Meet ApplyKite Twin, the application assistant that learns with you
Meet your Twin, it learns with you
Your Twin learns with you
Launch week codes:30% off annual Pro20% off monthly Pro
Ends in00h00m00s
Ends in00h00m00s
Peter Reese
Professor
Research Interests
Explore related searches
Articles (17)
Year:
2026
Year:
2026
Year:
2026
Year:
2026
Year:
2026
Year:
2026
Year:
2025
Year:
2025
Key Points Hemodialysis clinic social networks spread attitudes and behaviors toward kidney transplants. Identifying and characterizing influential patients is a first step in future hemodialysis clinic social network interventions to promote kidney transplantation. Background Hemodialysis clinics help develop patient social networks that may spread kidney transplant (KT) attitudes and behaviors. Identifying influential social network members is an important first step to increase KT rates. We mapped the social networks of two hemodialysis facilities to identify which patients were influential using in-degree centrality as a proxy for popularity and influence. Methods In this cross-sectional study, we performed a sociocentric social network analysis of patients on hemodialysis in two geographically and demographically different hemodialysis facilities. Statistical and social network analyses were performed using R statistical software. Results More patients at facility 1 ( N =71) were waitlisted/evaluating living donor KT (50.7% versus 20.0%, P = 0.021), considered KT as very important (70.4% versus 45.0%, P = 0.019), and knew people who received a successful KT (1.0 versus 0.0, P = 0.003). Variables predicting relationship formation at facility 1 were the same shift ( β =1.87, 95% confidence interval [CI] [1.19 to 2.55]; P < 0.0001), same sex ( β =0.51, 95% CI [0.01 to 1.00]; P = 0.045), younger age ( β =−0.03, 95% CI [−0.05 to −0.01]; P = 0.004), different lengths of time on hemodialysis ( β =−0.49, 95% CI [−0.86 to −0.12]; P = 0.009), and knowing more people who received a successful KT ( β =0.12, 95% CI [0.03 to 0.21]; P = 0.009). Predictive variables at facility 2 ( N =40) were the same race ( β =2.52, 95% CI [0.39 to 4.65]; P = 0.021) and knowing fewer people with successful KT ( β =−0.92, 95% CI [−1.82 to −0.02]; P = 0.045). In-degree centrality was higher at facility 1 (1.1±1.2) compared with facility 2 (0.6±0.9). Conclusions Social networks differed between the hemodialysis clinics in structure and prevalent transplant attitudes. Influential patients at facility 1 (measured by in-degree centrality) had positive attitudes toward KT, whereas influential patients at facility 2 had negative attitudes.
Year:
2024
Year:
2024
Year:
2024
Year:
2024
Year:
2024
Year:
2023
Year:
2023
Year:
2023
Year:
2023
Collaborators (19)
Harald Schmidt
Assistant Professor, Clinician-Educator
University of Pennsylvania
Matthew Cooper
Medical College of Wisconsin
John Friedewald
Professor
Northwestern University Feinberg School of Medicine
Vineeta Kumar
Professor of Medicine, Endowed Professor of Transplant Nephrology
The University of Alabama at Birmingham School of Medicine
Benjamin Lok
Professor
University of Florida
Andrew Adams
Professor
University of Minnesota
Ciara Shaver
Assistant Professor
Vanderbilt University Medical Center
Angie Gabriela Nishio Lucar
Associate Professor
University of Virginia
Mona D. Doshi
University of Michigan
Neetika Garg
University of Wisconsin-Madison
Michael S. Wolf
Northwestern University
Vishnu Potluri
University of Pennsylvania
Matthew Bacchetta
Vanderbilt University Medical Center
Avrum Gillespie
Associate Professor of Medicine
Temple University Hospital
Catherine R. Butler
University of Washington
Xingxing Cheng
Stanford University Medical Center
Allan Kirk
Duke University
Emily Vail
Assistant professor
University of Pennsylvania
Heather Thiessen Philbrook
Johns Hopkins University
Make your next move.
Prepare your application
Review drafts before you send them.