Impact of time-varying center volume on technique failure and mortality in peritoneal dialysis

Abstract

Background: Most studies on volume–outcome association used the number of patients at a particular period as the independent variable. However, peritoneal dialysis (PD) is a chronic treatment, and center volume usually changes over a patient’s treatment period. Accordingly, this study used the time-varying center volume to explore the volume–outcome association in PD. Methods: We conducted a nationwide population-based retrospective cohort study, which included patients who began chronic PD between 2001 and 2010. The risk factors of 5-year technique failure and mortality were analyzed using cause-specific and subdistribution hazard models, respectively. The annual number of patients initiating PD in each patient’s treatment center was modeled as a time-varying variable with four categories. Results: We included 9071 patients who started PD in 100 centers where the number of incident patients ranged from 1 to 107 patients per year (median, 25; interquartile range, 13–42). The estimated 5-year patient and technique survival rates were 64.7% and 66.6%, respectively. Being treated in centers in the largest volume category (the number of incident PD patients ≥43 per year) was associated with significantly lower cause-specific and cumulative hazards for technique failure. No association was found between facility volume and hazards of mortality. Conclusions: Receiving PD in high-volume facilities was associated with a lower risk in technique failure. No association was found between facility volume and mortality risk.

Initial Home Dialysis Is Increased for Rural Patients by Accessing Urban Facilities

“…Additionally, incentivizing small rural facilities could have the unintended consequence of reducing individual facility expertise, which may expose patients to higher risks of technique failure. 22 Our analysis is limited by its retrospective nature, which cannot establish causal relationships. There is not a reliable way to account for why patients end up at a particular dialysis facility, or why they change modalities, but there is evidence from this study, others in literature, as well as those in access to transplantation, that the dialysis facility itself played a significant role in care and outcomes for these patients.…”

“…In that hypothetical case, travel would likely decrease and patient experience and quality of care would likely improve, but fully estimating these effects is outside of the scope of the current study, given the uncertain future course. Additionally, incentivizing small rural facilities could have the unintended consequence of reducing individual facility expertise, which may expose patients to higher risks of technique failure (22). Our analysis is limited by its retrospective nature, which cannot establish causal relationships.…”

The mean residual life model for the right‐censored data in the presence of covariate measurement errors

“…Taiwan has the highest prevalence of end-stage renal disease (ESRD) in the world 15 and most of CKD5-ND patients receive dialysis to save their lives. 16 To manage the annual budget for CKD5-ND treatment, it is important for health policy makers to estimate the expected remaining time to dialysis or death and evaluate the effects of risk factors for CKD5-ND patients. One suitable way for the aim is the mean residual life (MRL) model, which focuses on the expected additional lifetime conditional on that a subject has survived until a given time.…”

“…This article is motivated by data from patients with non‐dialysis chronic kidney disease stage 5 (CKD5‐ND) treated at outpatient clinics in the Division of Nephrology of Taichung Veterans General Hospital, Taichung, Taiwan. Taiwan has the highest prevalence of end‐stage renal disease (ESRD) in the world 15 and most of CKD5‐ND patients receive dialysis to save their lives 16 . To manage the annual budget for CKD5‐ND treatment, it is important for health policy makers to estimate the expected remaining time to dialysis or death and evaluate the effects of risk factors for CKD5‐ND patients.…”