Detection of MICA Antibodies in Kidney Transplant Candidates on the Deceased Donor Waiting List in Panama.

Introduction: Frailty and decreased physical performance are associated with poor outcomes after kidney transplant. Less is known about their relationship with pretransplant outcomes. The aim of this study was to characterize associations between frailty and physical performance with death on the kidney transplant waiting list. Design: Since December 2014, high-risk kidney transplant candidates at our center (age > 59, diabetic and/or history of >3 years dialysis) have undergone frailty and physical performance testing using Fried Criteria and the Short Physical Performance Battery. Results: Between December 2014 and November 2016, 272 high-risk candidates underwent testing and were approved for transplant. Both frailty and physical performance score were significantly associated with death on the waiting list (hazard ratio [HR]: 6.7, confidence interval [CI]: 1.5-30.1; P = .01; HR: 0.8 per 1-point increase, CI: 0.7-1.0; P = .02, respectively). The relationship between frailty, physical performance score, and death on the waiting list appeared to be independent of age, diabetes, or duration of dialysis. Discussion: Frailty and decreased physical performance appear to be independently associated with increased mortality on the kidney transplant waiting list. Further studies are needed to determine whether improving frailty and physical performance prior to transplant can decrease waiting list mortality.

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A machine learning prediction model for waiting time to kidney transplant

PLoS ONE ◽

10.1371/journal.pone.0252069 ◽

2021 ◽

Vol 16 (5) ◽

pp. e0252069

Author(s):

Juliana Feiman Sapiertein Silva ◽

Gustavo Fernandes Ferreira ◽

Marcelo Perosa ◽

Hong Si Nga ◽

Luis Gustavo Modelli de Andrade

Keyword(s):

Machine Learning ◽

Kidney Transplant ◽

Waiting Time ◽

Cox Regression ◽

Final Analysis ◽

Deceased Donor ◽

Waiting List ◽

Sensitivity Analyses ◽

Cox Models ◽

Use Of Resources

Background Predicting waiting time for a deceased donor kidney transplant can help patients and clinicians to discuss management and contribute to a more efficient use of resources. This study aimed at developing a predictor model to estimate time on a kidney transplant waiting list using a machine learning approach. Methods A retrospective cohort study including data of patients registered, between January 1, 2000 and December 31, 2017, in the waiting list of São Paulo State Organ Allocation System (SP-OAS) /Brazil. Data were randomly divided into two groups: 75% for training and 25% for testing. A Cox regression model was fitted with deceased donor transplant as the outcome. Sensitivity analyses were performed using different Cox models. Cox hazard ratios were used to develop the risk-prediction equations. Results Of 54,055 records retrieved, 48,153 registries were included in the final analysis. During the study period, approximately 1/3 of the patients were transplanted with a deceased donor. The major characteristics associated with changes in the likelihood of transplantation were age, subregion, cPRA, and frequency of HLA-DR, -B and -A. The model developed was able to predict waiting time with good agreement in internal validation (c-index = 0.70). Conclusion The kidney transplant waiting time calculator developed shows good predictive performance and provides information that may be valuable in assisting candidates and their providers. Moreover, it can significantly improve the use of economic resources and the management of patient care before transplant.

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