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A Predictive Model for Progression of CKD to Kidney Failure Based on Routine Laboratory Tests.
Zacharias, Helena U; Altenbuchinger, Michael; Schultheiss, Ulla T; Raffler, Johannes; Kotsis, Fruzsina; Ghasemi, Sahar; Ali, Ibrahim; Kollerits, Barbara; Metzger, Marie; Steinbrenner, Inga; Sekula, Peggy; Massy, Ziad A; Combe, Christian; Kalra, Philip A; Kronenberg, Florian; Stengel, Bénédicte; Eckardt, Kai-Uwe; Köttgen, Anna; Schmid, Matthias; Gronwald, Wolfram; Oefner, Peter J.
Afiliação
  • Zacharias HU; 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.zacharia
  • Altenbuchinger M; Chair of Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany; Computational Biology Group, University of Hohenheim, Stuttgart, Germany.
  • Schultheiss UT; 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.
  • Raffler J; Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
  • Kotsis F; 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.
  • Ghasemi S; Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany.
  • Ali I; Salford Royal Hospital and University of Manchester, Salford, United Kingdom.
  • Kollerits B; Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria.
  • Metzger M; 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.
  • Steinbrenner I; Institute of Genetic Epidemiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Sekula P; Institute of Genetic Epidemiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Massy ZA; 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, Boul
  • Combe C; Service de Néphrologie Transplantation Dialyse Aphérèse, Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France; Inserm, U1026, Bordeaux Segalen University, Bordeaux, France.
  • Kalra PA; Salford Royal Hospital and University of Manchester, Salford, United Kingdom.
  • Kronenberg F; Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria.
  • Stengel B; 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.
  • Eckardt KU; Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin, Berlin, Germany; Department of Nephrology and Hypertension, Friedrich-Alexander Universität Erlangen Nürnberg, Erlangen, Germany.
  • Köttgen A; Institute of Genetic Epidemiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Schmid M; Department of Medical Biometry, Informatics and Epidemiology, Faculty of Medicine, University of Bonn, Bonn, Germany.
  • Gronwald W; Chair and Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.
  • Oefner PJ; Chair and Institute of Functional Genomics, University of Regensburg, Regensburg, Germany. Electronic address: peter.oefner@ukr.de.
Am J Kidney Dis ; 79(2): 217-230.e1, 2022 02.
Article em En | MEDLINE | ID: mdl-34298143
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Insuficiência Renal / Insuficiência Renal Crônica / Falência Renal Crônica Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Insuficiência Renal / Insuficiência Renal Crônica / Falência Renal Crônica Idioma: En Ano de publicação: 2022 Tipo de documento: Article