Abstract: TH-PO1081
CKD Self-Management in Older Adults
Session Information
- CKD: Epidemiology, Risk Factors, Prevention - I
October 25, 2018 | Location: Exhibit Hall, San Diego Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: CKD (Non-Dialysis)
- 1901 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention
Authors
- Schrauben, Sarah J., University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
- Wright Nunes, Julie A., UM Health System, Ann Arbor, Michigan, United States
- Fischer, Michael J., University of Illinois Hospital and Health Sciences Center, Chicago, Illinois, United States
- Srivastava, Anand, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
- Tan, Thida C., Kaiser Permanente Northern California, Oakland, California, United States
- Ricardo, Ana C., University of Illinois at Chicago, Chicago, Illinois, United States
- Lash, James P., University of Illinois at Chicago, Chicago, Illinois, United States
- Wolf, Myles, Duke University, Durham, North Carolina, United States
- Anderson, Amanda Hyre, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Chen, Jing, Tulane School of Medicine, New Orleans, Louisiana, United States
- Feldman, Harold I., University of Pennsylvania, Philadelphia, Pennsylvania, United States
Background
Chronic kidney disease (CKD) patients are asked to engage in self-management behaviors in order to manage the disease and mitigate its sequelae. However, an examination of self-management behaviors and their association with clinical outcomes in older adults has not been well characterized.
Methods
Data from the Chronic Renal Insufficiency Cohort were analyzed using latent class analysis (LCA) to identify behavior patterns among those <65 and ≥65 years of age. LCA was based on body mass index, diet, physical activity, blood pressure, smoking, and hemoglobin A1c. Logistic regression assessed association of select factors to behavior patterns. Cox proportional hazards models were used to examine association of behavior patterns with cardiovascular events, CKD progression, and death. Results stratified by diabetes.
Results
Three behavior patterns (healthy, obese/sedentary, non-obese/smoking) were identified separately among age groups. Less healthy patterns included those with more depression, lower education, and self-efficacy. In older adults, less healthy patterns were associated with inadequate health literacy, less social support, and physical functioning. Among <65 years, the obese/sedentary pattern had increased hazard of death, CKD progression (diabetics), and cardiovascular events (non-diabetics), and the non-obese/sedentary pattern had an increased hazard of death (diabetics), and cardiovascular events (non-diabetics). Among 65+ years, the obese/sedentary and non-obese/smoking patterns were associated with increased death and CKD progression (non-diabetics), see Table.
Conclusion
Self-management behavior engagement patterns are associated with risk of important outcomes among CKD patients. These patterns may be able to identify high-risk groups and be targeted for aggressive management.
| <65 yrs Non-Diabetes (n=1, 484) | <65 yrs Diabetes (n=1,315) | 65+ yrs Non-Diabetes (n=547) | 65+ yrs Diabetes (n=593) | |
| Death | ||||
| Obese/Sedentary vs. Healthy Pattern | 2.17 (1.09-4.29) | 1.37 (1.01-1.86) | 2.97 (1.43-6.19) | 0.89 (0.67-1.17) |
| Non-Obese/Smoking vs. Healthy Pattern | 2.13 (0.75-6.02) | 2.50 (1.39-4.50) | 3.47 (1.48-8.11) | 1.18 (0.72-1.93) |
| CKD Progression | ||||
| Obese/Sedentary vs. Healthy Pattern | 1.22 (0.95-1.55) | 1.34 (1.13-1.59) | 1.06 (0.70-1.61) | 0.97 (0.69-1.34) |
| Non-Obese/Smoking vs. Healthy Pattern | 1.12 (0.70-1.79) | 1.46 (0.96-2.21) | 1.85 (1.07-3.22) | 0.62 (0.32-1.24) |
| Atherosclerotic Cardiovascular Event | ||||
| Obese/Sedentary vs. Healthy Pattern | 1.59 (1.04-2.43) | 1.26 (0.98-1.63) | 0.61 (0.38-1.00) | 1.12 (0.76-1.64) |
| Non-Obese/Smoking vs. Healthy Pattern | 2.97 (1.49-5.90) | 1.40 (0.75-2.61) | 1.36 (0.74-2.50) | 1.32 (0.67-2.59) |
Funding
- NIDDK Support