Continuous risk prediction of acute kidney injury in elderly critically-ill patients based on electronic medical records

doi:10.3969/j.issn.1006-7795.2022.04.014

Abstract

Abstract: Objective To explore the feasibility of early continuous risk prediction of acute renal injury in severe elderly patients (≥60 years old) and promote the application of machine learning in clinical decision support. Methods The data were collected from the Medical Information Mart for Intensive Care (MIMIC)-Ⅲ database. Logistic regression (LR), support vector machine (SVM), random forest (RF), and light gradient boosting machine (LightGBM) were applied to predict the risk of acute kidney injury (AKI). The prediction results were evaluated based on area under curve (AUC), accuracy, and recall. Results A total of 11 261 intensive care unit (ICU) records were included. When the data of every six hours was used for continuous prediction, AUCs yielded with LightGBM were 0.845－0.925, and those with RF, SVM, and LR were all less than 0.73. As for using the data of the first 6 hours, LightGBM reached AUC 0.845. Compared current data with the cumulative data of ICU, LightGBM yielded higher AUC, accuracy, and recall, whilst it was opposite in RF, SVM, and LR. Conclusion LightGBM completed AKI continuous prediction task with acceptable performance. It is practical to use the data of the first 6 hours on ICU admission for AKI early prediction, which achieve prediction effect of 24－hour accumulated data. In addition, different models have different data applicability. LightGBM performed better based on current data while the other three models favored cumulative data.

Key words: machine learning, disease prediction, acute kidney injury, electronic medical records, intensive care unit

CLC Number:

Wu Jinming, Sun Haixia, Wang Jiayang, Qian Qing. Continuous risk prediction of acute kidney injury in elderly critically-ill patients based on electronic medical records[J]. Journal of Capital Medical University, 2022, 43(4): 600-609.

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URL: https://journal03.magtech.org.cn/Jweb_sdykdxxb/EN/10.3969/j.issn.1006-7795.2022.04.014

https://journal03.magtech.org.cn/Jweb_sdykdxxb/EN/Y2022/V43/I4/600

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