Azar Kazemi1, Kourosh Kazemi, Ashkan Sami, Roxana Sharifian. 1. From the Student Research Committee, School of Management and Medical Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
Abstract
OBJECTIVES: Survival after liver transplant depends on pretransplant, peritransplant, and posttransplant factors. Identifying effective factors for patient survival after transplant can help transplant centers make better decisions. MATERIALS AND METHODS: Our study included 902 adults who received livers from deceased donors from March 2011 to March 2014 at the Shiraz Organ Transplant Center (Shiraz, Iran). In a 3-step feature selection method, effective features of 6-month survival were extracted by (1) F statistics, Pearson chi-square, and likelihood ratio chi-square and by (2) 5 machine-learning techniques. To evaluate the performance of the machine-learning techniques, Cox regression was applied to the data set. Evaluations were based on the area under the receiver operating characteristic curve and sensitivity of models. (3) We also constructed a model using all factors identified in the previous step. RESULTS: The model predicted survival based on 26 identified effective factors. In the following order, graft failure, Aspergillus infection, acute renal failure and vascular complications after transplant, as well as graft failure diagnosis interval, previous diabetes mellitus, Model for End-Stage Liver Disease score, donor inotropic support, units of packed cell received, and previous recipient dialysis, were found to be predictive factors in patient survival. The area under the receiver operating characteristic curve and model sensitivity were 0.90 and 0.81, respectively. CONCLUSIONS: Data mining analyses can help identify effective features of patient survival after liver transplant and build models with equal or higher performance than Cox regression. The order of influential factors identified with the machine-learning model was close to clinical experiments.
OBJECTIVES: Survival after liver transplant depends on pretransplant, peritransplant, and posttransplant factors. Identifying effective factors for patient survival after transplant can help transplant centers make better decisions. MATERIALS AND METHODS: Our study included 902 adults who received livers from deceased donors from March 2011 to March 2014 at the Shiraz Organ Transplant Center (Shiraz, Iran). In a 3-step feature selection method, effective features of 6-month survival were extracted by (1) F statistics, Pearson chi-square, and likelihood ratio chi-square and by (2) 5 machine-learning techniques. To evaluate the performance of the machine-learning techniques, Cox regression was applied to the data set. Evaluations were based on the area under the receiver operating characteristic curve and sensitivity of models. (3) We also constructed a model using all factors identified in the previous step. RESULTS: The model predicted survival based on 26 identified effective factors. In the following order, graft failure, Aspergillus infection, acute renal failure and vascular complications after transplant, as well as graft failure diagnosis interval, previous diabetes mellitus, Model for End-Stage Liver Disease score, donor inotropic support, units of packed cell received, and previous recipient dialysis, were found to be predictive factors in patient survival. The area under the receiver operating characteristic curve and model sensitivity were 0.90 and 0.81, respectively. CONCLUSIONS: Data mining analyses can help identify effective features of patient survival after liver transplant and build models with equal or higher performance than Cox regression. The order of influential factors identified with the machine-learning model was close to clinical experiments.
Authors: Neta Gotlieb; Amirhossein Azhie; Divya Sharma; Ashley Spann; Nan-Ji Suo; Jason Tran; Ani Orchanian-Cheff; Bo Wang; Anna Goldenberg; Michael Chassé; Heloise Cardinal; Joseph Paul Cohen; Andrea Lodi; Melanie Dieude; Mamatha Bhat Journal: NPJ Digit Med Date: 2022-07-11