Optimal Oxygen Saturation Targets in Patients with Sepsis- Associated Encephalopathy: A Cohort Study from the MIMIC-IV Database

Zhao, Lina; Wang, Yunying; Ge, Zengzheng; Zhu, Huadong; Li, Yang

doi:10.21203/rs.3.rs-478223/v1

Cited by 2 publications

References 27 publications

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Prediction and risk assessment of sepsis-associated encephalopathy in ICU based on interpretable machine learning

Kang

Zhou

et al. 2022

Sci Rep

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Sepsis-associated encephalopathy (SAE) is a major complication of sepsis and is associated with high mortality and poor long-term prognosis. The purpose of this study is to develop interpretable machine learning models to predict the occurrence of SAE after ICU admission and implement the individual prediction and analysis. Patients with sepsis admitted to ICU were included. SAE was diagnosed as glasgow coma score (GCS) less than 15. Statistical analysis at baseline was performed between SAE and non-SAE. Six machine learning classifiers were employed to predict the occurrence of SAE, and the adjustment of model super parameters was performed by using Bayesian optimization method. Finally, the optimal algorithm was selected according to the prediction efficiency. In addition, professional physicians were invited to evaluate our model prediction results for further quantitative assessment of the model interpretability. The preliminary analysis of variance showed significant differences in the incidence of SAE among patients with pathogen infection. There were significant differences in physical indicators like respiratory rate, temperature, SpO2 and mean arterial pressure (P < 0.001). In addition, the laboratory results were also significantly different. The optimal classification model (XGBoost) indicated that the best risk factors (cut-off points) were creatinine (1.1 mg/dl), mean respiratory rate (18), pH (7.38), age (72), chlorine (101 mmol/L), sodium (138.5 k/ul), SAPSII score (23), platelet count (160), and phosphorus (2.4 and 5.0 mg/dL). The ranked features derived from the best model (AUC is 0.8837) were mechanical ventilation, duration of mechanical ventilation, phosphorus, SOFA score, and vasopressin usage. The SAE risk prediction model based on XGBoost created here can make very accurate predictions using simple indicators and support the visual explanation. The interpretable model was effectively evaluated by professional physicians and can help them predict the occurrence of SAE more intuitively.

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Prediction and risk assessment of sepsis-associated encephalopathy in ICU based on interpretable machine learning

Kang

Zhou

et al. 2022

Sci Rep

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Prediction of All-cause Mortality with Sepsis-associated Encephalopathy in the ICU Based on Interpretable Machine Learning

Zhu

Gui

et al. 2022

2022 IEEE International Conference on Mechatronics and Automation (ICMA)

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Optimal Oxygen Saturation Targets in Patients with Sepsis- Associated Encephalopathy: A Cohort Study from the MIMIC-IV Database

Cited by 2 publications

References 27 publications

Prediction and risk assessment of sepsis-associated encephalopathy in ICU based on interpretable machine learning

Prediction and risk assessment of sepsis-associated encephalopathy in ICU based on interpretable machine learning

Prediction of All-cause Mortality with Sepsis-associated Encephalopathy in the ICU Based on Interpretable Machine Learning

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