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Abstract: PO0724

COVID-19 Infection Patterns in an Academic Inner City Dialysis Unit

Session Information

Category: Coronavirus (COVID-19)

  • 000 Coronavirus (COVID-19)

Authors

  • Srinivasan, Vinay, University of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Ahmad, Sarah M., University of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Aggarwal, Sandeep, University of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Wahba, Ihab M., University of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Huan, Yonghong, University of Pennsylvania, Philadelphia, Pennsylvania, United States
Background

COVID-19 remains a major public health emergency and in-center dialysis provides multiple opportunities for its spread. Elderly immunocompromised hosts pose a significant risk for infection as well as poor outcomes. We present a retrospective analysis of COVID-19 cases in our dialysis unit.

Methods

Retrospective analysis was done as a part of a quality improvement project using unidentified patient data including: demographics, distribution of dialysis shift, patient zip code, transportation mode (self, ride share or public transport), residence type (home, long term care facility or homeless shelter), etiology of ESRD and dialysis vintage. T-test and multivariate analysis (including logistic regression for binary and categorical data) were conducted using SPSS v23.

Results

There were 70 patients in the unit and 10 became positive for COVID-19. 65/70 (92%) of all patients were African American. Between COVID-19 positive and negative patients, there was no significant difference in age (62±15 vs 63±14 years p=0.2), dialysis vintage (7.6±8.7 vs 5.2±4.7 years p=0.31), male gender (7/10 (70%) vs 40/70 (58%) p=0.31). 5/10 (50%) of the positive patients were MWF 2nd shift. On multivariate analysis, this effect approached significance (p=0.051); however, there was no interaction of COVID-19 positive status with demographic characteristics, dialysis vintage, residence type, zip code distribution, or transportation modality. Of note, universal masking and temperature screening were implemented on March 5, 2020 in this unit and no new cases were noted after May 2, 2020.

Conclusion

Our analysis did not show any clear factor associated with COVID-19 infection among our dialysis patients although clustering approached statistical significance. Small sample size and demographic distribution are shortcomings of our study; larger scale epidemiological studies and data analysis are required for better understanding the risk of COVID-19 infection amongst in-center dialysis patients.

Chronological Distrubtion of COVID-19 Cases