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A prediction model for 30-day mortality of sepsis patients based on intravenous fluids and electrolytes
1Department of Critical Care Medicine, The Second Affiliated Hospital of Dalian Medical University, Dalian, China.
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.
Insights
A new prediction model using extreme gradient boosting (XGBoost) effectively identifies 30-day mortality risk in sepsis patients. Incorporating intravenous fluid and electrolyte data significantly improved prediction accuracy for sepsis mortality.
Area of Science:
- Critical Care Medicine
- Health Informatics
- Machine Learning in Healthcare
Background:
- Sepsis poses a significant threat to patient survival, with accurate mortality prediction crucial for timely intervention.
- Existing prediction models may not fully capture the complex interplay of factors influencing sepsis outcomes.
Purpose of the Study:
- To develop and validate an extreme gradient boosting (XGBoost) model for predicting 30-day mortality in sepsis patients.
- To evaluate the impact of intravenous (IV) fluid management and electrolyte variables on prediction accuracy.
Main Methods:
- Utilized data from 1185 sepsis patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database.
- Randomly divided data into training (n=829) and testing (n=356) sets.
- Developed an XGBoost model incorporating demographic, input/output, and statistically significant variables, including IV fluid and electrolyte data.
Main Results:
- The XGBoost model incorporating IV fluid and electrolyte variables achieved an Area Under the Curve (AUC) of 0.868 in the training set and 0.781 in the testing set.
- This model demonstrated a sensitivity of 0.755 and specificity of 0.737 in the testing set.
- A model excluding IV fluid and electrolyte variables showed lower predictive performance in both training and testing sets.
Conclusions:
- The developed XGBoost prediction model, particularly when including IV fluid and electrolyte variables, demonstrates good predictive value for 30-day mortality in sepsis patients.
- This model can aid clinicians in risk stratification and management of sepsis.
- Further research may explore additional variables to enhance predictive accuracy.
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