Prediction Models for AKI in ICU: A Comparative Study

Qing Qian; Jinming Wu; Jiayang Wang; Haixia Sun; Lei Yang

doi:10.2147/IJGM.S289671

Prediction Models for AKI in ICU: A Comparative Study

Int J Gen Med. 2021 Feb 25:14:623-632. doi: 10.2147/IJGM.S289671. eCollection 2021.

Authors

Qing Qian^{1

2}, Jinming Wu², Jiayang Wang², Haixia Sun², Lei Yang¹

Affiliations

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

Abstract

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; deep learning; intensive care unit; machine learning; prediction models.

Grants and funding

This work was supported by the National Key Research & Development Plan of China (Grant No 2016YFC0901602) and the Chinese Academy of Medical Sciences (Grant No 2018PT33024, 2018-I2M-AI-016).