Comparing Five Comorbidity Indices to Predict Mortality in Chronic Kidney Disease: A Retrospective Cohort Study

Eric McArthur1, Sarah E Bota1, Manish M Sood1,2

  • 1Institute for Clinical Evaluative Sciences, London, ON, Canada.

Insights

Existing comorbidity indices fail to accurately predict mortality in patients with chronic kidney disease (CKD). New or modified indices are needed to improve risk assessment for this population.

Area of Science:

  • Nephrology
  • Epidemiology
  • Health Services Research

Background:

  • Comorbidity indices are used to assess patient risk and adjust analyses.
  • Their performance in chronic kidney disease (CKD) populations is not well understood.

Purpose of the Study:

  • To evaluate five common comorbidity indices for predicting 1-year mortality.
  • The study focused on three CKD patient groups: kidney transplant recipients, maintenance dialysis patients, and those with low estimated glomerular filtration rate (eGFR).

Main Methods:

  • A population-based retrospective cohort study was conducted in Ontario, Canada (2004-2014).
  • Five indices (Charlson, CKD-modified Charlson, Johns Hopkins AGD, Elixhauser, Wright-Khan) were assessed.
  • Model discrimination (c-statistics) and calibration were evaluated using derivation and validation samples.

Main Results:

  • The study included over 200,000 patients across the three CKD groups.
  • All five comorbidity indices demonstrated inadequate discrimination (median c-statistics < 0.7) for predicting 1-year mortality in all groups.
  • Calibration of the models was also poor across all assessed indices and patient groups.

Conclusions:

  • Current administrative data-based comorbidity indices do not accurately predict mortality in CKD patients.
  • There is a need to develop new indices or modify existing ones with additional risk factors specific to CKD populations.
  • Improved indices are crucial for better risk profiling and adjustment in research involving CKD patients.
Abstract

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