A competing risk predictive model for kidney failure in patients with advanced chronic kidney disease

Abstract

Background/purpose: Predictive modeling aids in identifying patients at high risk of adverse events. Using routinely collected data, we report a competing risk prediction model for kidney failure. Methods: A total of 5138 patients with CKD stages 3b–5 were included and randomized into the development and validation cohorts at a ratio of 7:3. The outcome was end-stage kidney disease, defined as the initiation of dialysis or kidney transplantation. All patients were followed-up until December 31, 2020. A Fine and Gray model was applied to estimate the sub-hazard ratio of kidney failure, with death as a competing event. Results: In the development cohort, the mean age was 67.6 ± 13.9 years and 60 % were male. The mean index eGFR and median urinary protein-creatinine ratio (UPCR) were 26.5 ± 12.8 mL/min/1.73 m2 and 1051 mg/g, respectively. The median follow-up duration was 1051 days. The proportion of patients with kidney failure and death was 25.4 % and 14.1 %, respectively. Four models were applied, including eGFR, age, sex, UPCR, systolic and diastolic blood pressure, serum albumin, phosphate, uric acid, haemoglobin, and potassium levels had the best goodness of fit. All models had good discrimination with time-to-event c statistics of 0.89–0.95 in the development cohort and 0.86–0.95 in the validation cohort. The prediction models showed excellent and fairly good calibration at 2 and 5-year risk, respectively. Conclusion: Using real-world data, our competing risk model can accurately predict progression to kidney failure over 2 years in patients with advanced CKD.

Keywords

• Chronic kidney disease
• Clinical decision rules
• Kidney failure
• Chronic