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Saudi J Kidney Dis Transpl. 2019 Jan-Feb;30(1):1-14.

Prediction models to measure transplant readiness of patients with renal failure: A systematic review.

Author information

1
Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
2
Department of Internal Medicine, School of Medicine, Ghaem Hospital, Mashhad, Iran.
3
Organ Procurement Center, Montaserie Hospital, Mashhad University of Medical Sciences, Mashhad, Iran.

Abstract

Predicting the future of illness, a patient is facing helps the physicians to choose the best strategy to manage the disease. Models for predicting the readiness of candidates for kidney transplant can be very promising. This study sought to systematically review the predictive models and algorithms that assess the readiness of renal transplant candidates in different countries. This systematic review study was according to PRISMA-P protocol in PubMed and Science Direct databases and general search engines up to March 2017. Eligible studies were those that introduced a model to assess the readiness for renal transplantation of patients with chronic renal failure from cadavers and this assessment led to scoring prioritization or superiority among patients. We found 28 studies from 11 countries that met the search criteria and >50% of them were published from 2015 onward. Of the studies, nine models and algorithms were extracted that included 12 factors. Some models, including the European and Scandinavian models, were used jointly between different countries. All the models had at least four factors, and nearly 90% of the models considered four or five factors to measure kidney transplantation readiness. More than 50% of the models had age, dialysis duration, HLA type, and emergency status factors and, dialysis duration. Predictive models are important for renal transplant because of the significant reduction in number of cadavers and longer wait of candidates for a kidney transplant. Further studies can examine the effect of these models on the survival of the kidney transplant.

PMID:
30804261

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