Rationale & objective: Stratification of chronic kidney disease (CKD) patients at risk for progressing to kidney failure requiring kidney replacement therapy (KFRT) is important for clinical decision-making and trial enrollment.
Study design: Four independent prospective observational cohort studies.
Setting & participants: The development cohort comprised 4,915 CKD patients, and 3 independent validation cohorts comprised a total of 3,063. Patients were observed for approximately 5 years.
Exposure: 22 demographic, anthropometric, and laboratory variables commonly assessed in CKD patients.
Outcome: Progression to KFRT.
Analytical approach: A least absolute shrinkage and selection operator (LASSO) Cox proportional hazards model was fit to select laboratory variables that best identified patients at high risk for KFRT. Model discrimination and calibration were assessed and compared against the 4-variable Tangri (T4) risk equation both in a resampling approach within the development cohort and in the validation cohorts using cause-specific concordance (C) statistics, net reclassification improvement, and calibration graphs.
Results: The newly derived 6-variable risk score (Z6) included serum creatinine, albumin, cystatin C, and urea, as well as hemoglobin and the urinary albumin-creatinine ratio. In the the resampling approach, Z6 achieved a median C statistic of 0.909 (95% CI, 0.868-0.937) at 2 years after the baseline visit, whereas the T4 achieved a median C statistic of 0.855 (95% CI, 0.799-0.915). In the 3 independent validation cohorts, the Z6C statistics were 0.894, 0.921, and 0.891, whereas the T4C statistics were 0.882, 0.913, and 0.862.
Limitations: The Z6 was both derived and tested only in White European cohorts.
Conclusions: A new risk equation based on 6 routinely available laboratory tests facilitates identification of patients with CKD who are at high risk of progressing to KFRT.
Keywords: CKD progression; Chronic kidney disease (CKD); German Chronic Kidney Disease study; end-stage kidney disease (ESKD); kidney disease trajectory; kidney failure requiring kidney replacement therapy (KFRT); kidney failure risk equation; machine learning; risk equation.
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