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Development of Risk Prediction Equations for Incident Chronic Kidney Disease.

Authors
  • Nelson, Robert G1
  • Grams, Morgan E2
  • Ballew, Shoshana H2
  • Sang, Yingying2, 3
  • Azizi, Fereidoun4
  • Chadban, Steven J5
  • Chaker, Layal6, 7, 8
  • Dunning, Stephan C3
  • Fox, Caroline9, 10
  • Hirakawa, Yoshihisa11
  • Iseki, Kunitoshi12
  • Ix, Joachim13, 14
  • Jafar, Tazeen H15, 16, 17
  • Köttgen, Anna2, 18
  • Naimark, David M J19
  • Ohkubo, Takayoshi20
  • Prescott, Gordon J21
  • Rebholz, Casey M2
  • Sabanayagam, Charumathi22, 23, 24
  • Sairenchi, Toshimi25
  • And 9 more
  • 1 Chronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona.
  • 2 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
  • 3 OptumLabs, Cambridge, Massachusetts.
  • 4 Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. , (Iran)
  • 5 Charles Perkins Centre, University of Sydney, Sydney, Australia. , (Australia)
  • 6 Academic Center for Thyroid Diseases, Erasmus Medical Center, Rotterdam, the Netherlands. , (Netherlands)
  • 7 Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands. , (Netherlands)
  • 8 Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands. , (Netherlands)
  • 9 Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland.
  • 10 The Framingham Heart Study, Framingham, Massachusetts.
  • 11 Department of Public Health and Health Systems, Nagoya University Graduate School of Medicine, Nagoya, Japan. , (Japan)
  • 12 Nakamura Clinic & Okinawa Asia Clinical Investigation Synergy, Okinawa, Japan. , (Japan)
  • 13 University of California, San Diego, La Jolla.
  • 14 Veterans Affairs San Diego Healthcare System, San Diego.
  • 15 Program in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore. , (Singapore)
  • 16 Department of Medicine, Aga Khan University, Karachi, Pakistan. , (Pakistan)
  • 17 Duke Global Health Institute, Durham, Duke University, North Carolina.
  • 18 Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany. , (Germany)
  • 19 Sunnybrook Hospital, University of Toronto, Toronto, Ontario, Canada. , (Canada)
  • 20 Department of Hygiene and Public Health, Teikyo University School of Medicine, Tokyo, Japan. , (Japan)
  • 21 Medical Statistics Team, School of Medicine, Medical Sciences & Nutrition, University of Aberdeen, Aberdeen, United Kingdom. , (United Kingdom)
  • 22 Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore. , (Singapore)
  • 23 Yong Loo Lin School of Medicine, National University of Singapore, Singapore. , (Singapore)
  • 24 Duke-National University of Singapore Medical School, Singapore, Singapore. , (Singapore)
  • 25 Department of Public Health, Dokkyo Medical University, Tochigi, Japan. , (Japan)
  • 26 Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany. , (Germany)
  • 27 Network Aging Research, University of Heidelberg, Heidelberg, Germany. , (Germany)
  • 28 Division of Nephrology and Hypertension, Department of Internal Medicine, St Marianna University School of Medicine, Kawasaki, Japan. , (Japan)
  • 29 Department of Medicine, University of Calgary, Calgary, Alberta, Canada. , (Canada)
  • 30 Peking University Institute of Nephrology, Division of Nephrology, Peking University First Hospital, Beijing, China. , (China)
  • 31 Department of Nephrology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands. , (Netherlands)
  • 32 The George Institute for Global Health, University of Oxford, United Kingdom. , (United Kingdom)
  • 33 The George Institute for Global Health, University of New South Wales, Australia. , (Australia)
  • 34 Medical Division, Maccabi Healthcare Services, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. , (Israel)
Type
Published Article
Journal
JAMA
Publisher
American Medical Association
Publication Date
Nov 08, 2019
Identifiers
DOI: 10.1001/jama.2019.17379
PMID: 31703124
Source
Medline
Language
English
License
Unknown

Abstract

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. To develop assessment tools to identify individuals at increased risk of CKD, defined by reduced estimated glomerular filtration rate (eGFR). 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). Demographic and clinical factors. Incident eGFR of less than 60 mL/min/1.73 m2. 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. 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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