MO669: Predictive Parameters of Automated PD Cycler Software for Diagnosis of Catheter Dysfunction

Abstract

Abstract BACKGROUND AND AIMS An adequately functioning catheter is of vital importance for successful long-term treatment and technique survival to peritoneal dialysis (PD) patients. According to the 2021 International Society of Peritoneal Dialysis guidelines on membrane function, catheter drainage problems should be suspected first in patients with low ultrafiltration (UF) capacity. Catheter dysfunction and the consequent decrease in UF are frequent complications in PD patients associated with significant negative clinical outcomes. METHOD We introduce the use of a combination of parameters obtained from daily measurements of the cycler software PD Link (Baxter) for predicting catheter dysfunction in automated peritoneal dialysis (APD) patients. Out of 117 APD patients treated at the Medical University of Vienna from 2015 to 2021, we identified all patients with verified catheter malposition and dysfunction (n = 14, cases) and compared them to controls without clinical evidence of mechanical problems and a recent X-ray confirming position of the PD catheter tip in the recto-vesical/recto-uterine space (n = 19). We retrieved cycler data for 7 days each and tested parameters predictive capability of catheter dysfunction. RESULTS Total number of alarms/week >7 as single predictive parameter of catheter dislocation identified 85.7% (sensitivity) of patients with dislocated catheter and 31.6% (1—specificity) of control patients. A combination of two out of three parameters (number of alarms/week >7, drain time >22 min, UF of last fill <150 mL) identified the same proportion of patients with catheter dislocation, but was more accurate in identifying controls (21% false positive). CONCLUSION We confirmed our clinical impression that number of alarms, total drain time at the end of cycler session and UF of last fill are good predictors for mechanical drainage problems. In contrast to yearly peritoneal equilibration test measurements, these data provide valuable information for clinical management of PD patients on a daily base and will be easily available in new APD cyclers with devices connected to remote monitoring platforms.