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Background <p>Sepsis-induced myocardial injury (SIMI) is a common complication in sepsis patients with poor prognosis. Consequently, its early accurate prediction is crucial for optimizing clinical management.</p> Methods <p>We collected data on 14,208 patients with sepsis from the MIMIC IV database, and used the Bootstrap Sampling to handle unbalanced data. 70% of sepsis patients were selected randomly from the MIMIC-IV database, and feature selection was performed using a Boruta algorithm. Six machine learning prediction models (Decision Tree, GBDT, AdaBoost, Logistic Regression, Random Forest, CatBoost) were constructed. The remaining 30% of patients were utilized to validate the model’s accuracy. Additionally, local data from the Intensive Care Unit (ICU) of Zunyi First People’s Hospital were employed for further model validation. Model performance was assessed using AUC, decision curve analysis, PR curves, calibration plots, and SHapley Additive Explanations (SHAP).</p> Results <p>A total of 1882 sepsis patients were enrolled, with a SIMI risk prediction of 43.3%. Univariate analysis identified significant differences in multiple indicators between SIMI and non-SIMI groups (<i>p</i> &lt; 0.05). Among six machine learning models, the CatBoost model exhibited the optimal performance, with an accuracy of 0.8690, AUROC of 0.9215, F1-score of 0.8452, and MCC of 0.7368. Five-fold cross-validation confirmed its stable performance (mean AUROC = 0.9183), and external validation showed good generalizability (AUROC = 0.7567, 95% CI: 0.6944–0.8191). SHAP analysis revealed that CK-MB, D-dimer, and NT-proBNP were the top three risk factors for SIMI.</p> Conclusions <p>The CatBoost model constructed in this study has high accuracy, stability, and generalizability for predicting SIMI risk prediction in sepsis patients. SHAP analysis confirms that CK-MB, D-dimer, and NT-proBNP stand out as the primary predictive factors for sepsis-induced myocardial injury (SIMI) risk. This model could assist clinicians in early identification of high-risk patients and guide targeted interventions, thereby improving patient prognosis.</p> Clinical trial number <p>Not applicable.</p>

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Machine learning-based prediction of sepsis-induced myocardial injury: external validation and SHAP interpretation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月20日

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  • Botao Li,
  • Shiyu Pi,
  • Min Xu,
  • Han Mu,
  • Xiaomin Liu,
  • Xinrong Huang,
  • Zelan Wu,
  • Zhisheng Zheng,
  • Yongkang Li,
  • Daiqin Wu,
  • Wei Liu,
  • Zhangrong Chen,
  • Wei Li,
  • Xia Li,
  • Fangjie Dai

摘要

Background

Sepsis-induced myocardial injury (SIMI) is a common complication in sepsis patients with poor prognosis. Consequently, its early accurate prediction is crucial for optimizing clinical management.

Methods

We collected data on 14,208 patients with sepsis from the MIMIC IV database, and used the Bootstrap Sampling to handle unbalanced data. 70% of sepsis patients were selected randomly from the MIMIC-IV database, and feature selection was performed using a Boruta algorithm. Six machine learning prediction models (Decision Tree, GBDT, AdaBoost, Logistic Regression, Random Forest, CatBoost) were constructed. The remaining 30% of patients were utilized to validate the model’s accuracy. Additionally, local data from the Intensive Care Unit (ICU) of Zunyi First People’s Hospital were employed for further model validation. Model performance was assessed using AUC, decision curve analysis, PR curves, calibration plots, and SHapley Additive Explanations (SHAP).

Results

A total of 1882 sepsis patients were enrolled, with a SIMI risk prediction of 43.3%. Univariate analysis identified significant differences in multiple indicators between SIMI and non-SIMI groups (p < 0.05). Among six machine learning models, the CatBoost model exhibited the optimal performance, with an accuracy of 0.8690, AUROC of 0.9215, F1-score of 0.8452, and MCC of 0.7368. Five-fold cross-validation confirmed its stable performance (mean AUROC = 0.9183), and external validation showed good generalizability (AUROC = 0.7567, 95% CI: 0.6944–0.8191). SHAP analysis revealed that CK-MB, D-dimer, and NT-proBNP were the top three risk factors for SIMI.

Conclusions

The CatBoost model constructed in this study has high accuracy, stability, and generalizability for predicting SIMI risk prediction in sepsis patients. SHAP analysis confirms that CK-MB, D-dimer, and NT-proBNP stand out as the primary predictive factors for sepsis-induced myocardial injury (SIMI) risk. This model could assist clinicians in early identification of high-risk patients and guide targeted interventions, thereby improving patient prognosis.

Clinical trial number

Not applicable.