Impact of initial dialysis modality on the survival of patients with ESRD: a propensity-score-matched study

Abstract

Background Studies comparing the survival of hemodialysis (HD) and peritoneal dialysis (PD) patients are controversial. This study evaluated the impact of initial dialysis modality on the survival of patients with end-stage renal disease (ESRD) in a matched-pair cohort. Methods A retrospective cohort study was performed on ESRD patients who initiated renal replacement treatment between January 1, 2010, and December 31, 2018. Propensity score matching was applied to balance the baseline conditions, and multivariate Cox regression analysis was applied to compare mortality between HD and PD patients and evaluate correlations between mortality and various baseline characteristics. Subgroup analysis was performed with respect to diabetes status. Results There were 739 patients in our center in the Chinese National Renal Data System (CNRDS) between 2010 and 2018. Of these, 125 PD patients were matched with 125 HD patients. The 1-, 2-, and 3-year survival rates were 96.5%, 90.7%, and 82.5%, respectively, in the HD group and 99.5%, 97.8%, and 92.5%, respectively, in the PD group (log-rank P < 0.001). Among the propensity score-matched cohorts, no significant differences in Kaplan–Meier curves were observed between the two groups (log-rank P = 0.514). Age at dialysis initiation, CCI, congestive heart failure and cerebrovascular disease were risk factors in the multivariable-adjusted model. In subgroups defined by diabetes status, the Kaplan‒Meier survival curve showed that PD survival was significantly higher than that of HD (log-rank P = 0.022). Conclusions HD and PD were not significantly different regarding the survival of patients with ESRD. PD was associated with better survival in diabetic ESRD patients.

An Approach for Personalized Dynamic Assessment of Chronic Kidney Disease Progression Using Joint Model

“…The prediction of CKD progression and renal failure constitutes a significant clinical challenge. Current predictive methods, including machine learning models [ 22 , 23 ] and traditional statistical approaches [ 24 , 25 , 26 ], though efficacious in certain aspects, do not adequately capture the dynamic and individual variability of renal function over time. These approaches frequently overlook comprehensive consideration of time-varying biomarker data and patient survival time data.…”