Helena U Zacharias1, Michael Altenbuchinger2, Ulla T Schultheiss3, Johannes Raffler4, Fruzsina Kotsis3, Sahar Ghasemi5, Ibrahim Ali6, Barbara Kollerits7, Marie Metzger8, Inga Steinbrenner9, Peggy Sekula9, Ziad A Massy10, Christian Combe11, Philip A Kalra6, Florian Kronenberg7, Bénédicte Stengel8, Kai-Uwe Eckardt12, Anna Köttgen9, Matthias Schmid13, Wolfram Gronwald14, Peter J Oefner15. 1. Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany; Department of Internal Medicine I and Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, Kiel, Germany. Electronic address: h.zacharias@ikmb.uni-kiel.de. 2. Chair of Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany; Computational Biology Group, University of Hohenheim, Stuttgart, Germany. 3. Renal Division, Department of Medicine IV, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Institute of Genetic Epidemiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany. 4. Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany. 5. Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany. 6. Salford Royal Hospital and University of Manchester, Salford, United Kingdom. 7. Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria. 8. Clinical Epidemiology Team, Centre for Research in Epidemiology and Population Health (CESP), National Institute of Health and Medical Research (Inserm), Université Paris-Saclay, Université Versailles Saint-Quentin, Villejuif, France. 9. Institute of Genetic Epidemiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany. 10. Clinical Epidemiology Team, Centre for Research in Epidemiology and Population Health (CESP), National Institute of Health and Medical Research (Inserm), Université Paris-Saclay, Université Versailles Saint-Quentin, Villejuif, France; Department of Nephrology, Ambroise Paré University Hospital, Boulogne-Billancourt/Paris, France. 11. Service de Néphrologie Transplantation Dialyse Aphérèse, Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France; Inserm, U1026, Bordeaux Segalen University, Bordeaux, France. 12. Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin, Berlin, Germany; Department of Nephrology and Hypertension, Friedrich-Alexander Universität Erlangen Nürnberg, Erlangen, Germany. 13. Department of Medical Biometry, Informatics and Epidemiology, Faculty of Medicine, University of Bonn, Bonn, Germany. 14. Chair and Institute of Functional Genomics, University of Regensburg, Regensburg, Germany. 15. Chair and Institute of Functional Genomics, University of Regensburg, Regensburg, Germany. Electronic address: peter.oefner@ukr.de.
Abstract
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.
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.
Authors: Thomas Ferguson; Pietro Ravani; Manish M Sood; Alix Clarke; Paul Komenda; Claudio Rigatto; Navdeep Tangri Journal: Kidney Int Rep Date: 2022-05-13
Authors: S Schrod; A Schäfer; S Solbrig; R Lohmayer; W Gronwald; P J Oefner; T Beißbarth; R Spang; H U Zacharias; M Altenbuchinger Journal: Bioinformatics Date: 2022-06-24 Impact factor: 6.931