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Rationale & Objective: Most living kidney donors are members of a hemodialysis patient's social network. Network members are divided into core members, those strongly connected to the patient and other members; and peripheral members, those weakly connected to the patient and other members. We identify how many hemodialysis patients' network members offered to become kidney donors, whether these offers were from core or peripheral network members, and whose offers the patients accepted. Study Design: A cross-sectional interviewer-administered hemodialysis patient social network survey. Setting & Participants: Prevalent hemodialysis patients in 2 facilities. Predictors: Network size and constraint, a donation from a peripheral network member. Outcomes: Number of living donor offers, accepting an offer. Analytical Approach: We performed egocentric network analyses for all participants. Poisson regression models evaluated associations between network measures and number of offers. Logistic regression models determined the associations between network factors and accepting a donation offer. Results: The mean age of the 106 participants was 60 years. Forty-five percent were female, and 75% self-identified as Black. Fifty-two percent of participants received at least one living donor offer (range 1-6); 42% of the offers were from peripheral members. Participants with larger networks received more offers (incident rate ratio [IRR], 1.26; 95% CI, 1.12-1.42; P = 0.001), including networks with more peripheral members (constraint, IRR, 0.97; 95% CI, 0.96-0.98; P < 0.001). Participants who received a peripheral member offer had 3.6 times greater odds of accepting an offer (OR, 3.56; 95% CI, 1.15-10.8; P = 0.02) than those who did not receive a peripheral member offer. Limitations: A small sample of only hemodialysis patients. Conclusions: Most participants received at least one living donor offer, often from peripheral network members. Future living donor interventions should focus on both core and peripheral network members.
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BACKGROUND: Hemodialysis clinic patient social networks may reinforce positive and negative attitudes towards kidney transplantation. We examined whether a patient's position within the hemodialysis clinic social network could improve machine learning classification of the patient's positive or negative attitude towards kidney transplantation when compared to sociodemographic and clinical variables. METHODS: We conducted a cross-sectional social network survey of hemodialysis patients in two geographically and demographically different hemodialysis clinics. We evaluated whether machine learning logistic regression models using sociodemographic or network data best predicted the participant's transplant attitude. Models were evaluated for accuracy, precision, recall, and F1-score. RESULTS: The 110 surveyed participants' mean age was 60 ± 13 years old. Half (55%) identified as male, and 74% identified as Black. At facility 1, 69% of participants had a positive attitude towards transplantation whereas at facility 2, 45% of participants had a positive attitude. The machine learning logistic regression model using network data alone obtained a higher accuracy and F1 score than the sociodemographic and clinical data model (accuracy 65% ± 5% vs. 61% ± 7%, F1 score 76% ± 2% vs. 70% ± 7%). A model with a combination of both sociodemographic and network data had a higher accuracy of 74% ± 3%, and an F1-score of 81% ± 2%. CONCLUSION: Social network data improved the machine learning algorithm's ability to classify attitudes towards kidney transplantation, further emphasizing the importance of hemodialysis clinic social networks on attitudes towards transplant.