLT2i) era (1988-2003).
• Developed heatmaps to predict the probability of reaching eGFR < 30 mL/min/1.73 m² by age 80.
• Assessed predictive performance using calibration, discrimination metrics, and decision curve analysis.

Main Results:

• Age and eGFR showed a correlation with CKD progression risk; e.g., at age 40, an eGFR of ~57 mL/min/1.73 m² indicated a 50% risk of reaching stage 4 CKD by age 80.
• The age + eGFR model demonstrated acceptable calibration and discrimination.
• Smoking cessation reduced risk by 9.4-11.2%; combined RASi and SGLT2i treatment reduced progression probability by 31.2-40.0%.

Conclusions:

• The age + eGFR rule offers a simple, interpretable method for age-adjusted risk stratification using a single eGFR measurement.
• This heuristic may enhance clinical utility in guiding surveillance intensity and early intervention strategies.
• External validation is necessary before widespread clinical application.