Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failure: A Meta-analysis | Chronic Kidney Disease | JAMA | JAMA Network
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Original Investigation
January 12, 2016

Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failure: A Meta-analysis

Author Affiliations
  • 1Department of Medicine, Seven Oaks General Hospital, University of Manitoba, Winnipeg, Canada
  • 2Department of Community Health Sciences, Seven Oaks General Hospital, University of Manitoba, Winnipeg, Canada
  • 3Johns Hopkins Medical Institutions, Baltimore, Maryland
  • 4Division of Nephrology at Tufts Medical Center, Boston, Massachusetts
  • 5Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
  • 6Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison
  • 7Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison
  • 8Medical Division, Maccabi Healthcare Services, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
  • 9Chronic Disease Research Group, Minneapolis Medical Research Foundation, Minneapolis, Minnesota
  • 10Department of Medicine, University of Minnesota, Minneapolis
  • 11Department of Measurement & Reporting, Provincial Health Service Authority, Vancouver, British Columbia, Canada
  • 12Department of General Practice and Primary Health Care, School of Population Health, University of Auckland, Auckland, New Zealand
  • 13Division of Renal Medicine, CLINTEC, Karolinska Institutet, Stockholm, Sweden
  • 14Departments of Medicine and Epidemiology and Biostatistics, Western University, and Institute for Clinical Evaluative Sciences, Ontario, Canada
  • 15Department of Cancer Research and Molecular Medicine, Faculty of Medicine, Norwegian University of Science Technology, Trondheim
  • 16Division of Nephrology, Department of Medicine, St Olav University Hospital, Trondheim, Norway
  • 17Division of Nephrology, Endocrinology and Vascular Medicine, Department of Medicine, Tohoku University School of Medicine, Sendai, Japan
  • 18Department of Epidemiology and Health Promotion, Institute for Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, Korea
  • 19Memphis Veterans Affairs Medical Center, Memphis, Tennessee
  • 20University of Tennessee Health Science Center, Memphis, Tennessee
  • 21Department of Medical Genetics, Molecular and Clinical Pharmacology, Division of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
  • 22Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, the Netherlands
  • 23Division of Applied Health Sciences, University of Aberdeen, and NHS Grampian, Foresterhill, Aberdeen, Scotland
  • 24Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
  • 25Division of Nephrology and Hypertension, Cleveland Clinic, Cleveland, Ohio
  • 26National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona
  • 27Department of Nephrology and Hypertension, University of Erlangen-Nürnberg, Erlangen, Germany
  • 28CESP, INSERM, Villejuif, France
  • 29Université Paris-Saclay, Université Paris-Sud, UVSQ, Villejuif, France
  • 30The George Institute for Global Health, Nuffield Department of Population Health, University of Oxford, Oxford, England
  • 31The George Institute for Global Health, University of Sydney, Sydney, Australia
  • 32Dialysis Unit, University Hospital of the Ryukyus, Nishihara, Okinawa, Japan
JAMA. 2016;315(2):164-174. doi:10.1001/jama.2015.18202
Abstract

Importance  Identifying patients at risk of chronic kidney disease (CKD) progression may facilitate more optimal nephrology care. Kidney failure risk equations, including such factors as age, sex, estimated glomerular filtration rate, and calcium and phosphate concentrations, were previously developed and validated in 2 Canadian cohorts. Validation in other regions and in CKD populations not under the care of a nephrologist is needed.

Objective  To evaluate the accuracy of the risk equations across different geographic regions and patient populations through individual participant data meta-analysis.

Data Sources  Thirty-one cohorts, including 721 357 participants with CKD stages 3 to 5 in more than 30 countries spanning 4 continents, were studied. These cohorts collected data from 1982 through 2014.

Study Selection  Cohorts participating in the CKD Prognosis Consortium with data on end-stage renal disease.

Data Extraction and Synthesis  Data were obtained and statistical analyses were performed between July 2012 and June 2015. Using the risk factors from the original risk equations, cohort-specific hazard ratios were estimated and combined using random-effects meta-analysis to form new pooled kidney failure risk equations. Original and pooled kidney failure risk equation performance was compared, and the need for regional calibration factors was assessed.

Main Outcomes and Measures  Kidney failure (treatment by dialysis or kidney transplant).

Results  During a median follow-up of 4 years of 721 357 participants with CKD, 23 829 cases kidney failure were observed. The original risk equations achieved excellent discrimination (ability to differentiate those who developed kidney failure from those who did not) across all cohorts (overall C statistic, 0.90; 95% CI, 0.89-0.92 at 2 years; C statistic at 5 years, 0.88; 95% CI, 0.86-0.90); discrimination in subgroups by age, race, and diabetes status was similar. There was no improvement with the pooled equations. Calibration (the difference between observed and predicted risk) was adequate in North American cohorts, but the original risk equations overestimated risk in some non-North American cohorts. Addition of a calibration factor that lowered the baseline risk by 32.9% at 2 years and 16.5% at 5 years improved the calibration in 12 of 15 and 10 of 13 non-North American cohorts at 2 and 5 years, respectively (P = .04 and P = .02).

Conclusions and Relevance  Kidney failure risk equations developed in a Canadian population showed high discrimination and adequate calibration when validated in 31 multinational cohorts. However, in some regions the addition of a calibration factor may be necessary.

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