Machine learning for the prediction of acute kidney injury in patients with sepsis

Background Acute kidney injury (AKI) is the most common and serious complication of sepsis, accompanied by high mortality and disease burden. The early prediction of AKI is critical for timely intervention and ultimately improves prognosis. This study aims to establish and validate predictive models...

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Published in:Journal of translational medicine Vol. 20; no. 1; pp. 215 - 12
Main Authors: Yue, Suru, Li, Shasha, Huang, Xueying, Liu, Jie, Hou, Xuefei, Zhao, Yumei, Niu, Dongdong, Wang, Yufeng, Tan, Wenkai, Wu, Jiayuan
Format: Journal Article
Language:English
Published: London BioMed Central 13.05.2022
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Acute kidney injury
Acute renal failure
Algorithms
Biomedical and Life Sciences
Biomedicine
Blood pressure
Complications and side effects
Computational Modelling and Epidemiology
Datasets
Disease
Feature selection
Health aspects
Intensive care
Kidneys
Laboratories
Learning algorithms
Machine learning
Medical prognosis
Medicine/Public Health
MIMIC- III database
Mortality
Neural networks
Patients
Physiology
Prediction model
Prediction models
Risk factors
Risk groups
Sepsis
Support vector machines
Vital signs
China
Sepsis
MIMIC- III database
Acute kidney injury
Machine learning
Prediction model
ISSN:1479-5876, 1479-5876
Online Access:Get full text
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Summary:Background Acute kidney injury (AKI) is the most common and serious complication of sepsis, accompanied by high mortality and disease burden. The early prediction of AKI is critical for timely intervention and ultimately improves prognosis. This study aims to establish and validate predictive models based on novel machine learning (ML) algorithms for AKI in critically ill patients with sepsis. Methods Data of patients with sepsis were extracted from the Medical Information Mart for Intensive Care III (MIMIC- III) database. Feature selection was performed using a Boruta algorithm. ML algorithms such as logistic regression (LR), k -nearest neighbors (KNN), support vector machine (SVM), decision tree, random forest, Extreme Gradient Boosting (XGBoost), and artificial neural network (ANN) were applied for model construction by utilizing tenfold cross-validation. The performances of these models were assessed in terms of discrimination, calibration, and clinical application. Moreover, the discrimination of ML-based models was compared with those of Sequential Organ Failure Assessment (SOFA) and the customized Simplified Acute Physiology Score (SAPS) II model. Results A total of 3176 critically ill patients with sepsis were included for analysis, of which 2397 cases (75.5%) developed AKI during hospitalization. A total of 36 variables were selected for model construction. The models of LR, KNN, SVM, decision tree, random forest, ANN, XGBoost, SOFA and SAPS II score were established and obtained area under the receiver operating characteristic curves of 0.7365, 0.6637, 0.7353, 0.7492, 0.7787, 0.7547, 0.821, 0.6457 and 0.7015, respectively. The XGBoost model had the best predictive performance in terms of discrimination, calibration, and clinical application among all models. Conclusion The ML models can be reliable tools for predicting AKI in septic patients. The XGBoost model has the best predictive performance, which can be used to assist clinicians in identifying high-risk patients and implementing early interventions to reduce mortality.
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ISSN:1479-5876
1479-5876
DOI:10.1186/s12967-022-03364-0