AUTHOR=Li Jinzhang , Gong Ming , Joshi Yashutosh , Sun Lizhong , Huang Lianjun , Fan Ruixin , Gu Tianxiang , Zhang Zonggang , Zou Chengwei , Zhang Guowei , Qian Ximing , Qiao Chenhui , Chen Yu , Jiang Wenjian , Zhang Hongjia 

TITLE=Machine Learning Prediction Model for Acute Renal Failure After Acute Aortic Syndrome Surgery

JOURNAL=Frontiers in Medicine

VOLUME=Volume 8 - 2021

YEAR=2022

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2021.728521

DOI=10.3389/fmed.2021.728521

ISSN=2296-858X

ABSTRACT=Background
Acute renal failure (ARF) is the most common major complication following cardiac surgery for acute aortic syndrome (AAS) and worsens the postoperative prognosis. Our aim was to establish a machine learning prediction model for ARF occurrence in AAS patients.
Methods
We included AAS patient data from 9 medical centres (n=1637) and analysed the incidence of ARF and the risk factors for postoperative ARF. We used data from 6 medical centres to compare the performance of 4 machine learning models and performed internal validation to identify AAS patients who developed postoperative ARF. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to compare the performance of the predictive models. We compared the performance of the optimal machine learning prediction model with that of traditional prediction models. Data from 3 medical centres were used for external validation.
Results
The eXtreme Gradient Boosting (XGBoost) algorithm performed best in the internal validation process (AUC = 0.82), which was better than both the logistic regression (LR) prediction model (AUC = 0.77, p<0.001) and the traditional scoring systems. Upon external validation, the XGBoost prediction model (AUC =0.81) also performed better than both the LR prediction model (AUC = 0.75, p=0.03) and the traditional scoring systems. We created an online application based on the XGBoost prediction model.
Conclusions
We have developed a machine learning model that has better predictive performance than traditional LR prediction models as well as other existing risk scoring systems for postoperative ARF. This model can be utilized to provide early warnings when high-risk patients are found, enabling clinicians to take prompt measures.