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Abstract: TH-OR141

Assessment of Fistula Flow Using Smartphone Video Analysis

Session Information

Category: Dialysis

  • 704 Dialysis: Vascular Access

Authors

  • Zhu, Fansan, Renal Research Institute, New York, New York, United States
  • Wang, Lin-Chun, Renal Research Institute, New York, New York, United States
  • Thwin, Ohnmar, Renal Research Institute, New York, New York, United States
  • Lin, Karen, Renal Research Institute, New York, New York, United States
  • Tao, Xia, Renal Research Institute, New York, New York, United States
  • Patel, Samir D., Renal Research Institute, New York, New York, United States
  • Maheshwari, Vaibhav, Renal Research Institute, New York, New York, United States
  • Garbaccio, Mia Genevieve, Renal Research Institute, New York, New York, United States
  • Thijssen, Stephan, Renal Research Institute, New York, New York, United States
  • Preddie, Dean C., Azura Vascular Care, Brooklyn, New York, United States
  • Shtaynberg, Norbert, Azura Vascular Care, Brooklyn, New York, United States
  • Karimi, Kamran, Azura Vascular Care, Brooklyn, New York, United States
  • Sallit, Shadi, Azura Vascular Care, Brooklyn, New York, United States
  • Kotanko, Peter, Renal Research Institute, New York, New York, United States
Background

Frequent, non-invasive assessment of arterio-venous fistula (AVF) blood flow (ABF) is desirable to timely detect access malfunction. Given the almost universal availability of smartphones with video capabilities, we assessed ABF by video image processing.

Methods

Fify-eight hemodialysis patients were studied after endovascular interventions where ABF was measured invasively using thermodilution (HVT100, Transonic, Ithaca, NY). A 1-minute video of the skin above the AVF was recorded (iPhone 6; Fig.1). Frame-to-frame pixel changes were quantified and followed over time. These time series were then transformed into the frequency domain. The frequency domain signal was characterized by the squared ratio of the maximal-to-median magnitude (M2; an example is shown in Fig.2). Forty randomly selected patients (derivation cohort) were used to construct multiple regression models with ABF as dependent variable, 18 patients served as validation cohort.

Results

Visual inspection of the derivation cohort ABF vs. M2 scatterplot indicated 2 distinct populations, one with M2≤100 (n=30) and M2>100 (n=10; Table 1). In the validation cohort 17 patients had M2≤100, and 1 patient >100. In the derivation cohort a multiple regression model including M2, sex, body weight, and race explained 64% of the ABF variance in patients with M2≤100 (Fig 3A) and 44% in patients with M2>100 (Fig. 3B), respectively. In the validation cohort we predicted 51% of the ABF variance in patients with M2≤100 (Fig 4); the one patient with M2>100 was not analyzed.

Conclusion

Our results show that advanced mathematical analysis of smartphone videos may have the potential to assess AVF blood flow. If corroborated in more extensive clinical studies, smartphone video analysis provides an attractive and low-cost means to non-invasively evaluate AVF blood flow.

Table and Figures

Funding

  • NIDDK Support