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Objective <p>Robot-assisted partial nephrectomy (RAPN) is an established, minimally invasive technique to treat patients with renal masses. The incidence of acute kidney injury (AKI) after RAPN is high and is associated with poor prognosis. This study aims to develop and validate an interpretable machine-learning model based on clinical features for individualized risk assessment of RAPN-AKI.</p> Methods <p>We retrospectively reviewed 325 patients undergoing RAPN at the Third Medical Center of PLA General Hospital (May 2022–Oct 2023) as the training dataset, and 146 from the Fifth Medical Center of PLA General Hospital (Nov 2023–Dec 2024) for external validation. Models were constructed using Boruta-selected features and eight machine learning algorithms. Performance was assessed by the area under the receiver operating characteristic curve (AUC), F1-score, accuracy, precision, calibration, and decision curve analysis (DCA). Shapley additive explanations (SHAP) interpreted feature contributions.</p> Results <p>The incidence of AKI in internal training and external validation datasets was 24.6% and 26%, respectively. The Boruta algorithm identified duration of renal artery blockade, preoperative serum creatinine (Scr), gender, body mass index (BMI), and age as important features. Among the eight machine learning models, the Gradient Boosting Machine (GBM) model demonstrated the best and most stable predictive outcomes in the internal training dataset (AUC = 0.889) and external validation dataset (AUC = 0.779). Both the calibration curve and DCA indicated better calibration and greater net benefit. SHAP analysis revealed the contribution of important features in the following order: duration of renal artery blockade, Scr, BMI, age, and gender. Dependency plots showed that duration of renal artery blockade &gt; 22&#xa0;min, Scr &gt; 80 µmol/L, BMI &gt; 25&#xa0;kg/m², age &gt; 60 years, and male were significantly associated with an increased risk of AKI.</p> Conclusion <p>The GBM model exhibited strong predictive performance in both internal training dataset and external validation dataset and has the potential to assist clinicians to identify the high-risk patients early, enabling timely interventions that may reduce the incidence of RAPN-AKI and improving clinical outcomes. While, the interpretable machine learning model is currently applicable only to patients with low-risk or normal preoperative renal function.</p>

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Development and validation of an explainable machine learning model for predicting acute kidney injury after robot-assisted partial nephrectomy: a retrospective multicenter study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月5日

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  • Jiaxin Li,
  • Longhe Xu,
  • Yingqun Yu,
  • Xiufeng Li,
  • Yi Liu,
  • Weiwei Liu,
  • Jin Yan,
  • Han Gao,
  • Fei Liu,
  • Changhong Sun,
  • Huixian Chen,
  • Yunfei Lv,
  • Jiang Huo,
  • Yongzhe Liu

摘要

Objective

Robot-assisted partial nephrectomy (RAPN) is an established, minimally invasive technique to treat patients with renal masses. The incidence of acute kidney injury (AKI) after RAPN is high and is associated with poor prognosis. This study aims to develop and validate an interpretable machine-learning model based on clinical features for individualized risk assessment of RAPN-AKI.

Methods

We retrospectively reviewed 325 patients undergoing RAPN at the Third Medical Center of PLA General Hospital (May 2022–Oct 2023) as the training dataset, and 146 from the Fifth Medical Center of PLA General Hospital (Nov 2023–Dec 2024) for external validation. Models were constructed using Boruta-selected features and eight machine learning algorithms. Performance was assessed by the area under the receiver operating characteristic curve (AUC), F1-score, accuracy, precision, calibration, and decision curve analysis (DCA). Shapley additive explanations (SHAP) interpreted feature contributions.

Results

The incidence of AKI in internal training and external validation datasets was 24.6% and 26%, respectively. The Boruta algorithm identified duration of renal artery blockade, preoperative serum creatinine (Scr), gender, body mass index (BMI), and age as important features. Among the eight machine learning models, the Gradient Boosting Machine (GBM) model demonstrated the best and most stable predictive outcomes in the internal training dataset (AUC = 0.889) and external validation dataset (AUC = 0.779). Both the calibration curve and DCA indicated better calibration and greater net benefit. SHAP analysis revealed the contribution of important features in the following order: duration of renal artery blockade, Scr, BMI, age, and gender. Dependency plots showed that duration of renal artery blockade > 22 min, Scr > 80 µmol/L, BMI > 25 kg/m², age > 60 years, and male were significantly associated with an increased risk of AKI.

Conclusion

The GBM model exhibited strong predictive performance in both internal training dataset and external validation dataset and has the potential to assist clinicians to identify the high-risk patients early, enabling timely interventions that may reduce the incidence of RAPN-AKI and improving clinical outcomes. While, the interpretable machine learning model is currently applicable only to patients with low-risk or normal preoperative renal function.