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Prediction of risk for acute kidney injury and its progression to mortality in obese patients admitted to ICU postoperatively
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Qiang LI1, 2, Guo MU1, 2, Wenzhang WANG1, 2, Jie YIN1, 2, Xuan YU1, 2, Bin LU2, Qian LI3, Jun ZHOU1
Journal of Army Medical University | 2025, 47(10) : 1110 - 1125
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Journal of Army Medical University | 2025, 47(10): 1110-1125
• Clinical Medicine •
Prediction of risk for acute kidney injury and its progression to mortality in obese patients admitted to ICU postoperatively
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Qiang LI1, 2, Guo MU1, 2, Wenzhang WANG1, 2, Jie YIN1, 2, Xuan YU1, 2, Bin LU2, Qian LI3, Jun ZHOU1
Affiliations
  • 1Department of Anesthesiology,Affiliated Hospital of Southwest Medical University,Luzhou,Sichuan
  • 2Department of Anesthesiology,Zigong Fourth People’s Hospital,Zigong,Sichuan
  • 3Department of Anesthesiology,West China Hospital of Sichuan University,Chengdu,Sichuan,China
Published: 2025-05-30 doi: 10.16016/j.2097-0927.202503010
Outline
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Objective

To develop a machine learning-based risk prediction model for postoperative acute kidney injury (AKI) and a model for mortality in obese patients admitted to intensive care unit (ICU) in order to improve early warning and prognostic evaluation to support clinical decision-making.

Methods‍

Data of obese postoperative ICU patients were retrospectively retrieved from the MIMIC-IV and eICU databases for statistical analysis. Ultimately, 2 520 patients (670 from MIMIC-IV and 1 850 from eICU databases) were included to build the risk prediction models for AKI and mortality. The data included demographic information, vital signs, laboratory findings, surgical types, comorbidities, and medication use. After data cleaning and preprocessing, Boruta feature selection was applied, followed by the construction of prediction models using 7 machine learning algorithms, that is, Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), k-Nearest Neighbors (KNN), Naïve Bayes (NB), Neural Network (NNET), Support Vector Machine (SVM), and XGBoost. Model performance was evaluated through cross-validation and external validation.

Results‍

In the risk prediction models of AKI, the SVM model achieved the highest AUC value of 0.80 in the testing set and 0.71 in the external validation test. For the risk prediction models of mortality, the GBM model outperformed others in the prediction, attaining an AUC value of 0.91 in the testing set.

Conclusion‍

Risk predictive models for postoperative AKI and mortality in obese ICU patients are successfully constructed, and are valuable tools for clinicians to optimize early intervention and improve clinical outcomes for the patients.

obese patients  /  acute kidney injury  /  machine learning  /  risk prediction  /  mortality prediction
李强, 母国, 王文章, 印婕, 余璇, 卢彬, 李茜, 周军. 术后入ICU肥胖患者AKI风险及其进展为死亡预测研究. 陆军军医大学学报, 2025 , 47 (10) : 1110 -1125 . DOI: 10.16016/j.2097-0927.202503010
Qiang LI, Guo MU, Wenzhang WANG, Jie YIN, Xuan YU, Bin LU, Qian LI, Jun ZHOU. Prediction of risk for acute kidney injury and its progression to mortality in obese patients admitted to ICU postoperatively[J]. Journal of Army Medical University, 2025 , 47 (10) : 1110 -1125 . DOI: 10.16016/j.2097-0927.202503010
Year 2025 volume 47 Issue 10
16
7
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Article Info
doi: 10.16016/j.2097-0927.202503010
  • Receive Date:2025-03-04
  • Online Date:2026-10-28
  • Published:2025-05-30
Article Data
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History
  • Received:2025-03-04
  • Revised:2025-04-01
Affiliations
    1Department of Anesthesiology,Affiliated Hospital of Southwest Medical University,Luzhou,Sichuan
    2Department of Anesthesiology,Zigong Fourth People’s Hospital,Zigong,Sichuan
    3Department of Anesthesiology,West China Hospital of Sichuan University,Chengdu,Sichuan,China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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