Navdeep Tangri, MD, PhD; Georgios D. Kitsios, MD, PhD, MS; Lesley Ann Inker, MD, MS; John Griffith, PhD; David M. Naimark, MD, MSc; Simon Walker, BSc(Hons); Claudio Rigatto, MD, MSc; Katrin Uhlig, MD, MS; David M. Kent, MD, MS; Andrew S. Levey, MD
Note: Drs. Tangri, Naimark, Levey, Inker, and Griffith were part of the team that developed one of the predictive models reviewed in this paper.
Financial Support: By the William B. Schwartz Research Fund, Division of Nephrology, Tufts Medical Center.
Potential Conflicts of Interest: Dr. Levey: Board membership (money to institution): National Kidney Foundation; Grants/grants pending (money to institution): National Kidney Foundation, National Institutes of Health, Amgen, Pharmalink; Payment for lectures including service on speakers bureaus: Multiple universities; Travel/accommodations/meeting expenses unrelated to activities listed (money to institution): Multiple universities. All other authors have no disclosures. Disclosures can be also viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M12-2411.
Requests for Single Reprints: Navdeep Tangri, MD, PhD, Seven Oaks General Hospital, 2PD-13, 2300 McPhillips Street, Winnipeg, Manitoba R2V 3M3, Canada; e-mail, firstname.lastname@example.org.
Current Author Addresses: Drs. Tangri and Rigatto and Mr. Walker: Seven Oaks General Hospital, 2PD-13, 2300 McPhillips Street, Winnipeg, Manitoba R2V 3M3, Canada.
Drs. Inker, Uhlig, Kent, and Levey: Tufts Medical Center, 800 Washington Street, Box 391, Boston, MA 02111.
Dr. Kitsios: Graduate Medical Education, Internal Medicine, Lahey Clinic Medical Center, 41 Mall Road, Burlington, MA 01805.
Dr. Griffith: Northeastern University, 110 Behrakis Health Sciences Center, 360 Huntington Avenue, Boston, MA 02115.
Dr. Naimark: University of Toronto, Room A139, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada.
Author Contributions: Conception and design: N. Tangri, G.D. Kitsios, K. Uhlig, D.M. Kent, A.S. Levey.
Analysis and interpretation of the data: N. Tangri, G.D. Kitsios, L.A. Inker, J. Griffith, S. Walker, K. Uhlig, D.M. Kent, A.S. Levey.
Drafting of the article: N. Tangri, G.D. Kitsios, D.M. Naimark, C. Rigatto.
Critical revision of the article for important intellectual content: N. Tangri, G.D. Kitsios, L.A. Inker, D.M. Naimark, C. Rigatto, K. Uhlig, D.M. Kent, A.S. Levey.
Final approval of the article: N. Tangri, G.D. Kitsios, L.A. Inker, D.M. Naimark, C. Rigatto, K. Uhlig, D.M. Kent, A.S. Levey.
Provision of study materials or patients: N. Tangri.
Statistical expertise: N. Tangri, G.D. Kitsios, J. Griffith.
Obtaining of funding: N. Tangri, A.S. Levey.
Administrative, technical, or logistic support: D.M. Kent, A.S. Levey.
Collection and assembly of data: N. Tangri, G.D. Kitsios, S. Walker, C. Rigatto.
Patients with chronic kidney disease (CKD) are at increased risk for kidney failure, cardiovascular events, and all-cause mortality. Accurate models are needed to predict the individual risk for these outcomes.
To systematically review risk prediction models for kidney failure, cardiovascular events, and death in patients with CKD.
MEDLINE search of English-language articles published from 1966 to November 2012.
Cohort studies that examined adults with any stage of CKD who were not receiving dialysis and had not had a transplant; had at least 1 year of follow-up; and reported on a model that predicted the risk for kidney failure, cardiovascular events, or all-cause mortality.
Reviewers extracted data on study design, population characteristics, modeling methods, metrics of model performance, risk of bias, and clinical usefulness.
Thirteen studies describing 23 models were found. Eight studies (11 models) involved kidney failure, 5 studies (6 models) involved all-cause mortality, and 3 studies (6 models) involved cardiovascular events. Measures of estimated glomerular filtration rate or serum creatinine level were included in 10 studies (17 models), and measures of proteinuria were included in 9 studies (15 models). Only 2 studies (4 models) met the criteria for clinical usefulness, of which 1 study (3 models) presented reclassification indices with clinically useful risk categories.
A validated risk-of-bias tool and comparisons of the performance of different models in the same validation population were lacking.
Accurate, externally validated models for predicting risk for kidney failure in patients with CKD are available and ready for clinical testing. Further development of models for cardiovascular events and all-cause mortality is needed.
Tangri N, Kitsios GD, Inker LA, Griffith J, Naimark DM, Walker S, et al. Risk Prediction Models for Patients With Chronic Kidney Disease: A Systematic Review. Ann Intern Med. 2013;158:596–603. doi: 10.7326/0003-4819-158-8-201304160-00004
Download citation file:
Published: Ann Intern Med. 2013;158(8):596-603.
Chronic Kidney Disease, Nephrology.
Results provided by:
Copyright © 2019 American College of Physicians. All Rights Reserved.
Print ISSN: 0003-4819 | Online ISSN: 1539-3704
Conditions of Use