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Prediction Models for AKI in ICU: A Comparative Study

Authors Qian Q, Wu J, Wang J, Sun H, Yang L

Received 30 October 2020

Accepted for publication 7 January 2021

Published 25 February 2021 Volume 2021:14 Pages 623—632

DOI https://doi.org/10.2147/IJGM.S289671

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Scott Fraser


Qing Qian, 1, 2 Jinming Wu, 2 Jiayang Wang, 2 Haixia Sun, 2 Lei Yang 1

1Hangzhou Normal University, Hangzhou, People’s Republic of China; 2Institute of Medical Information & Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People’s Republic of China

Correspondence: Lei Yang
Hangzhou Normal University, Hangzhou, People’s Republic of China
Email [email protected]

Purpose: To assess the performance of models for early prediction of acute kidney injury (AKI) in the Intensive Care Unit (ICU) setting.
Patients and Methods: Data were collected from the Medical Information Mart for Intensive Care (MIMIC)-III database for all patients aged ≥ 18 years who had their serum creatinine (SCr) level measured for 72 h following ICU admission. Those with existing conditions of kidney disease upon ICU admission were excluded from our analyses. Seventeen predictor variables comprising patient demographics and physiological indicators were selected on the basis of the Kidney Disease Improving Global Outcomes (KDIGO) and medical literature. Six models from three types of methods were tested: Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Decision Machine (LightGBM), and Convolutional Neural Network (CNN). The area under receiver operating characteristic curve (AUC), accuracy, precision, recall and F-measure (F1) were calculated for each model to evaluate performance.
Results: We extracted the ICU records of 17,205 patients from MIMIC-III dataset. LightGBM had the best performance, with all evaluation indicators achieving the highest value (average AUC = 0.905, F1 = 0.897, recall = 0.836). XGBoost had the second best performance and LR, RF, SVM performed similarly (P = 0.082, 0.158 and 0.710, respectively) on AUC. The CNN model achieved the lowest score for accuracy, precision, F1 and AUC. SVM and LR had relatively low recall compared with that of the other models. The SCr level had the most significant effect on the early prediction of AKI onset in LR, RF, SVM and LightGBM.
Conclusion: LightGBM demonstrated the best capability for predicting AKI in the first 72 h of ICU admission. LightGBM and XGBoost showed great potential for clinical application owing to their high recall value. This study can provide references for artificial intelligence-powered clinical decision support systems for AKI early prediction in the ICU setting.

Keywords: acute kidney injury, intensive care unit, prediction models, machine learning, deep learning

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