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Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated delirium
Xiaoli Shen1, Dongfeng Shang2, Weize Sun1
1Department of Emergency Medicine, Huishan 3rd People's Hospital of Wuxi City, Wuxi, China.
Abstract:
This study aimed to develop models for predicting the 30-day mortality of sepsis-associated delirium (SAD) by multiple machine learning (ML) algorithms. On the whole, a cohort of 3,197 SAD patients were collected from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Among them, a total of 659 (20.61%) patients died following SAD. The patients who died were about 73.00 (62.00, 82.00) years old and mostly male (56.75%). Recursive feature elimination (RFE) was used to distinguish risk factors. Subsequently, six ML algorithms including artificial neural network (NNET), gradient boosting machine (GBM), adaptive boosting (Ada), random forest (RF), eXtreme Gradient Boosting (XGB) and logistic regression (LR) were employed to establish models to predict the 30-day mortality of SAD. The performance of models was assessed via both discrimination and calibration by cross-validation with 100 resamples. Overall, 10 independent predictors, including Glasgow Coma Scale (GCS), Sequential Organ Failure Assessment (SOFA), anion gap (AG), continuous renal replacement therapy (CRRT), temperature, mean corpuscular hemoglobin concentration (MCHC), vasopressor, blood urea nitrogen (BUN), base excess (BE), and bicarbonate were identified as independent predictors for the 30-day mortality of SAD. The validation cohort demonstrated that all these six models had relatively favorable differentiation, while among them, the GBM model had the highest area under the curve (AUC) of 0.845 (95% Confidence Interval (CI): 0.816, 0.874). Furthermore, the calibration curve of these six models was close to the diagonal line in the validation sets. As for decision curve analysis, the predictive models were clinically useful as well. Based on real-world research, we developed ML models to provide personalized predictions of delirium-related mortality in sepsis patients, potentially enabling clinicians to identify high-risk SAD patients more promptly.
Insights
Machine learning models predict 30-day mortality in sepsis-associated delirium (SAD) patients. The Gradient Boosting Machine model showed the highest accuracy, aiding early identification of high-risk individuals.
Area of Science:
- Critical Care Medicine
- Medical Informatics
- Computational Biology
Background:
- Sepsis-associated delirium (SAD) is a critical condition with significant mortality.
- Predicting 30-day mortality in SAD patients is crucial for timely intervention.
- Existing prediction models may lack the precision required for personalized patient care.
Purpose of the Study:
- To develop and validate machine learning (ML) models for predicting 30-day mortality in sepsis-associated delirium (SAD) patients.
- To identify key independent predictors of mortality in SAD.
- To compare the performance of various ML algorithms in predicting SAD mortality.
Main Methods:
- Utilized a cohort of 3,197 SAD patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database.
- Employed Recursive Feature Elimination (RFE) to identify risk factors.
- Developed and validated six ML models: NNET, GBM, Ada, RF, XGB, and LR, assessing performance via cross-validation and decision curve analysis.
Main Results:
- Identified 10 independent predictors of 30-day mortality: GCS, SOFA, AG, CRRT, temperature, MCHC, vasopressor, BUN, BE, and bicarbonate.
- All six ML models demonstrated favorable discrimination and calibration.
- The Gradient Boosting Machine (GBM) model achieved the highest Area Under the Curve (AUC) of 0.845 (95% CI: 0.816, 0.874).
Conclusions:
- Developed robust ML models for predicting 30-day mortality in SAD patients.
- The GBM model offers superior predictive performance compared to other tested algorithms.
- These models can assist clinicians in promptly identifying high-risk SAD patients for personalized management.
