Predicting mortality in older adults with kidney disease: A pragmatic prediction model

Jessica Weiss, Robert W. Platt, Micah L. Thorp, Xiuhai Yang, David H. Smith, Amanda Petrik, Elizabeth Eckstrom, Cynthia Morris, Ann M. O'Hare, Eric S. Johnson

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Objectives To develop mortality risk prediction models for older adults with chronic kidney disease (CKD) that include comorbidities and measures of health status and use not associated with particular comorbid conditions (nondisease-specific measures). Design Retrospective cohort study. Setting Kaiser Permanente Northwest (KPNW) Health Maintenance Organization. Participants Individuals with severe CKD (estimated glomerular filtration rate 2; N = 4,054; n = 1,915 aged 65-79, n = 2,139 aged ≥80) who received care at KPNW between 2000 and 2008. Measurements Cox proportional hazards analysis was used to examine the association between selected participant characteristics and all-cause mortality and to generate age group-specific risk prediction models. Predicted and observed risks were evaluated according to quintile. Predictors from the Cox models were translated into a points-based system. Internal validation was used to provide best estimates of how these models might perform in an external population. Results The risk prediction models used 16 characteristics to identify participants with the highest risk of mortality at 2 years for adults aged 65 to 79 and 80 and older. Predicted and observed risks agreed within 5% for each quintile; a 4 to 5 times difference in 2-year predicted mortality risk was observed between the highest and lowest quintiles. The c-statistics for each model (0.68-0.69) indicated effective discrimination without evidence of significant overfit (slope shrinkage 0.06-0.09). Models for each age group performed similarly for mortality prediction at 6 months and 2 years in terms of discrimination and calibration. Conclusion When validated, these risk prediction models may be helpful in supporting discussions about prognosis and treatment decisions sensitive to prognosis in older adults with CKD in real-world clinical settings.

Original languageEnglish (US)
Pages (from-to)508-515
Number of pages8
JournalJournal of the American Geriatrics Society
Volume63
Issue number3
DOIs
StatePublished - Mar 1 2015

Fingerprint

Kidney Diseases
Mortality
Chronic Renal Insufficiency
Age Groups
Health Maintenance Organizations
Glomerular Filtration Rate
Proportional Hazards Models
Calibration
Health Status
Comorbidity
Cohort Studies
Retrospective Studies
Population

Keywords

  • chronic kidney disease
  • elderly
  • mortality
  • risk

ASJC Scopus subject areas

  • Geriatrics and Gerontology

Cite this

Predicting mortality in older adults with kidney disease : A pragmatic prediction model. / Weiss, Jessica; Platt, Robert W.; Thorp, Micah L.; Yang, Xiuhai; Smith, David H.; Petrik, Amanda; Eckstrom, Elizabeth; Morris, Cynthia; O'Hare, Ann M.; Johnson, Eric S.

In: Journal of the American Geriatrics Society, Vol. 63, No. 3, 01.03.2015, p. 508-515.

Research output: Contribution to journalArticle

Weiss, Jessica ; Platt, Robert W. ; Thorp, Micah L. ; Yang, Xiuhai ; Smith, David H. ; Petrik, Amanda ; Eckstrom, Elizabeth ; Morris, Cynthia ; O'Hare, Ann M. ; Johnson, Eric S. / Predicting mortality in older adults with kidney disease : A pragmatic prediction model. In: Journal of the American Geriatrics Society. 2015 ; Vol. 63, No. 3. pp. 508-515.
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abstract = "Objectives To develop mortality risk prediction models for older adults with chronic kidney disease (CKD) that include comorbidities and measures of health status and use not associated with particular comorbid conditions (nondisease-specific measures). Design Retrospective cohort study. Setting Kaiser Permanente Northwest (KPNW) Health Maintenance Organization. Participants Individuals with severe CKD (estimated glomerular filtration rate 2; N = 4,054; n = 1,915 aged 65-79, n = 2,139 aged ≥80) who received care at KPNW between 2000 and 2008. Measurements Cox proportional hazards analysis was used to examine the association between selected participant characteristics and all-cause mortality and to generate age group-specific risk prediction models. Predicted and observed risks were evaluated according to quintile. Predictors from the Cox models were translated into a points-based system. Internal validation was used to provide best estimates of how these models might perform in an external population. Results The risk prediction models used 16 characteristics to identify participants with the highest risk of mortality at 2 years for adults aged 65 to 79 and 80 and older. Predicted and observed risks agreed within 5{\%} for each quintile; a 4 to 5 times difference in 2-year predicted mortality risk was observed between the highest and lowest quintiles. The c-statistics for each model (0.68-0.69) indicated effective discrimination without evidence of significant overfit (slope shrinkage 0.06-0.09). Models for each age group performed similarly for mortality prediction at 6 months and 2 years in terms of discrimination and calibration. Conclusion When validated, these risk prediction models may be helpful in supporting discussions about prognosis and treatment decisions sensitive to prognosis in older adults with CKD in real-world clinical settings.",
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AU - Yang, Xiuhai

AU - Smith, David H.

AU - Petrik, Amanda

AU - Eckstrom, Elizabeth

AU - Morris, Cynthia

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AU - Johnson, Eric S.

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N2 - Objectives To develop mortality risk prediction models for older adults with chronic kidney disease (CKD) that include comorbidities and measures of health status and use not associated with particular comorbid conditions (nondisease-specific measures). Design Retrospective cohort study. Setting Kaiser Permanente Northwest (KPNW) Health Maintenance Organization. Participants Individuals with severe CKD (estimated glomerular filtration rate 2; N = 4,054; n = 1,915 aged 65-79, n = 2,139 aged ≥80) who received care at KPNW between 2000 and 2008. Measurements Cox proportional hazards analysis was used to examine the association between selected participant characteristics and all-cause mortality and to generate age group-specific risk prediction models. Predicted and observed risks were evaluated according to quintile. Predictors from the Cox models were translated into a points-based system. Internal validation was used to provide best estimates of how these models might perform in an external population. Results The risk prediction models used 16 characteristics to identify participants with the highest risk of mortality at 2 years for adults aged 65 to 79 and 80 and older. Predicted and observed risks agreed within 5% for each quintile; a 4 to 5 times difference in 2-year predicted mortality risk was observed between the highest and lowest quintiles. The c-statistics for each model (0.68-0.69) indicated effective discrimination without evidence of significant overfit (slope shrinkage 0.06-0.09). Models for each age group performed similarly for mortality prediction at 6 months and 2 years in terms of discrimination and calibration. Conclusion When validated, these risk prediction models may be helpful in supporting discussions about prognosis and treatment decisions sensitive to prognosis in older adults with CKD in real-world clinical settings.

AB - Objectives To develop mortality risk prediction models for older adults with chronic kidney disease (CKD) that include comorbidities and measures of health status and use not associated with particular comorbid conditions (nondisease-specific measures). Design Retrospective cohort study. Setting Kaiser Permanente Northwest (KPNW) Health Maintenance Organization. Participants Individuals with severe CKD (estimated glomerular filtration rate 2; N = 4,054; n = 1,915 aged 65-79, n = 2,139 aged ≥80) who received care at KPNW between 2000 and 2008. Measurements Cox proportional hazards analysis was used to examine the association between selected participant characteristics and all-cause mortality and to generate age group-specific risk prediction models. Predicted and observed risks were evaluated according to quintile. Predictors from the Cox models were translated into a points-based system. Internal validation was used to provide best estimates of how these models might perform in an external population. Results The risk prediction models used 16 characteristics to identify participants with the highest risk of mortality at 2 years for adults aged 65 to 79 and 80 and older. Predicted and observed risks agreed within 5% for each quintile; a 4 to 5 times difference in 2-year predicted mortality risk was observed between the highest and lowest quintiles. The c-statistics for each model (0.68-0.69) indicated effective discrimination without evidence of significant overfit (slope shrinkage 0.06-0.09). Models for each age group performed similarly for mortality prediction at 6 months and 2 years in terms of discrimination and calibration. Conclusion When validated, these risk prediction models may be helpful in supporting discussions about prognosis and treatment decisions sensitive to prognosis in older adults with CKD in real-world clinical settings.

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