Predicting timing of clinical outcomes in patients with chronic kidney disease and severely decreased glomerular filtration rate

TY - JOUR

T1 - Predicting timing of clinical outcomes in patients with chronic kidney disease and severely decreased glomerular filtration rate

AU - Grams, Morgan E.

AU - Sang, Yingying

AU - Ballew, Shoshana H.

AU - Carrero, Juan Jesus

AU - Djurdjev, Ognjenka

AU - Heerspink, Hiddo J.L.

AU - Ho, Kevin

AU - Ito, Sadayoshi

AU - Marks, Angharad

AU - Naimark, David

AU - Nash, Danielle M.

AU - Navaneethan, Sankar D.

AU - Sarnak, Mark

AU - Stengel, Benedicte

AU - Visseren, Frank L.J.

AU - Wang, Angela Yee-Moon

AU - Köttgen, Anna

AU - Levey, Andrew S.

AU - Woodward, Mark

AU - Eckardt, Kai-Uwe

AU - Hemmelgarn, Brenda

AU - Coresh, Josef

N1 - This project was funded by the Kidney Disease: Improving Global Outcomes Foundation. The CKD-PC Data Coordinating Center is funded in part by a program grant from the US National Kidney Foundation, the Kidney Disease: Improving Global Outcomes Foundation, and the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK100446-01). A variety of sources have supported enrollment and data collection, including laboratory measurements and follow-up in the collaborating cohorts of the CKD-PC. These funding sources include government agencies such as national institutes of health and medical research councils, as well as foundations and industry sponsors listed in Appendix S3. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Some of the data reported here have been supplied by the United States Renal Data System. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the US government.

PY - 2018/6

Y1 - 2018/6

N2 - Patients with chronic kidney disease and severely decreased glomerular filtration rate (GFR) are at high risk for kidney failure, cardiovascular disease (CVD) and death. Accurate estimates of risk and timing of these clinical outcomes could guide patient counseling and therapy. Therefore, we developed models using data of 264,296 individuals in 30 countries participating in the international Chronic Kidney Disease Prognosis Consortium with estimated GFR (eGFR)s under 30 ml/min/1.73m2. Median participant eGFR and urine albumin-to-creatinine ratio were 24 ml/min/1.73m2 and 168 mg/g, respectively. Using competing-risk regression, random-effect meta-analysis, and Markov processes with Monte Carlo simulations, we developed two- and four-year models of the probability and timing of kidney failure requiring kidney replacement therapy (KRT), a non-fatal CVD event, and death according to age, sex, race, eGFR, albumin-to-creatinine ratio, systolic blood pressure, smoking status, diabetes mellitus, and history of CVD. Hypothetically applied to a 60-year-old white male with a history of CVD, a systolic blood pressure of 140 mmHg, an eGFR of 25 ml/min/1.73m2 and a urine albumin-to-creatinine ratio of 1000 mg/g, the four-year model predicted a 17% chance of survival after KRT, a 17% chance of survival after a CVD event, a 4% chance of survival after both, and a 28% chance of death (9% as a first event, and 19% after another CVD event or KRT). Risk predictions for KRT showed good overall agreement with the published kidney failure risk equation, and both models were well calibrated with observed risk. Thus, commonly-measured clinical characteristics can predict the timing and occurrence of clinical outcomes in patients with severely decreased GFR.

AB - Patients with chronic kidney disease and severely decreased glomerular filtration rate (GFR) are at high risk for kidney failure, cardiovascular disease (CVD) and death. Accurate estimates of risk and timing of these clinical outcomes could guide patient counseling and therapy. Therefore, we developed models using data of 264,296 individuals in 30 countries participating in the international Chronic Kidney Disease Prognosis Consortium with estimated GFR (eGFR)s under 30 ml/min/1.73m2. Median participant eGFR and urine albumin-to-creatinine ratio were 24 ml/min/1.73m2 and 168 mg/g, respectively. Using competing-risk regression, random-effect meta-analysis, and Markov processes with Monte Carlo simulations, we developed two- and four-year models of the probability and timing of kidney failure requiring kidney replacement therapy (KRT), a non-fatal CVD event, and death according to age, sex, race, eGFR, albumin-to-creatinine ratio, systolic blood pressure, smoking status, diabetes mellitus, and history of CVD. Hypothetically applied to a 60-year-old white male with a history of CVD, a systolic blood pressure of 140 mmHg, an eGFR of 25 ml/min/1.73m2 and a urine albumin-to-creatinine ratio of 1000 mg/g, the four-year model predicted a 17% chance of survival after KRT, a 17% chance of survival after a CVD event, a 4% chance of survival after both, and a 28% chance of death (9% as a first event, and 19% after another CVD event or KRT). Risk predictions for KRT showed good overall agreement with the published kidney failure risk equation, and both models were well calibrated with observed risk. Thus, commonly-measured clinical characteristics can predict the timing and occurrence of clinical outcomes in patients with severely decreased GFR.

KW - albuminuria

KW - cardiovascular disease

KW - chronic kidney disease

UR - http://www.scopus.com/inward/record.url?scp=85044330182&partnerID=8YFLogxK

U2 - 10.1016/j.kint.2018.01.009

DO - 10.1016/j.kint.2018.01.009

M3 - Article

VL - 93

SP - 1442

EP - 1451

JO - Kidney International

JF - Kidney International

SN - 0085-2538

IS - 6

ER -