PREDICTING INTENSIVE CARE UNIT READMISSION AMONG PATIENTS AFTER LIVER TRANSPLANTATION USING MACHINE LEARNING

Linmei Gong, Subo Gong, Xiaoqiang Wu, Jiezhou He, Yanjun Zhong, Jun Tang, Jiayi Deng, Zhongzhou Si, Yi Liu, Guyi Wang and Jinxiu Li
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Linmei Gong: Department of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, P. R. China
Subo Gong: ��Department of Geriatrics, The Second Xiangya Hospital, Central South University, Changsha 410011, P. R. China
Xiaoqiang Wu: ��College of Information Science and Engineering, Hunan Normal University Changsha 410012, P. R. China
Jiezhou He: �Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, P. R. China
Yanjun Zhong: Department of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, P. R. China
Jun Tang: Department of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, P. R. China
Jiayi Deng: Department of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, P. R. China
Zhongzhou Si: �Center for Organ Transplantation, The Second Xiangya Hospital, Central South University, Changsha 410011, P. R. China
Yi Liu: ��Department of Respiratory and Critical Care Medicine, Zhuzhou People’s Hospital, Zhuzhou 412007, P. R. China
Guyi Wang: Department of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, P. R. China
Jinxiu Li: Department of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, P. R. China

FRACTALS (fractals), 2023, vol. 31, issue 06, 1-14

Abstract: Intensive care unit (ICU) readmission of patients following liver transplantation (LT) is associated with poor outcomes. However, its risk factors remain unclarified. Nowadays, machine learning methods are widely used in many aspects of medical health. This study aims to develop a reliable prognostic model for ICU readmission for post-LT patients using machine learning methods. In this paper, a single center cohort (N = 543) was studied, of which 5.9% (N = 32) were readmitted to the ICU during hospitalization for LT. A retrospective review of baseline and perioperative factors possibly related to ICU readmission was performed. Three feature selection techniques were used to detect the best features influencing ICU readmission. Moreover, seven machine learning classifiers were proposed and compared to detect the risk of ICU readmission. Alanine transaminase (ALT) at hospital admission, intraoperative fresh frozen plasma (FFP) and red blood cell (RBC) transfusion, and N-Terminal pro-brain natriuretic peptide (NT-proBNP) after LT were found to be essential features for ICU readmission risk prediction. And the stacking model produced the best performance, identifying patients that were readmitted to the ICU after LT at an accuracy of 97.50%, precision of 96.34%, recall of 96.32%, and F1-score of 96.32%. RBC transfusion is the most crucial feature of the stacking classification model, which produced the best performance with overall accuracy, precision, recall, and F1-score of 88.49%, 88.66%, 76.01%, and 81.84%, respectively.

Keywords: Liver Transplantation; Intensive Care Units; Risk Factors; Machine Learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218348X23401345

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