Format

Send to

Choose Destination
Mayo Clin Proc. 2018 Sep;93(9):1224-1235. doi: 10.1016/j.mayocp.2018.04.017. Epub 2018 Aug 10.

Development and Validation of Prediction Scores for Early Mortality at Transition to Dialysis.

Author information

1
Harold Simmons Center for Kidney Disease Research and Epidemiology, Division of Nephrology and Hypertension, University of California, Irvine Medical Center, Orange, CA.
2
Division of General Internal Medicine and Primary Care, University of California, Irvine Medical Center, Orange, CA.
3
Kaiser Permanente Southern California, Pasadena, CA.
4
Institute for Clinical and Translational Science, University of California, Irvine, CA.
5
Harold Simmons Center for Kidney Disease Research and Epidemiology, Division of Nephrology and Hypertension, University of California, Irvine Medical Center, Orange, CA; Nephrology Section, Tibor Rubin Veterans Affairs Medical Center, Long Beach, CA.
6
Division of Nephrology, Department of Medicine, University of Tennessee Health Science Center, Memphis, TN; Division of Transplant Surgery, Methodist University Hospital Transplant Institute, Memphis, TN; Division of Transplant Surgery, Department of Surgery, University of Tennessee Health Science Center, Memphis, TN; Department of Transplantation and Surgery, Semmelweis University, Budapest, Hungary.
7
National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD.
8
Division of Nephrology, Department of Medicine, University of Tennessee Health Science Center, Memphis, TN; Nephrology Section, Memphis Veterans Affairs Medical Center, Memphis, TN.
9
Harold Simmons Center for Kidney Disease Research and Epidemiology, Division of Nephrology and Hypertension, University of California, Irvine Medical Center, Orange, CA; Nephrology Section, Tibor Rubin Veterans Affairs Medical Center, Long Beach, CA; Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA. Electronic address: kkz@uci.edu.

Abstract

OBJECTIVE:

To develop and validate a risk prediction model that would help individualize treatment and improve the shared decision-making process between clinicians and patients.

PATIENTS AND METHODS:

We developed a risk prediction tool for mortality during the first year of dialysis based on pre-end-stage renal disease characteristics in a cohort of 35,878 US veterans with incident end-stage renal disease who transitioned to dialysis treatment between October 1, 2007, and March 31, 2014 and then externally validated this tool among 4284 patients in the Kaiser Permanente Southern California (KPSC) health care system who transitioned to dialysis treatment between January 1, 2007, and September 30, 2015.

RESULTS:

To ensure model goodness of fit, 2 separate models were selected for patients whose last estimated glomerular filtration rate (eGFR) before dialysis initiation was less than 15 mL/min per 1.73 m2 or 15 mL/min per 1.73 m2 or higher. Model discrimination in the internal validation cohort of veterans resulted in C statistics of 0.71 (95% CI, 0.70-0.72) and 0.66 (95% CI, 0.65-0.67) among patients with eGFR lower than 15 mL/min per 1.73 m2 and 15 mL/min per 1.73 m2 or higher, respectively. In the KPSC external validation cohort, the developed risk score exhibited C statistics of 0.77 (95% CI, 0.74-0.79) in men and 0.74 (95% CI, 0.71-0.76) in women with eGFR lower than 15 mL/min per 1.73 m2 and 0.71 (95% CI, 0.67-0.74) in men and 0.67 (95% CI, 0.62-0.72) in women with eGFR of 15 mL/min per 1.73 m2 or higher.

CONCLUSION:

A new risk prediction tool for mortality during the first year after transition to dialysis (available at www.DialysisScore.com) was developed in the large national Veterans Affairs cohort and validated with good performance in the racially, ethnically, and gender diverse KPSC cohort. This risk prediction tool will help identify high-risk populations and guide management strategies at the transition to dialysis.

PMID:
30104041
DOI:
10.1016/j.mayocp.2018.04.017
[Indexed for MEDLINE]
Free full text

Supplemental Content

Full text links

Icon for Elsevier Science Icon for eScholarship, California Digital Library, University of California
Loading ...
Support Center