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Abstract: SA-PO0427

Monitoring and Quantifying Cloudiness Levels of Spent Peritoneal Dialysis Effluent Using a Light-Based Smartphone Application

Session Information

Category: Dialysis

  • 802 Dialysis: Home Dialysis and Peritoneal Dialysis

Authors

  • Kakembo, Mark, Fresenius Medical Care Holdings Inc, New York, New York, United States
  • Garbaccio, Mia Genevieve, Renal Research Institute, New York, New York, United States
  • Tisdale, Lela, Fresenius Medical Care Holdings Inc, New York, New York, United States
  • Grobe, Nadja, Fresenius Medical Care Holdings Inc, New York, New York, United States
  • Tao, Xia, Renal Research Institute, New York, New York, United States
  • Haq, Zahin Sultana, Renal Research Institute, New York, New York, United States
  • Ferreira Dias, Gabriela, Renal Research Institute, New York, New York, United States
  • Wang, Xin, Fresenius Medical Care Holdings Inc, New York, New York, United States
  • Wang, Xiaoling, Fresenius Medical Care Holdings Inc, New York, New York, United States
  • Kotanko, Peter, Renal Research Institute, New York, New York, United States
Background

Peritonitis is a major complication of peritoneal dialysis (PD), with early detection crucial for patient outcomes. Clinicians still rely on subjective methods—visual inspection of cloudy spent PD effluent (PDE) and reported symptoms—to screen for infection. However, objective assessment of PDE cloudiness remains challenging. We evaluated a smartphone app that quantifies PDE cloudiness as a percentage score.

Methods

The PD app leverages a standard smartphone light sensor to detect ambient light (Lamb) and light transversing after mounting PDE drain bag (Lbag) across the light sensor. Cloudiness score (CS, %) is calculated as follows: 100 × (1 – Lbag/Lamb). From two multicenter clinical studies, 117 PDE bags were collected from 26 patients and tested in a lab (lab cohort; Figure 1A). Separately, two patients used the PD app at home for at least 7 consecutive weeks (self-monitoring cohort).

Results

In the lab cohort (N=26; age 57.5±4.5 years; 11F), 113 bags were collected from confirmed asymptomatic PD visits with a mean CS of 18.9±3.6% (range: 8.0–30.3%; Figure 1A, B). Of the remaining 4 lab cohort PDE bags: one asymptomatic but suspected (CS 26.7%), one unconfirmed peritonitis case (CS 24.0%), and two with clinically diagnosed peritonitis (CS, 32.3% and 40.3%). In the self-monitoring cohort, one CAPD patient recorded CS of 16.0±10.1% (range: 0–38%; Figure 1C); another on CAPD and CCPD recorded 31±0.3% (range: 0–77%) and 23±0.3% (range: 0–82%), respectively (Figure 1D).

Conclusion

The PD app demonstrated reliable quantification of PDE cloudiness both in lab and home settings. Broader clinical validation is warranted to establish the app as an accessible, low-cost tool for early peritonitis screening in PD patients.

Funding

  • Commercial Support – Renal Research Institute

Digital Object Identifier (DOI)