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Kidney Week

Abstract: FR-PO1129

Factors Associated with Variability in Home-Based Kidney Function Measures

Session Information

Category: CKD (Non-Dialysis)

  • 2301 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention

Authors

  • Larkin, Claire T., University of Illinois Chicago, Chicago, Illinois, United States
  • Appel, Lawrence J., Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Chen, Jing, The University of Texas Southwestern Medical Center, Dallas, Texas, United States
  • Cohen, Debbie L., University of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Dember, Laura M., University of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Hsu, Jesse Yenchih, University of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Hsu, Chi-yuan, University of California San Francisco, San Francisco, California, United States
  • Parsa, Afshin, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland, United States
  • Rahman, Mahboob, Case Western Reserve University, Cleveland, Ohio, United States
  • Shah, Vallabh O., The University of New Mexico, Albuquerque, New Mexico, United States
  • Unruh, Mark L., The University of New Mexico, Albuquerque, New Mexico, United States
  • Rao, Panduranga S., University of Michigan, Ann Arbor, Michigan, United States
  • Go, Alan S., The Permanente Medical Group Inc, Oakland, California, United States
  • Lash, James P., University of Illinois Chicago, Chicago, Illinois, United States
Background

Evidence suggests that kidney function variability may be associated with adverse outcomes. Between 2019-2024, 1,034 Chronic Renal Insufficiency Cohort participants completed a monthly home-based kidney function protocol consisting of fingerstick whole blood creatinine (NovaMax creatinine analyzer) and albuminuria (urine dipstick and smartphone app). The aim of this study is to identify factors associated with variability of home-based kidney function measures.

Methods

We analyzed individuals with ≥6 measures of fingerstick blood creatinine (n=847) and ≥6 albuminuria measures (n=830) during the 12-month period. Creatinine variability was defined as SD/mean; variability of albuminuria (a categorical variable) assessed using Shannon's entropy. Multivariable logistic regression evaluated the association of baseline factors with highest vs lowest variability quartile.

Results

At baseline, mean age 68 years, 47% White, 32% Black, 17% Hispanic, 53% had diabetes, 31% had CVD, mean eGFR 52.5 ml/min/1.73m2, and median protein-to-creatine ratio 0.2. Baseline fingerstick creatinine was highly correlated with the simultaneously drawn serum creatinine (Figure a). Figures b and c show distributions of fingerstick creatinine and albuminuria variability. Multivariable predictors are listed in the Table.

Conclusion

Select demographic and clinical factors were associated with variability of home-based creatinine and albuminuria. Future work is needed to assess the association of kidney function variability with long term outcomes.

Funding

  • NIDDK Support

Digital Object Identifier (DOI)