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Background <p>Acute kidney injury (AKI) has been confirmed to be related to the prognosis of aSAH patients. Evaluating the risk of AKI in the early stage is important to avoid the unfavorable outcome of aSAH patients. However, no study has explored the predictive value of machine learning algorithms for AKI after aSAH. This study was designed to develop a machine learning algorithm-based predictive model for AKI among aSAH patients.</p> Methods <p>The outcome of this study was the AKI confirmed using the KDIGO criteria. The predictive value of seven machine learning algorithms for the AKI among aSAH patients was explored and verified using the 5-fold cross-validation. The predictive efficiency of machine learning algorithms-based predictive models was evaluated by the area under the receiver operating characteristics curve (AUC). The Shapley Additive explanation method was performed to visualize the importance of features incorporated in machine learning algorithms-based predictive models.</p> Results <p>711 aSAH patients were enrolled with an AKI incidence of 7.7%. The AKI group had higher WFNS (<i>p</i> = 0.011), Hunt Hess (<i>p</i> = 0.006), and lower Glasgow Coma Scale (GCS) (<i>p</i> = 0.004). The multiple aneurysm was more frequently observed in the AKI group (<i>p</i> = 0.027). The AKI group had longer length of ICU stay (<i>p</i> &lt; 0.001), length of hospital stay (<i>p</i> &lt; 0.001), and higher mortality (<i>p</i> &lt; 0.001). Three algorithms performed well in predicting the AKI in the training dataset including the random forest (AUC = 1.000), AdaBoost (AUC = 0.954), and XGBoost (AUC = 0.947). The random forest performed the best in the validation dataset with an AUC of 0.724. The top ten features in the random forest algorithm were GCS, mean blood pressure, initial serum creatinine, cystatin C level, albumin, neutrophil, lactate dehydrogenase, glucose, white blood cell, and sodium.</p> Conclusions <p>The random forest model demonstrated superior performance in predicting AKI in aSAH patients, achieving a high AUC value, predictive accuracy, and remarkable stability. This model could help clinicians evaluate the risk of AKI in the early stage and guide therapeutic options among aSAH patients.</p>

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A machine learning predictive model for acute kidney injury among aneurysmal subarachnoid hemorrhage patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月10日

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  • Ruoran Wang,
  • Lingzhu Qian,
  • Yunhui Zeng,
  • Linrui Cai,
  • Min He,
  • Jianguo Xu,
  • Yu Zhang

摘要

Background

Acute kidney injury (AKI) has been confirmed to be related to the prognosis of aSAH patients. Evaluating the risk of AKI in the early stage is important to avoid the unfavorable outcome of aSAH patients. However, no study has explored the predictive value of machine learning algorithms for AKI after aSAH. This study was designed to develop a machine learning algorithm-based predictive model for AKI among aSAH patients.

Methods

The outcome of this study was the AKI confirmed using the KDIGO criteria. The predictive value of seven machine learning algorithms for the AKI among aSAH patients was explored and verified using the 5-fold cross-validation. The predictive efficiency of machine learning algorithms-based predictive models was evaluated by the area under the receiver operating characteristics curve (AUC). The Shapley Additive explanation method was performed to visualize the importance of features incorporated in machine learning algorithms-based predictive models.

Results

711 aSAH patients were enrolled with an AKI incidence of 7.7%. The AKI group had higher WFNS (p = 0.011), Hunt Hess (p = 0.006), and lower Glasgow Coma Scale (GCS) (p = 0.004). The multiple aneurysm was more frequently observed in the AKI group (p = 0.027). The AKI group had longer length of ICU stay (p < 0.001), length of hospital stay (p < 0.001), and higher mortality (p < 0.001). Three algorithms performed well in predicting the AKI in the training dataset including the random forest (AUC = 1.000), AdaBoost (AUC = 0.954), and XGBoost (AUC = 0.947). The random forest performed the best in the validation dataset with an AUC of 0.724. The top ten features in the random forest algorithm were GCS, mean blood pressure, initial serum creatinine, cystatin C level, albumin, neutrophil, lactate dehydrogenase, glucose, white blood cell, and sodium.

Conclusions

The random forest model demonstrated superior performance in predicting AKI in aSAH patients, achieving a high AUC value, predictive accuracy, and remarkable stability. This model could help clinicians evaluate the risk of AKI in the early stage and guide therapeutic options among aSAH patients.