期刊
AMERICAN JOURNAL OF KIDNEY DISEASES
卷 72, 期 3, 页码 360-370出版社
W B SAUNDERS CO-ELSEVIER INC
DOI: 10.1053/j.ajkd.2018.01.047
关键词
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资金
- National Institutes of Health (NIH) [T32-DK07785, F32-DK113681-01A1]
- National Institute of Diabetes and Digestive and Kidney Diseases [U01 DK060990, U01 DK060984, U01DK061022, U01DK061021, U01DK061028, U01 DK060980, U01 DK060963, U01 DK060902]
- Perelman School of Medicine at the University of Pennsylvania Clinical and Translational Science Award (CTSA) NIH/National Center for Advancing Translational Sciences (NCATS) [UL1TR000003]
- Johns Hopkins University [UL1 TR-000424]
- University of Maryland [GCRC M01 RR-16500]
- Clinical and Translational Science Collaborative of Cleveland from the NCATS [UL1TR000439]
- NIH roadmap for Medical Research
- Michigan Institute for Clinical and Health Research (MICHR) [UL1TR000433]
- University of Illinois at Chicago [CTSA UL1 RR029879]
- Tulane COBRE for Clinical and Translational Research in Cardiometabolic Diseases [P20 GM109036]
- Kaiser Permanente NIH/National Center for Research Resources [UCSF-CTSI UL1 RR-024131]
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 selfmanagement 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 selfmanagement behavior phenotypes that distinguish risk for clinical outcomes. These phenotypes may inform the development of studies and guidelines regarding optimal selfmanagement.
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