Please use this identifier to cite or link to this item: https://doi.org/10.1001/jama.2019.17379
Title: Development of Risk Prediction Equations for Incident Chronic Kidney Disease
Authors: Nelson, Robert G
Grams, Morgan E
Ballew, Shoshana H
Sang, Yingying
Azizi, Fereidoun
Chadban, Steven J
Chaker, Layal
Dunning, Stephan C
Fox, Caroline
Hirakawa, Yoshihisa
Iseki, Kunitoshi
Ix, Joachim
Jafar, Tazeen H
Koettgen, Anna
Naimark, David MJ
Ohkubo, Takayoshi
Prescott, Gordon J
Rebholz, Casey M
Sabanayagam, Charumathi 
Sairenchi, Toshimi
Schoettker, Ben
Shibagaki, Yugo
Tonelli, Marcello
Zhang, Luxia
Gansevoort, Ron T
Matsushita, Kunihiro
Woodward, Mark
Coresh, Josef
Shalev, Varda
Chalmers, John
Arima, Hisatomi
Perkovic, Vlado
Woodward, Mark
Coresh, Josef
Matsushita, Kunihiro
Grams, Morgan
Sang, Yingying
Polkinghorne, Kevan
Atkins, Robert
Chadban, Steven
Zhang, Luxia
Liu, Lisheng
Zhao, Ming-Hui
Wang, Fang
Wang, Jinwei
Tonelli, Marcello
Sacks, Frank M
Curhan, Gary C
Shlipak, Michael
Sarnak, Mark J
Katz, Ronit
Hiramoto, Jade
Iso, Hiroyasu
Muraki, Isao
Yamagishi, Kazumasa
Umesawa, Mitsumasa
Brenner, Hermann
Schoettker, Ben
Saum, Kai-Uwe
Rothenbacher, Dietrich
Fox, Caroline S
Hwang, Shih-Jen
Chang, Alex R
Green, Jamie
Singh, Gurmukteshwar
Kirchner, H Lester
Black, Corri
Marks, Angharad
Prescott, Gordon J
Clark, Laura
Fluck, Nick
Cirillo, Massimo
Hallan, Stein
Ovrehus, Marius
Langlo, Knut Asbjorn
Romundstad, Solfrid
Irie, Fujiko
Sairenchi, Toshimi
Correa, Adolfo
Rebholz, Casey M
Young, Bessie A
Boulware, L Ebony
Mwasongwe, Stanford
Watanabe, Tsuyoshi
Yamagata, Kunihiro
Iseki, Kunitoshi
Asahi, Koichi
Chodick, Gabriel
Shalev, Varda
Shlipak, Michael
Sarnak, Mark
Katz, Ronit
Peralta, Carmen
Bottinger, Erwin
Nadkarni, Girish N
Ellis, Stephen B
Nadukuru, Rajiv
Kenealy, Timothy
Elley, C Raina
Collins, John F
Drury, Paul L
Ohkubo, Takayoshi
Asayama, Kei
Kikuya, Masahiro
Metoki, Hirohito
Nakayama, Masaaki
Iseki, Kunitoshi
Iseki, Chiho
Nelson, Robert G
Looker, Helen C
Knowler, William C
Gansevoort, Ron T
Bakker, Stephan JL
Heerspink, Hiddo JL
Bernardo, Rancho
Jassal, Simerjot K
Bergstrom, Jaclyn
Ix, Joachim H
Barrett-Connor, Elizabeth
Kovesdy, Csaba P
Sumida, Keiichi
Kalantar-Zadeh, Kamyar
Sedaghat, Sanaz
Chaker, Layal
Ikram, M Arfan
Hoorn, Ewout J
Dehghan, Abbas
Carrero, Juan J
Evans, Marie
Elinder, Carl-Gustaf
Wettermark, Bjorn
Wong, Tien Y
Sabanayagam, Charumathi
Cheng, Ching-Yu
Sokor, Riswana Banu Binte Mohamed Abdul
Wen, Chi-Pang
Tsao, Chwen-Keng
Tsai, Min-Kuang
Chen, Chien-Hua
Hosseinpanah, Farhad
Hadaegh, Farzad
Azizi, Fereidoun
Mirbolouk, Mohammadhassan
Solbu, Marit Dahl
Eriksen, Bjorn Odvar
Jenssen, Trond Geir
Eggen, Anne Elise
Lannfelt, Lars
Larsson, Anders
Arnlov, Johan
Bilo, Henk JG
Landman, Gijs WD
van Hateren, Kornelis JJ
Kleefstra, Nanne
Dunning, Stephan C
Stempniewicz, Nikita
Cuddeback, John
Ciemins, Elizabeth
Coresh, Josef
Grams, Morgan E
Ballew, Shoshana H
Matsushita, Kunihiro
Woodward, Mark
Gansevoort, Ron T
Chang, Alex R
Hallan, Stein
Koettgen, Anna
Kovesdy, Csaba P
Levey, Andrew S
Shalev, Varda
Zhang, Luxia
Ballew, Shoshana H
Chen, Jingsha
Coresh, Josef
Grams, Morgan E
Kwak, Lucia
Matsushita, Kunihiro
Sang, Yingying
Surapeneni, Aditya
Woodward, Mark
Keywords: Science & Technology
Life Sciences & Biomedicine
Medicine, General & Internal
General & Internal Medicine
RENAL-DISEASE
MODEL
CKD
Issue Date: 3-Dec-2019
Publisher: AMER MEDICAL ASSOC
Citation: Nelson, Robert G, Grams, Morgan E, Ballew, Shoshana H, Sang, Yingying, Azizi, Fereidoun, Chadban, Steven J, Chaker, Layal, Dunning, Stephan C, Fox, Caroline, Hirakawa, Yoshihisa, Iseki, Kunitoshi, Ix, Joachim, Jafar, Tazeen H, Koettgen, Anna, Naimark, David MJ, Ohkubo, Takayoshi, Prescott, Gordon J, Rebholz, Casey M, Sabanayagam, Charumathi, Sairenchi, Toshimi, Schoettker, Ben, Shibagaki, Yugo, Tonelli, Marcello, Zhang, Luxia, Gansevoort, Ron T, Matsushita, Kunihiro, Woodward, Mark, Coresh, Josef, Shalev, Varda, Chalmers, John, Arima, Hisatomi, Perkovic, Vlado, Woodward, Mark, Coresh, Josef, Matsushita, Kunihiro, Grams, Morgan, Sang, Yingying, Polkinghorne, Kevan, Atkins, Robert, Chadban, Steven, Zhang, Luxia, Liu, Lisheng, Zhao, Ming-Hui, Wang, Fang, Wang, Jinwei, Tonelli, Marcello, Sacks, Frank M, Curhan, Gary C, Shlipak, Michael, Sarnak, Mark J, Katz, Ronit, Hiramoto, Jade, Iso, Hiroyasu, Muraki, Isao, Yamagishi, Kazumasa, Umesawa, Mitsumasa, Brenner, Hermann, Schoettker, Ben, Saum, Kai-Uwe, Rothenbacher, Dietrich, Fox, Caroline S, Hwang, Shih-Jen, Chang, Alex R, Green, Jamie, Singh, Gurmukteshwar, Kirchner, H Lester, Black, Corri, Marks, Angharad, Prescott, Gordon J, Clark, Laura, Fluck, Nick, Cirillo, Massimo, Hallan, Stein, Ovrehus, Marius, Langlo, Knut Asbjorn, Romundstad, Solfrid, Irie, Fujiko, Sairenchi, Toshimi, Correa, Adolfo, Rebholz, Casey M, Young, Bessie A, Boulware, L Ebony, Mwasongwe, Stanford, Watanabe, Tsuyoshi, Yamagata, Kunihiro, Iseki, Kunitoshi, Asahi, Koichi, Chodick, Gabriel, Shalev, Varda, Shlipak, Michael, Sarnak, Mark, Katz, Ronit, Peralta, Carmen, Bottinger, Erwin, Nadkarni, Girish N, Ellis, Stephen B, Nadukuru, Rajiv, Kenealy, Timothy, Elley, C Raina, Collins, John F, Drury, Paul L, Ohkubo, Takayoshi, Asayama, Kei, Kikuya, Masahiro, Metoki, Hirohito, Nakayama, Masaaki, Iseki, Kunitoshi, Iseki, Chiho, Nelson, Robert G, Looker, Helen C, Knowler, William C, Gansevoort, Ron T, Bakker, Stephan JL, Heerspink, Hiddo JL, Bernardo, Rancho, Jassal, Simerjot K, Bergstrom, Jaclyn, Ix, Joachim H, Barrett-Connor, Elizabeth, Kovesdy, Csaba P, Sumida, Keiichi, Kalantar-Zadeh, Kamyar, Sedaghat, Sanaz, Chaker, Layal, Ikram, M Arfan, Hoorn, Ewout J, Dehghan, Abbas, Carrero, Juan J, Evans, Marie, Elinder, Carl-Gustaf, Wettermark, Bjorn, Wong, Tien Y, Sabanayagam, Charumathi, Cheng, Ching-Yu, Sokor, Riswana Banu Binte Mohamed Abdul, Wen, Chi-Pang, Tsao, Chwen-Keng, Tsai, Min-Kuang, Chen, Chien-Hua, Hosseinpanah, Farhad, Hadaegh, Farzad, Azizi, Fereidoun, Mirbolouk, Mohammadhassan, Solbu, Marit Dahl, Eriksen, Bjorn Odvar, Jenssen, Trond Geir, Eggen, Anne Elise, Lannfelt, Lars, Larsson, Anders, Arnlov, Johan, Bilo, Henk JG, Landman, Gijs WD, van Hateren, Kornelis JJ, Kleefstra, Nanne, Dunning, Stephan C, Stempniewicz, Nikita, Cuddeback, John, Ciemins, Elizabeth, Coresh, Josef, Grams, Morgan E, Ballew, Shoshana H, Matsushita, Kunihiro, Woodward, Mark, Gansevoort, Ron T, Chang, Alex R, Hallan, Stein, Koettgen, Anna, Kovesdy, Csaba P, Levey, Andrew S, Shalev, Varda, Zhang, Luxia, Ballew, Shoshana H, Chen, Jingsha, Coresh, Josef, Grams, Morgan E, Kwak, Lucia, Matsushita, Kunihiro, Sang, Yingying, Surapeneni, Aditya, Woodward, Mark (2019-12-03). Development of Risk Prediction Equations for Incident Chronic Kidney Disease. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION 322 (21) : 2104-2114. ScholarBank@NUS Repository. https://doi.org/10.1001/jama.2019.17379
Abstract: © 2019 American Medical Association. All rights reserved. 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 5222711 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 = 2253540). Exposures: Demographic and clinical factors. Main Outcomes and Measures: Incident eGFR of less than 60 mL/min/1.73 m2. Results: Among 4441084 participants without diabetes (mean age, 54 years, 38% women), 660856 incident cases (14.9%) of reduced eGFR occurred during a mean follow-up of 4.2 years. Of 781627 participants with diabetes (mean age, 62 years, 13% women), 313646 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..
Source Title: JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
URI: https://scholarbank.nus.edu.sg/handle/10635/173174
ISSN: 00987484
15383598
DOI: 10.1001/jama.2019.17379
Appears in Collections:Staff Publications
Elements

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Risk equations for incient CKD_JAMA 2019 CKD PC.pdfPublished version433.59 kBAdobe PDF

CLOSED

None

SCOPUSTM   
Citations

27
checked on Oct 7, 2021

Page view(s)

131
checked on Oct 14, 2021

Download(s)

3
checked on Oct 14, 2021

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.