A machine learning prediction model for waiting time to kidney transplant

• Published in: PloS one Vol. 16; no. 5; p. e0252069
• Main Authors: Sapiertein Silva, Juliana Feiman, Ferreira, Gustavo Fernandes, Perosa, Marcelo, Nga, Hong Si, de Andrade, Luis Gustavo Modelli
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20-05-2021; Public Library of Science (PLoS)
• Subjects: Biology and Life Sciences; Blood; Blood groups; Blood transfusion; Computer and Information Sciences; Dialysis; Health risks; Hemodialysis; Hepatitis; Hepatitis B; Histocompatibility antigen HLA; Internal medicine; Kidney transplantation; Kidney transplants; Kidneys; Learning algorithms; Machine learning; Medical appointments and schedules; Medicine; Medicine and Health Sciences; People and places; Physical Sciences; Population; Prediction models; Registration; Research and Analysis Methods; Serology; Surgery; Technology application; Transplantation; Transplants; Brazil
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0252069

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.
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.
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).
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.