Smith, Byron

Outcomes of kidney-alone transplantation (KAT) in former simultaneous liver-kidney (SLK) transplant candidates after liver waitlist delisting are not well defined. Using Scientific Registry of Transplant Recipients (SRTR) data, we identified adult recipients who underwent KAT between 2005 and 2025 after prior SLK listing (RLD-KAT), which was matched 1:5 to primary KAT recipients without liver disease by age, transplant year, and diabetes status. Patient and graft survival were evaluated using the Kaplan-Meier method and multivariable Cox proportional hazards models. We identified 163 RLD-KAT recipients matched to 815 controls. Unadjusted Kaplan-Meier analyses demonstrated lower patient survival in RLD-KAT (p = 0.020), whereas death-censored and overall graft survival were similar (p > 0.05). In multivariable models, death-censored graft survival (HR 0.89; 95% CI 0.51–1.53), overall graft survival (HR 1.00; 95% CI 0.73–1.36), and patient survival (HR 1.07; 95% CI 0.76–1.5) did not differ significantly. Six RLD-KAT recipients (3.7%) subsequently underwent liver transplantation. Kidney-alone transplantation in prior SLK candidates was associated with acceptable patient and graft outcomes and a low incidence of subsequent liver transplantation.

Abstract Frailty influences treatment outcomes in older patients with multiple myeloma (MM), yet existing frailty tools capture limited geriatric domains and are incompletely studied in the context of contemporary therapies. We prospectively evaluated patient-reported comprehensive geriatric assessment (CGA) domains and their associations with treatment toxicity and patient-reported outcomes (PRO) in older adults with MM. Patients aged ≥60 years with newly diagnosed or relapsed/refractory MM initiating a new therapy completed electronic CGA and PRO questionnaires at baseline and 4 months. Outcomes included clinician- and patient-reported treatment toxicity, treatment adaptations, health care use, and health-related quality of life (HRQOL). A cumulative deficit frailty index (FI) was derived from CGA and HRQOL domains. Among 90 patients (mean age, 72 years), geriatric impairments were common, particularly physical function domains. High-grade nonhematologic toxicities were significantly associated with older age, comorbidities, patient-reported functional limitations, and anxiety. Toxicity rates on the PRO version of the Common Terminology Criteria for Adverse Events (CTCAE) were higher than on CTCAE, highlighting a discordance between physician- and patient-reported treatment toxicity. Frailty was present in 22% and associated with worse baseline HRQOL and increased high-grade nonhematologic toxicity but not treatment adaptations or health care use. Average CGA and HRQOL scores remained stable over 4 months, although individual trajectories varied. Patient-reported geriatric impairments are prevalent in older adults with MM and strongly associated with nonhematologic toxicities even in the contemporary treatment era. Physician-reported CTCAE may not fully reflect patients' symptom burden. Heterogeneity in individual geriatric trajectories during treatment emphasize the need for longitudinal assessment. This study was registered at www.clinicaltrials.gov as NCT05918185.

Delayed graft function (DGF) is a frequent complication after deceased donor kidney transplantation, yet its long-term impact remains understudied. Heterogeneous populations, short follow-up, and inadequate control of donor-related factors have limited prior studies. Using the Scientific Registry of Transplant Recipients (2005–2024), we evaluated the association between DGF and long-term graft outcomes, including a mate-kidney (same donor, discordant DGF status) analysis to isolate its independent effect. We identified 103,678 dialysis-dependent deceased donor kidney recipients maintained on tacrolimus and mycophenolate. Mixed-effects Cox models, adjusted for donor, recipient, immunologic, and procurement factors. DGF occurred in 30.1% of recipients and was associated with increased death-censored graft failure (HR 1.40; 95% CI 1.33–1.47) and overall graft failure (HR 1.27; 95% CI 1.23–1.31). DGF recipients had higher rejection rates at 6 (6.0% vs. 4.7%) and 12 months (8.9% vs. 7.3%), longer hospital stay, and higher 1-year rehospitalization. In the mate-kidney analysis (6,818 donors), DGF remained strongly associated with death-censored graft failure (HR 1.58; 95% CI 1.39–1.80) and overall graft failure (HR 1.35; 95% CI 1.25–1.46), confirming the independent effect of DGF. DGF is a strong predictor of adverse long-term outcomes irrespective of rejection and warrants targeted prevention strategies and intensified follow-up.

ABSTRACT Predicting delayed graft function (DGF) relies on donor, recipient, and perioperative factors. Despite the growing recognition that DGF duration strongly influences patient outcomes, no models currently address or predict its impact—highlighting an important gap in current research and clinical practice. This study aimed to develop an ensemble‐based machine learning model using perioperative data to predict DGF occurrence and duration. The gradient‐boosted decision trees model was trained and validated on 2725 patients, with k‐fold cross‐validation in an external cohort of 284 patients. Model performance was evaluated based on accuracy, ROC‐AUC, and other metrics using R and Python libraries. The binary DGF prediction model achieved a ROC‐AUC of 0.77, while the DGF duration classification model had an accuracy of 79.2%. DGF duration AUC values by time interval were 0.76 for 0–1 weeks, 0.81 for >1–2 weeks, 0.80 for >2–3 weeks, and 0.87 for >3 weeks; the overall macro‐averaged AUC was 0.81. The mean Brier score for multi‐class predictions was 0.14. External validation showed a 78% accuracy for DGF duration prediction. Acute kidney injury (AKI) and donor donation after circulatory death (DCD) status were key DGF predictors. The use of gradient‐boosted decision trees (GBDT) improves the prediction of both the likelihood and duration of DGF, addressing a current gap in kidney transplant patient care. By facilitating personalized transplant care, this model supports more effective perioperative planning and timely interventions, which may contribute to better patient outcomes.