Physical Symptom Cluster Subgroups in Chronic Kidney Disease

Area of Science:

• Nephrology
• Psychology
• Biostatistics

Background:

• Chronic kidney disease (CKD) significantly impacts patients' quality of life due to debilitating symptom burden.
• Latent class clustering analysis offers an innovative approach to classifying patient symptom experiences in CKD.

Purpose of the Study:

• To identify patient subgroups with the highest symptom burden in CKD.
• To facilitate the development of patient-centered symptom management interventions for CKD.

Main Methods:

• A cross-sectional analysis of 3,921 adults from the Chronic Renal Insufficiency Cohort Study (2003-2008).
• Latent class cluster modeling using 11 Kidney Disease Quality of Life symptom items to identify subgroups.
• Multinomial logistic regression analyzed demographic, lifestyle, clinical, and self-reported measures (e.g., Beck Depression Inventory).

Main Results:

• Three distinct symptom subgroups were identified: low, moderate, and high symptom burden.
• High symptom burden was associated with lower estimated glomerular filtration rate, cardiac/cardiovascular disease history, higher Beck Depression Inventory scores, and lower Kidney Disease Quality of Life summary scores.
• Non-Hispanic Blacks and men were less likely to be in the high-symptom subgroup after adjustment.

Conclusions:

• Three symptom subgroups of patients with mild-to-moderate CKD were identified.
• Demographic and clinical variables predicted subgroup membership.
• Further research is needed to assess subgroup stability and predictive value for healthcare utilization and outcomes.