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Machine Learning Approach for Sepsis Risk Assessment in Ischemic Stroke Patients
Fengkai Mao1, Leqing Lin2, Dongcheng Liang2
1Clinical Medical College, Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou, Zhejiang, China.
Journal of Intensive Care Medicine
|January 9, 2025
Summary
This study developed the first machine learning model to predict sepsis in ischemic stroke patients, identifying key risk factors like mechanical ventilation and low Glasgow Coma Scale scores. The model aims to improve early detection and reduce mortality in this vulnerable population.
Area of Science:
- Neurology
- Infectious Disease
- Medical Informatics
Background:
- Sepsis is a dangerous complication for ischemic stroke patients in the ICU.
- No existing models predict sepsis onset in this patient group.
- Early prediction is crucial for timely intervention and improved outcomes.
Purpose of the Study:
- To develop and validate the first predictive model for sepsis in ischemic stroke patients.
- To leverage machine learning techniques for accurate sepsis risk assessment.
- To provide a clinical decision support tool for early identification of high-risk individuals.
Main Methods:
- Utilized data from the MIMIC-IV database, including 2238 adult ischemic stroke patients.
- Employed Least Absolute Shrinkage and Selection Operator (LASSO) regression for feature selection (28 variables).
- Trained and validated multiple machine learning algorithms, selecting XGBoost as the optimal model based on performance metrics (AUC 0.863).
- Interpreted the XGBoost model using SHAP analysis to identify key predictive features.
Main Results:
- The XGBoost model achieved a high predictive performance with an AUC of 0.863.
- Key predictors of sepsis included invasive mechanical ventilation, excessive body weight, low Glasgow Coma Scale verbal score, age, and elevated body temperature.
- A user-friendly platform was developed for clinical application of the predictive model.
Conclusions:
- The developed XGBoost model is the first to accurately predict sepsis in ischemic stroke patients.
- This model serves as a valuable clinical decision support tool for early risk identification.
- Implementation can facilitate preventive measures, potentially reducing sepsis incidence and mortality in this population.
