Kidney Failure Prediction Models: A Comprehensive External Validation Study in Patients with Advanced CKD
Journal of the American Society of Nephrology, vol. 32, pp. 1174–1186
Abstract
Significance Statement Most kidney failure prediction models have been developed and validated in cohorts of patients with a wide range of disease severity, without accounting for the competing risk of death. Models recommended by guidelines, currently used in the clinic, have not undergone a head-to-head comparison. This study provides a comprehensive external validation of kidney failure prediction tools in two cohorts of patients with advanced CKD, taking the competing risk of death into account. Models that predict over a longer time frame of 5 years overestimate risk due to the competing risk of death. In patients with advanced CKD, the eight-variable 2-year Kidney Failure Risk Equation is recommended for short-term predictions surrounding preparation for RRT. The 4-year Grams model, which accounts for competing risk, is most suitable for longer-term predictions. 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 c 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).
Authors 14
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Leiden University Medical Center
Affiliation as printed
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
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Karolinska University Hospital · Karolinska Institutet
Affiliation as printed
Division of Renal Medicine, Department of Clinical Science, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
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Affiliation as printed
Division of Nephrology, University Hospital of Wurzburg, Wurzburg, Germany
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University of Würzburg · Universitätsklinikum Würzburg
Affiliation as printed
Division of Nephrology, Department of Internal Medicine 1, University Hospital Wurzburg, Wurzburg, Germany
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Amsterdam Public Health · Amsterdam University Medical Centers · University of Amsterdam
Affiliation as printed
Department of Medical Informatics, European Renal Association–European Dialysis and Transplant Association Registry, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Public Health Institute, Amsterdam, The Netherlands
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University of Wrocław · Wroclaw Medical University
Affiliation as printed
Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, Wroclaw, Poland
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University of Wrocław · Wroclaw Medical University
Affiliation as printed
Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, Wroclaw, Poland
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Istituto di Fisiologia Clinica · National Research Council
Affiliation as printed
Department of Clinical Epidemiology of Renal Diseases and Hypertension, Consiglio Nazionale della Ricerche - Istituto di fisiologia clinica, Reggio Calabria, Italy
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Istituto di Fisiologia Clinica · National Research Council
Affiliation as printed
Department of Clinical Epidemiology of Renal Diseases and Hypertension, Consiglio Nazionale della Ricerche - Istituto di fisiologia clinica, Reggio Calabria, Italy
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Affiliation as printed
Department of Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
United Kingdom Renal Registry, Bristol, United Kingdom
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Affiliation as printed
Departmen of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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Leiden University Medical Center
Affiliation as printed
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
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Amsterdam Public Health · Amsterdam University Medical Centers · University of Amsterdam
Affiliation as printed
Department of Medical Informatics, European Renal Association–European Dialysis and Transplant Association Registry, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Public Health Institute, Amsterdam, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
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