In Shock (Augusta, Ga.)
BACKGROUND : Acute kidney injury (AKI) is a prevalent and serious complication among patients with sepsis-associated acute respiratory distress syndrome (ARDS). Prompt and accurate prediction of AKI has an important role in timely intervention, ultimately improving the patients' survival rate. This study aimed to establish machine learning models to predict AKI via thorough analysis of data derived from electronic medical records.
METHOD : The data of eligible patients were retrospectively collected from the Medical Information Mart for Intensive Care III (MIMIC-III) database from 2001 to 2012. The primary outcome was the development of AKI within 48 hours after ICU admission. Four different machine learning models were established based on logistic regression (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). The performance of all predictive models was evaluated using the area under receiver operating characteristic curve (AUC), precision-recall curve, confusion matrix, and calibration plot. Moreover, the discrimination ability of the machine learning models was compared with that of the Sequential Organ Failure Assessment (SOFA) model.
RESULTS : Among 1085 sepsis-associated ARDS patients included in this research, 375 patients (34.6%) developed AKI within 48 hours after ICU admission. Twelve predictive variables were selected and further used to establish the machine learning models. The XGBoost model yielded the most accurate predictions with the highest AUC (0.86) and accuracy (0.81). In addition, a novel shiny app based on the XGBoost model was established to predict the probability of developing AKI among patients with sepsis-associated ARDS.
CONCLUSION : Machine learning models could be used for predicting AKI in patients with sepsis-associated ARDS. Accordingly, a user-friendly shiny app based on the XGBoost model with reliable predictive performance was released online to predict the probability of developing AKI among patients with sepsis-associated ARDS.
Zhou Yang, Feng Jinhua, Mei Shuya, Zhong Han, Tang Ri, Xing Shunpeng, Gao Yuan, Xu Qiaoyi, He Zhengyu
2023-Jan-11
