Predictive Value of Objective Nutritional Indexes in Technique Failure in Peritoneal Dialysis Patients.

OBJECTIVES

Patients undergoing peritoneal dialysis (PD) will encounter with a well-recognized challenge of technique failure (TF). We aimed to explore the predictive value of objective nutritional indexes in PD TF.

METHODS

This retrospective observational study included PD patients from August 2010 to March 2019. The Controlling Nutritional Status (CONUT) score, Prognostic Nutritional Index (PNI), and Geriatric Nutritional Risk Index (GNRI) were calculated at baseline. TF was defined as a permanent switch from PD to hemodialysis. Univariate and multivariate Cox regression was performed to investigate the association between confounding factors and outcomes. The optimal cut-off values were determined using receiver operating characteristic curve analysis. We used the Kaplan-Meier curve to compare the outcomes according to the cut-off values. The area under the curve (AUC) was used to test discriminative power of these objective nutritional indexes.

RESULTS

We analyzed 276 PD patients, 84 (30.43%) experienced TF during 2.5 (1.4, 4.0) years of follow-up. In the Kaplan-Meier analysis, patients with a higher CONUT score (>3), lower GNRI (≤85.77), and lower PNI (≤40.2) had significantly higher risk of TF (38.2% vs. 18.9%, P = .011; 39.6% vs. 25.1%, P = .043; 35.9% vs. 17.9%, P = .022; respectively). After adjusting confounding factors, a high CONUT score and low PNI were independently and significantly associated with TF analyzed by a multivariate Cox regression model (hazard ratio 2.284, 95% confidence interval [CI] 1.248-4.179, P = .007; hazard ratio 2.070, 95% CI 1.233-3.475, P = .006; respectively). The largest AUC to predict TF was PNI (AUC 0.600, 95% CI 0.539-0.658), followed by CONUT score (AUC 0.596, 95% CI 0.535-0.654) and GNRI (AUC 0.572, 95% CI 0.511-0.631).

CONCLUSIONS

The CONUT score and PNI are independently associated with TF in PD patients. Moreover, assessment of PNI and the CONUT score may provide more useful predictive values than GNRI.