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Am J Kidney Dis. 2018 Sep;72(3):360-370. doi: 10.1053/j.ajkd.2018.01.047. Epub 2018 Mar 24.

CKD Self-management: Phenotypes and Associations With Clinical Outcomes.

Author information

1
Division of Renal, Electrolyte, and Hypertension, University of Pennsylvania, Philadelphia, PA. Electronic address: sarah.schrauben@uphs.upenn.edu.
2
Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA.
3
Joslin Diabetes Center, Harvard Medical School, Boston, MA.
4
Division of Nephrology, Johns Hopkins University, Baltimore, MD; Welch Center for Prevention, Epidemiology & Clinical Research, Johns Hopkins University, Baltimore, MD.
5
Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA; Division of Cardiology, University of Pennsylvania, Philadelphia, PA.
6
Division of Nephrology, MetroHealth Medical Center, Case Western Reserve University, Cleveland, OH.
7
Department of Medicine, Tulane University, New Orleans, LA.
8
Department of Medicine, University of Texas Southwestern, Dallas, TX.
9
Department of Medicine, Case Western University, Cleveland, OH.
10
Department of Medicine, University of Illinois at Chicago, Chicago, IL.
11
Division of Renal, Electrolyte, and Hypertension, University of Pennsylvania, Philadelphia, PA; Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA.

Abstract

BACKGROUND:

To slow chronic kidney disease (CKD) progression and its complications, patients need to engage in self-management behaviors. The objective of this study was to classify CKD self-management behaviors into phenotypes and assess the association of these phenotypes with clinical outcomes.

STUDY DESIGN:

Prospective cohort study.

SETTING & PARTICIPANTS:

Adults with mild to moderate CKD enrolled in the Chronic Renal Insufficiency Cohort (CRIC) Study. 3,939 participants in the CRIC Study recruited between 2003 and 2008 served as the derivation cohort and 1,560 participants recruited between 2013 and 2015 served as the validation cohort.

PREDICTORS:

CKD self-management behavior phenotypes.

OUTCOMES:

CKD progression, atherosclerotic events, heart failure events, death from any cause.

MEASUREMENTS:

Latent class analysis stratified by diabetes was used to identify CKD self-management phenotypes based on measures of body mass index, diet, physical activity, blood pressure, smoking status, and hemoglobin A1c concentration (if diabetic); Cox proportional hazards models.

RESULTS:

3 identified phenotypes varied according to the extent of implementation of recommended CKD self-management behaviors: phenotype I characterized study participants with the most recommended behaviors; phenotype II, participants with a mixture of recommended and not recommended behaviors; and phenotype III, participants with minimal recommended behaviors. In multivariable-adjusted models for those with and without diabetes, phenotype III was strongly associated with CKD progression (HRs of 1.82 and 1.49), death (HRs of 1.95 and 4.14), and atherosclerotic events (HRs of 2.54 and 1.90; each P < 0.05). Phenotype II was associated with atherosclerotic events and death among those with and without diabetes.

LIMITATIONS:

No consensus definition of CKD self-management; limited to baseline behavior data.

CONCLUSIONS:

There are potentially 3 CKD self-management behavior phenotypes that distinguish risk for clinical outcomes. These phenotypes may inform the development of studies and guidelines regarding optimal self-management.

KEYWORDS:

CKD progression: cardiovascular outcomes; Chronic renal failure; all-cause death; atherosclerotic events; blood pressure control; chronic kidney disease (CKD); diabetes; healthy behaviors; heart failure; modifiable risk factors; patient engagement; self-care; self-management; smoking

PMID:
29580660
PMCID:
PMC6109611
[Available on 2019-09-01]
DOI:
10.1053/j.ajkd.2018.01.047

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