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Abstract: TH-PO038

The Prediction of In-Hospital Mortality in Elderly Patients with Sepsis-Associated AKI Utilizing Machine Learning Models

Session Information

Category: Augmented Intelligence, Digital Health, and Data Science

  • 300 Augmented Intelligence, Digital Health, and Data Science

Authors

  • He, Xin, Xiangya Hospital Central South University, Changsha, Hunan, China
  • Yuan, Qiongjing, Xiangya Hospital Central South University, Changsha, Hunan, China
  • Peng, Zhangzhe, Xiangya Hospital Central South University, Changsha, Hunan, China
Background

Sepsis-associated acute kidney injury (SA-AKI) is a severe complication associated with poorer prognosis and increased mortality, particularly in elderly patients with sepsis. Currently, there is a lack of accurate mortality risk prediction models for these patients in clinic. This study aimed to develop and validate machine learning models for predicting in-hospital mortality risk in elderly patients with SA-AKI.

Methods

Machine learning models were developed and validated using the public, high-quality Medical Information Mart for Intensive Care (MIMIC)-IV critically ill database. The recursive feature elimination (RFE) algorithm was employed for key feature selection. Eleven predictive models were compared, with the best one selected for further validation. Shapley Additive Explanations (SHAP) values were used for visualization and interpretation, making the machine learning models clinically interpretable.

Results

A total of 8,426 SA-AKI patients were included in this study (median age: 77.0 years; female: 45%). They were randomly divided into a training cohort (5,934, 70%) and a validation cohort (2,492, 30%). Nine key features were selected by the RFE algorithm. The CatBoost model achieved the best performance, with an AUC of 0.844 in the training cohort and 0.804 in the validation cohort. SHAP values revealed that AKI stage, PaO2, and lactate were the top three most important features contributing to the CatBoost model.

Conclusion

We developed a model capable of predicting the risk of in-hospital mortality in elderly patients with SA-AKI.

Flowchart of this study