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Background <p>Acute kidney injury (AKI) is a common and serious complication of acute pancreatitis (AP), which greatly increases the economic burden and mortality. Measuring intra-abdominal pressure (IAP) is very important for the management of patients with AP, and intra-bladder pressure (IBP) is an indirect indicator of IAP. However, research on the relationship between IBP and AKI in patients with AP is limited. Therefore, the purpose of this study is to use machine learning (ML) to develop and verify a predictive model, in order to explore the relationship between bladder pressure and AKI in patients with pancreatitis.</p> Methods <p>The clinical data from 223 patients with AP were extracted from the MIMIC-IV v2.2 database. The relationship between IBP and AKI is analyzed using restricted cubic splines. The Boruta algorithm is used to evaluate the prediction ability of IBP and select characteristic variables, and divide the data into a training set and verification set. Then, the ML algorithm is used to establish the prediction model. The predictive performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). SHAP (Shapley addition explanation) is used to explain the ML model.</p> Results <p>A total of 223 patients with AP were included in this study. Restricted cubic splines showed that there was a “J-shaped” correlation between IBP and AKI, indicating that the increase in patients’ IBP was significantly related to the increase in the risk of AKI (IBP ≥ 18.03, OR = 1.13(1.03–1.28), <i>P</i> = 0.05). Using the Boruta algorithm, ten variables were determined for model development: creatinine, blood urea nitrogen (BUN), bladder pressure, calcium, vasopressin, apsiii score, urine output, potassium, sofa score, and base_excess. According to the area under the ROC curve, calibration curve, and DCA results of the training set, the XGBoost model showed excellent performance. The F1-score (harmonic mean of precision and recall) in the test set was 0.95, and the area under the receiver operating characteristic curve (AUC) was 0.968. SHAP-based bar charts and waterfall charts are used to explain the XGBoost model both globally and locally.</p> Conclusion <p>The relationship between IBP and AKI in patients with pancreatitis is J-shaped. The XGBoost model has the best predictive performance and can be used to help clinicians identify high-risk patients and implement early interventions to reduce the incidence of AKI.</p>

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Relationship between intra-bladder pressure and acute kidney injury in patients with acute pancreatitis: interpretable machine learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月13日

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  • Gang Liao,
  • Baning Ye,
  • Jianquan Li,
  • Mingxiang Wen

摘要

Background

Acute kidney injury (AKI) is a common and serious complication of acute pancreatitis (AP), which greatly increases the economic burden and mortality. Measuring intra-abdominal pressure (IAP) is very important for the management of patients with AP, and intra-bladder pressure (IBP) is an indirect indicator of IAP. However, research on the relationship between IBP and AKI in patients with AP is limited. Therefore, the purpose of this study is to use machine learning (ML) to develop and verify a predictive model, in order to explore the relationship between bladder pressure and AKI in patients with pancreatitis.

Methods

The clinical data from 223 patients with AP were extracted from the MIMIC-IV v2.2 database. The relationship between IBP and AKI is analyzed using restricted cubic splines. The Boruta algorithm is used to evaluate the prediction ability of IBP and select characteristic variables, and divide the data into a training set and verification set. Then, the ML algorithm is used to establish the prediction model. The predictive performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). SHAP (Shapley addition explanation) is used to explain the ML model.

Results

A total of 223 patients with AP were included in this study. Restricted cubic splines showed that there was a “J-shaped” correlation between IBP and AKI, indicating that the increase in patients’ IBP was significantly related to the increase in the risk of AKI (IBP ≥ 18.03, OR = 1.13(1.03–1.28), P = 0.05). Using the Boruta algorithm, ten variables were determined for model development: creatinine, blood urea nitrogen (BUN), bladder pressure, calcium, vasopressin, apsiii score, urine output, potassium, sofa score, and base_excess. According to the area under the ROC curve, calibration curve, and DCA results of the training set, the XGBoost model showed excellent performance. The F1-score (harmonic mean of precision and recall) in the test set was 0.95, and the area under the receiver operating characteristic curve (AUC) was 0.968. SHAP-based bar charts and waterfall charts are used to explain the XGBoost model both globally and locally.

Conclusion

The relationship between IBP and AKI in patients with pancreatitis is J-shaped. The XGBoost model has the best predictive performance and can be used to help clinicians identify high-risk patients and implement early interventions to reduce the incidence of AKI.