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24 September 2021 : Database Analysis

[In Press] A Statistical Prediction Model for Survival After Kidney Transplantation from Deceased Donors

Jia-shan Pan1AE, Yi-ding Chen1BC, Han-dong Ding1B, Tian-chi Lan1B, Fei Zhang1B, Jin-biao Zhong1A, Gui-yi Liao1AF

DOI: 10.12659/MSM.933559

Med Sci Monit In Press; DOI: 10.12659/MSM.933559  

Available online: 2021-09-24, In Press, Corrected Proof

Publication in the "In-Press" formula aims at speeding up the public availability of the pending manuscript while waiting for the final publication. The assigned DOI number is active and citable. The availability of the article in the Medline, PubMed and PMC databases as well as Web of Science will be obtained after the final publication according to the journal schedule

Abstract

BACKGROUND
In an environment of limited kidney donation resources, patient recovery and survival after kidney transplantation (KT) are highly important. We used pre-operative data of kidney recipients to build a statistical model for predicting survivability after kidney transplantation.
MATERIAL AND METHODS
A dataset was constructed from a pool of patients who received a first KT in our hospital. For allogeneic transplantation, all donated kidneys were collected from deceased donors. Logistic regression analysis was used to change continuous variables into dichotomous ones through the creation of appropriate cut-off values. A regression model based on the least absolute shrinkage and selection operator (LASSO) algorithm was used for dimensionality reduction, feature selection, and survivability prediction. We used receiver operating characteristic (ROC) analysis, calibration, and decision curve analysis (DCA) to evaluate the performance and clinical impact of the proposed model. Finally, a 10-fold cross-validation scheme was implemented to verify the model robustness.
RESULTS
We identified 22 potential variables from which 30 features were selected as survivability predictors. The model established based on the LASSO regression algorithm had shown discrimination with an area under curve (AUC) value of 0.690 (95% confidence interval: 0.557-0.823) and good calibration result. DCA demonstrated clinical applicability of the prognostic model when the intervention progressed to the possibility threshold of 2%. An average AUC value of 0.691 was obtained on the validation data.
CONCLUSIONS
Our results suggest that the proposed model can predict the mortality risk for patients after kidney transplants and could help kidney specialists choose kidney recipients with better prognosis.

Keywords: Kidney Transplantation; Predictive Value of Tests; Tissue Donors

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