Effect of Renin - Angiotensin - Aldosterone System Blockade on Outcomes in Patients With ESRD: A Prospective Cohort Study in Korea
Authors
Kyung Don Yoo ; Clara Tammy Kim ; Yunmi Kim ; Hyo Jin Kim ; Jae Yoon Park ; Ji In Park ; Yun Kyu Oh ; Shin-Wook Kang ; Chul Woo Yang ; Yong-Lim Kim ; Yon Su Kim ; Chun Soo Lim ; Jung Pyo Lee
Citation
KIDNEY INTERNATIONAL REPORTS, Vol.3(6) : 1385-1393, 2018-08
Introduction: Conflicting results still exist regarding the benefit of renin-angiotensin-aldosterone system (RAAS) blockade on clinical outcomes in dialysis patients. The aim of this study was to evaluate the effects of RAAS blockade on survival in Korean patients with end-stage renal disease (ESRD).
Methods: Our analysis was based on the data of 5223 patients enrolled from the Clinical Research Center for ESRD, a nationwide prospective observational cohort. Multivariate Cox regression was applied for risk factor analysis with the cumulative duration of RAAS blockade use as time-varying covariate. The risks for mortality from all causes and major cardiovascular event-free survival were estimated.
Results: Compared to the control group, patients in the RAAS group were younger but had a higher proportion of diabetes mellitus, had higher systolic blood pressure, required a greater number of prescribed antihypertensive drugs, and had a longer dialysis duration. On multivariate time-varying Cox regression analysis, the RAAS group with cumulative duration of >90 days was significantly associated with a lower risk of mortality from all causes after adjustment for confounding (hazard ratio [HR] = 0.45, 95% confidence interval [CI] = 0.35-0.58, P < 0.0001). Major cardiovascular event-free survival was also better for the RAAS group than for the control group on multivariate analysis (HR = 0.27, 95% CI = 0.20-0.37, P < 0.0001), considering the cumulative duration of RAAS blockade use.
Conclusion: In Korean patients with ESRD, we reported a specific benefit of RAAS blockade in improving overall survival after adjustment for confounding factors from real-world data.