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000147513 0247_ $$2doi$$a10.1001/jama.2019.17379
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000147513 037__ $$aDKFZ-2019-02567
000147513 041__ $$aeng
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000147513 1001_ $$aNelson, Robert G$$b0
000147513 245__ $$aDevelopment of Risk Prediction Equations for Incident Chronic Kidney Disease.
000147513 260__ $$aChicago, Ill.$$bAmerican Medical Association$$c2019
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000147513 500__ $$aJAMA. 2019;322(21):2104-2114
000147513 520__ $$aEarly 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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000147513 7001_ $$aGrams, Morgan E$$b1
000147513 7001_ $$aBallew, Shoshana H$$b2
000147513 7001_ $$aSang, Yingying$$b3
000147513 7001_ $$aAzizi, Fereidoun$$b4
000147513 7001_ $$aChadban, Steven J$$b5
000147513 7001_ $$aChaker, Layal$$b6
000147513 7001_ $$aDunning, Stephan C$$b7
000147513 7001_ $$aFox, Caroline$$b8
000147513 7001_ $$aHirakawa, Yoshihisa$$b9
000147513 7001_ $$aIseki, Kunitoshi$$b10
000147513 7001_ $$aIx, Joachim$$b11
000147513 7001_ $$aJafar, Tazeen H$$b12
000147513 7001_ $$aKöttgen, Anna$$b13
000147513 7001_ $$aNaimark, David M J$$b14
000147513 7001_ $$aOhkubo, Takayoshi$$b15
000147513 7001_ $$aPrescott, Gordon J$$b16
000147513 7001_ $$aRebholz, Casey M$$b17
000147513 7001_ $$aSabanayagam, Charumathi$$b18
000147513 7001_ $$aSairenchi, Toshimi$$b19
000147513 7001_ $$0P:(DE-He78)c67a12496b8aac150c0eef888d808d46$$aSchöttker, Ben$$b20$$udkfz
000147513 7001_ $$aShibagaki, Yugo$$b21
000147513 7001_ $$aTonelli, Marcello$$b22
000147513 7001_ $$aZhang, Luxia$$b23
000147513 7001_ $$aGansevoort, Ron T$$b24
000147513 7001_ $$aMatsushita, Kunihiro$$b25
000147513 7001_ $$aWoodward, Mark$$b26
000147513 7001_ $$aCoresh, Josef$$b27
000147513 7001_ $$aShalev, Varda$$b28
000147513 7001_ $$aConsortium, CKD Prognosis$$b29$$eCollaboration Author
000147513 773__ $$0PERI:(DE-600)2018410-4$$a10.1001/jama.2019.17379$$n21$$p2104-2114$$tThe journal of the American Medical Association$$v322$$x0098-7484$$y2019
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