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Original Investigation
November 8, 2019

Development of Risk Prediction Equations for Incident Chronic Kidney Disease

Author Affiliations
  • 1Chronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona
  • 2Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
  • 3OptumLabs, Cambridge, Massachusetts
  • 4Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  • 5Charles Perkins Centre, University of Sydney, Sydney, Australia
  • 6Academic Center for Thyroid Diseases, Erasmus Medical Center, Rotterdam, the Netherlands
  • 7Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
  • 8Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
  • 9Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
  • 10The Framingham Heart Study, Framingham, Massachusetts
  • 11Department of Public Health and Health Systems, Nagoya University Graduate School of Medicine, Nagoya, Japan
  • 12Nakamura Clinic & Okinawa Asia Clinical Investigation Synergy, Okinawa, Japan
  • 13University of California, San Diego, La Jolla
  • 14Veterans Affairs San Diego Healthcare System, San Diego
  • 15Program in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore
  • 16Department of Medicine, Aga Khan University, Karachi, Pakistan
  • 17Duke Global Health Institute, Durham, Duke University, North Carolina
  • 18Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
  • 19Sunnybrook Hospital, University of Toronto, Toronto, Ontario, Canada
  • 20Department of Hygiene and Public Health, Teikyo University School of Medicine, Tokyo, Japan
  • 21Medical Statistics Team, School of Medicine, Medical Sciences & Nutrition, University of Aberdeen, Aberdeen, United Kingdom
  • 22Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
  • 23Yong Loo Lin School of Medicine, National University of Singapore, Singapore
  • 24Duke-National University of Singapore Medical School, Singapore, Singapore
  • 25Department of Public Health, Dokkyo Medical University, Tochigi, Japan
  • 26Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
  • 27Network Aging Research, University of Heidelberg, Heidelberg, Germany
  • 28Division of Nephrology and Hypertension, Department of Internal Medicine, St Marianna University School of Medicine, Kawasaki, Japan
  • 29Department of Medicine, University of Calgary, Calgary, Alberta, Canada
  • 30Peking University Institute of Nephrology, Division of Nephrology, Peking University First Hospital, Beijing, China
  • 31Department of Nephrology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
  • 32The George Institute for Global Health, University of Oxford, United Kingdom
  • 33The George Institute for Global Health, University of New South Wales, Australia
  • 34Medical Division, Maccabi Healthcare Services, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
JAMA. Published online November 8, 2019. doi:https://doi.org/10.1001/jama.2019.17379
Key Points

Question  Can development of chronic kidney disease be predicted using readily available demographic, clinical, and laboratory variables?

Findings  In this analysis of 5 222 711 individuals in 34 multinational cohorts from 28 countries, 5-year risk prediction equations for CKD were developed and demonstrated high discrimination (median C statistic for the equation for individuals without diabetes, 0.85; median C statistic for the equation for individuals with diabetes, 0.80) and variable calibration (69% of the study populations had a slope of observed to predicted risk between 0.80 and 1.25). Discrimination and calibration were similar in 9 external cohorts consisting of 2 253 540 individuals.

Meaning  Equations for predicting risk of incident chronic kidney disease were developed from more than 5 million individuals from 34 multinational cohorts and demonstrated high discrimination and variable calibration in diverse populations.

Abstract

Importance  Early identification of individuals at elevated risk of developing chronic kidney disease (CKD) could improve clinical care through enhanced surveillance and better management of underlying health conditions.

Objective  To develop assessment tools to identify individuals at increased risk of CKD, defined by reduced estimated glomerular filtration rate (eGFR).

Design, Setting, and Participants  Individual-level data analysis of 34 multinational cohorts from the CKD Prognosis Consortium including 5 222 711 individuals from 28 countries. Data were collected from April 1970 through January 2017. A 2-stage analysis was performed, with each study first analyzed individually and summarized overall using a weighted average. Because clinical variables were often differentially available by diabetes status, models were developed separately for participants with diabetes and without diabetes. Discrimination and calibration were also tested in 9 external cohorts (n = 2 253 540).

Exposures  Demographic and clinical factors.

Main Outcomes and Measures  Incident eGFR of less than 60 mL/min/1.73 m2.

Results  Among 4 441 084 participants without diabetes (mean age, 54 years, 38% women), 660 856 incident cases (14.9%) of reduced eGFR occurred during a mean follow-up of 4.2 years. Of 781 627 participants with diabetes (mean age, 62 years, 13% women), 313 646 incident cases (40%) occurred during a mean follow-up of 3.9 years. Equations for the 5-year risk of reduced eGFR included age, sex, race/ethnicity, eGFR, history of cardiovascular disease, ever smoker, hypertension, body mass index, and albuminuria concentration. For participants with diabetes, the models also included diabetes medications, hemoglobin A1c, and the interaction between the 2. The risk equations had a median C statistic for the 5-year predicted probability of 0.845 (interquartile range [IQR], 0.789-0.890) in the cohorts without diabetes and 0.801 (IQR, 0.750-0.819) in the cohorts with diabetes. Calibration analysis showed that 9 of 13 study populations (69%) had a slope of observed to predicted risk between 0.80 and 1.25. Discrimination was similar in 18 study populations in 9 external validation cohorts; calibration showed that 16 of 18 (89%) had a slope of observed to predicted risk between 0.80 and 1.25.

Conclusions and Relevance  Equations for predicting risk of incident chronic kidney disease developed from more than 5 million individuals from 34 multinational cohorts demonstrated high discrimination and variable calibration in diverse populations. Further study is needed to determine whether use of these equations to identify individuals at risk of developing chronic kidney disease will improve clinical care and patient outcomes.

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