A prediction model for 30-day mortality of sepsis patients based on intravenous fluids and electrolytes

Abstract

To establish a prediction model for the 30-day mortality in sepsis patients. The data of 1185 sepsis patients were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) and all participants were randomly divided into the training set (n = 829) and the testing set (n = 356). The model was established in the training set and verified in the testing set. After standardization of the data, age, gender, input, output, and variables with statistical difference between the survival group and the death group in the training set were involved in the extreme gradient boosting (XGBoost) model. Subgroup analysis was performed concerning age and gender in the testing set. In the XGBoost model with variables related to intravenous (IV) fluid management and electrolytes for the 30-day mortality of sepsis patients, the area under the curve (AUC) was 0.868 (95% confidence interval [CI]: 0.867–0.869) in the training set and 0.781 (95% CI: 0.779–0.782) in the testing set. The sensitivity was 0.815 (95% CI: 0.774–0.857) in the training set and 0.755 (95% CI: 0.686–0.825) in the testing set. The specificity was 0.761 (95% CI: 0.723–0.798) in the training set, and 0.737 (95% CI: 0.677–0.797) in the testing set. In the XGBoost forest model without variables related to IV fluid management and electrolytes for the 30-day mortality of sepsis patients, in the training set, the AUC was 0.830 (95% CI: 0.829–0.831), the sensitivity was 0.717 (95% CI: 0.669–0.765), the specificity was 0.797 (95% CI: 0.762–0.833), and the accuracy was 0.765 (95% CI: 0.736–0.794). In the testing set, the AUC was 0.751 (95% CI: 0.750–0.753), the sensitivity was 0.612 (95% CI: 0.533–0.691), the specificity was 0.756 (95% CI: 0.698–0.814), and the accuracy was 0.697(95% CI: 0.649–0.744). The prediction model including variables associated with IV fluids and electrolytes had good predictive value for the 30-day mortality of sepsis patients.