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Observational Study
. 2024 Apr 11;19(4):e0301014.
doi: 10.1371/journal.pone.0301014. eCollection 2024.

Machine learning for prediction of acute kidney injury in patients diagnosed with sepsis in critical care

Jianshan Shi  1 , Huirui Han  2   3 , Song Chen  4 , Wei Liu  2   3 , Yanfen Li  5
Affiliations
Observational Study

Machine learning for prediction of acute kidney injury in patients diagnosed with sepsis in critical care

Jianshan Shi et al. PLoS One. .

Abstract

Background and objective: Acute Kidney Injury (AKI) is a common and severe complication in patients diagnosed with sepsis. It is associated with higher mortality rates, prolonged hospital stays, increased utilization of medical resources, and financial burden on patients' families. This study aimed to establish and validate predictive models using machine learning algorithms to accurately predict the occurrence of AKI in patients diagnosed with sepsis.

Methods: This retrospective study utilized real observational data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. It included patients aged 18 to 90 years diagnosed with sepsis who were admitted to the ICU for the first time and had hospital stays exceeding 48 hours. Predictive models, employing various machine learning algorithms including Light Gradient Boosting Machine (LightGBM), EXtreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Logistic Regression (LR), were developed. The dataset was randomly divided into training and test sets at a ratio of 4:1.

Results: A total of 10,575 sepsis patients were included in the analysis, of whom 8,575 (81.1%) developed AKI during hospitalization. A selection of 47 variables was utilized for model construction. The models derived from LightGBM, XGBoost, RF, DT, ANN, SVM, and LR achieved AUCs of 0.801, 0.773, 0.772, 0.737, 0.720, 0.765, and 0.776, respectively. Among these models, LightGBM demonstrated the most superior predictive performance.

Conclusions: These machine learning models offer valuable predictive capabilities for identifying AKI in patients diagnosed with sepsis. The LightGBM model, with its superior predictive capability, could aid clinicians in early identification of high-risk patients.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Fig 1
Fig 1. Flow chart of patients’ selection.
Fig 2
Fig 2. Comparison of the ROC curve of the 7 models.
ROC = Receiver Operating Characteristic, TPR = True Positive Rate, TNR = True Negative Rate, AUC = Area Under the ROC Curve.
Fig 3
Fig 3. Confusion matrix for 7 models.
(a) LightGBM (b) XGBoost (c) RF (d) DT (e) ANN (f) SVM (g) LR.
Fig 4
Fig 4. Top-20 variable importance of LightGBM.
See this image and copyright information in PMC

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