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kidney disease, latent class analysis, quality of life



  1. Lockwood, Mark B.
  2. Lash, James P.
  3. Pauls, Heather
  4. Chung, Seon Yoon
  5. Samra, Manpreet
  6. Ryan, Catherine
  7. Park, Chang
  8. DeVon, Holli
  9. Bronas, Ulf G.


Background: Symptom burden associated with chronic kidney disease can be debilitating, with a negative effect on patient health-related quality of life. Latent class clustering analysis is an innovative tool for classifying patient symptom experience.


Objectives: The aim of the study was to identify subgroups of patients at greatest risk for high symptom burden, which may facilitate development of patient-centered symptom management interventions.


Methods: In this cross-sectional analysis, baseline data were analyzed from 3,921 adults enrolled in the Chronic Renal Insufficiency Cohort Study from 2003 to 2008. Latent class cluster modeling using 11 items on the Kidney Disease Quality of Life symptom profile was employed to identify patient subgroups based on similar observed physical symptom response patterns. Multinomial logistic regression models were estimated with demographic variables, lifestyle and clinical variables, and self-reported measures (Kidney Disease Quality of Life physical and mental component summaries and the Beck Depression Inventory).


Results: Three symptom-based subgroups were identified, differing in severity (low symptom, moderate symptom, and high symptom). After adjusting for other variables in multinomial logistic regression, membership in the high-symptom subgroup was less likely for non-Hispanic Blacks and men. Other factors associated with membership in the high-symptom subgroup included lower estimated glomerular filtration rate, history of cardiac/cardiovascular disease, higher Beck Depression Inventory scores, and lower Kidney Disease Quality of Life physical and mental component summaries.


Discussion: Three symptom subgroups of patients were identified among patients with mild-to-moderate chronic kidney disease. Several demographic and clinical variables predicted membership in subgroups. Further research is needed to determine if symptom subgroups are stable over time and can be used to predict healthcare utilization and clinical outcomes.