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

Yoshitsugu Obi, Danh V. Nguyen, Hui Zhou, Melissa Soohoo, Lishi Zhang, Yanjun Chen, Elani Streja, John J. Sim, M. Molnár, Connie M. Rhee, Kevin C. Abbott, Steven J. Jacobsen, Csaba P. Kovesdy, Kamyar Kalantar-Zadeh

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1224-1235
Number of pages12
JournalMayo Clinic Proceedings
Volume93
Issue number9
DOIs
Publication statusPublished - Sep 1 2018

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Dialysis
Glomerular Filtration Rate
Mortality
Veterans
Chronic Kidney Failure
Decision Making
Therapeutics
Delivery of Health Care
Kidney
Population

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Obi, Y., Nguyen, D. V., Zhou, H., Soohoo, M., Zhang, L., Chen, Y., ... Kalantar-Zadeh, K. (2018). Development and Validation of Prediction Scores for Early Mortality at Transition to Dialysis. Mayo Clinic Proceedings, 93(9), 1224-1235. https://doi.org/10.1016/j.mayocp.2018.04.017

Development and Validation of Prediction Scores for Early Mortality at Transition to Dialysis. / Obi, Yoshitsugu; Nguyen, Danh V.; Zhou, Hui; Soohoo, Melissa; Zhang, Lishi; Chen, Yanjun; Streja, Elani; Sim, John J.; Molnár, M.; Rhee, Connie M.; Abbott, Kevin C.; Jacobsen, Steven J.; Kovesdy, Csaba P.; Kalantar-Zadeh, Kamyar.

In: Mayo Clinic Proceedings, Vol. 93, No. 9, 01.09.2018, p. 1224-1235.

Research output: Contribution to journalArticle

Obi, Y, Nguyen, DV, Zhou, H, Soohoo, M, Zhang, L, Chen, Y, Streja, E, Sim, JJ, Molnár, M, Rhee, CM, Abbott, KC, Jacobsen, SJ, Kovesdy, CP & Kalantar-Zadeh, K 2018, 'Development and Validation of Prediction Scores for Early Mortality at Transition to Dialysis', Mayo Clinic Proceedings, vol. 93, no. 9, pp. 1224-1235. https://doi.org/10.1016/j.mayocp.2018.04.017
Obi, Yoshitsugu ; Nguyen, Danh V. ; Zhou, Hui ; Soohoo, Melissa ; Zhang, Lishi ; Chen, Yanjun ; Streja, Elani ; Sim, John J. ; Molnár, M. ; Rhee, Connie M. ; Abbott, Kevin C. ; Jacobsen, Steven J. ; Kovesdy, Csaba P. ; Kalantar-Zadeh, Kamyar. / Development and Validation of Prediction Scores for Early Mortality at Transition to Dialysis. In: Mayo Clinic Proceedings. 2018 ; Vol. 93, No. 9. pp. 1224-1235.
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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.",
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AU - Nguyen, Danh V.

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AU - Zhang, Lishi

AU - Chen, Yanjun

AU - Streja, Elani

AU - Sim, John J.

AU - Molnár, M.

AU - Rhee, Connie M.

AU - Abbott, Kevin C.

AU - Jacobsen, Steven J.

AU - Kovesdy, Csaba P.

AU - Kalantar-Zadeh, Kamyar

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N2 - 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.

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