Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated encephalopathy

Peng, Liwei; Peng, Chi Hsien; Yang, Fan; Wang, Jian; Zuo, Wei; Cheng, Chih‐Tao; Mao, Zilong; Jin, Zhichao; Li, Weixin

doi:10.1186/s12874-022-01664-z

Cited by 23 publications

(12 citation statements)

References 43 publications

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“…However, early diagnosis and appropriate interventions can help improve the neurological prognosis of patients with SAE [13] . In previous studies focusing on machine learning risk prediction models for SAE patients, a 30-day mortality risk prediction model for SAE patients constructed by Peng et al [14] . showed that the Adapting Boosting model exhibited the best discrimination ability in the validation set, with an AUC of 0.834, and the AUC for XGBoost was 0.833.…”

Section: Discussionmentioning

confidence: 99%

WITHDRAWN: Development of a 28-Day Mortality Risk Prediction Model for ICU Sepsis-Associated Encephalopathy Using Interpretable Machine Learning

YANG,

ZHAO,

Zhang

et al. 2024

Preprint

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In the ICU, patients with sepsis often develop sepsis-associated encephalopathy (SAE), which affects their prognosis. This study aims to construct a predictive model for the 28-day mortality risk of SAE patients using machine learning (ML) methods. We retrospectively collected clinical data of SAE patients admitted to our hospital's intensive care unit (ICU) from January 2018 to June 2023. The primary outcome was whether the patient died within 28 days. We employed six popular machine learning methods to build the predictive model for the 28-day mortality risk of SAE patients, including logistic regression (LR), Gaussian naive Bayes (GaussianNB), support vector machine (SVM), k-nearest neighbor (kNN), random forest (RF), and extreme gradient boosting (XGBoost). Various evaluation metrics were used to analyze the predictive performance of the models. The SHAP analysis method ranked the importance of features influencing the model's output and provided visual output and explanations for individual samples, meeting the need of clinicians to understand model outputs and personalized predictions. In total, this cohort study enrolled 506 SAE patients, with 243 cases (48.02%) resulting in death within 28 days. Overall, the XGBoost model demonstrated superior and stable performance, with the area under the receiver operating characteristic curve (AUC) for both the training and validation sets being higher than the other models, at 0.986 and 0.848, respectively. The SHAP summary plot revealed important clinical features associated with the risk of mortality within 28 days for SAE patients, with a strong dependence on age, SOFA score, and NEUT. Our study indicates that the XGBoost model has good predictive capability for the short-term prognostic outcomes of SAE patients in the ICU and can assist clinicians in the early identification of high-risk patients and the timely implementation of effective treatment strategies to improve the clinical outcomes of SAE patients.

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Section: Discussionmentioning

confidence: 99%

WITHDRAWN: Development of a 28-Day Mortality Risk Prediction Model for ICU Sepsis-Associated Encephalopathy Using Interpretable Machine Learning

YANG,

ZHAO,

Zhang

et al. 2024

Preprint

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“…It may be the reason why the mortality in our study is lower. The other study based on MIMIC-IV reported 17.62% of patients with SAE died in 30 days which was comparable with our results ( 22 ).…”

Section: Discussionsupporting

confidence: 93%

The nomogram to predict the occurrence of sepsis-associated encephalopathy in elderly patients in the intensive care units: A retrospective cohort study

et al. 2023

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BackgroundSepsis-associated encephalopathy (SAE) is a critical and common problem in elderly patients with sepsis, which is still short of efficient predictive tools. Therefore, this study aims to screen the risk factors and establish a useful predictive nomogram for SAE in elderly patients with sepsis in the intensive care unit (ICU).Patients and methodsElderly patients (age ≥ 65 years) with sepsis were selected from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Data from demographics and laboratory examinations were collected on the first day of admission to the ICU. SAE was defined by two criteria in the presence of sepsis: ① a Glasgow Coma Scale (GCS) score of < 15 or ② delirium. Differences in demographics and laboratory tests were calculated between SAE and non-SAE groups. Participants were randomly divided into a training set and a validation set without replacement at a ratio of 6:4. A predictive nomogram was constructed in the training set by logistic regression analysis and then validated. The predictive capability of the nomogram was demonstrated by receiver operating characteristic (ROC) analysis and calibration curve analysis.ResultsA total of 22,361 patients were selected, of which 2,809 patients (12.7%) died in the hospital and 8,290 patients (37.1%) had SAE. In-hospital mortality in the SAE group was higher than that in the non-SAE group (18.8 vs. 8.9%, p < 0.001). Based on the results of logistic regression analysis, a nomogram integrating age, Na+, Sequential Organ Failure Assessment (SOFA) score, heart rate, and body temperature were constructed. The area under the curve (AUC) of the nomogram was 80.2% in the training set and 80.9% in the validation set. Calibration curve analysis showed a good predictive capacity of the nomogram.ConclusionSAE is an independent risk of in-hospital mortality in elderly patients in the intensive care unit. The nomogram has an excellent predictive capability of SAE and helps in clinical practice.

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“…Their evaluation, including metrics such as AUC, accuracy, and calibration performance, adds valuable insights to the understanding of prognostic modeling in critical care settings. 23 In the context of our study, we used 10 ML models based on the MIMIC-IV database to predict in-hospital mortality risk among patients with SAE, with the stacked ensemble model emerging as the most effective.…”

Section: Discussionmentioning

confidence: 99%

Enhancing predictions with a stacking ensemble model for ICU mortality risk in patients with sepsis-associated encephalopathy

Liu,

Niu,

Peng

2024

J Int Med Res

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Objective We identified predictive factors and developed a novel machine learning (ML) model for predicting mortality risk in patients with sepsis-associated encephalopathy (SAE). Methods In this retrospective cohort study, data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database were used for model development and external validation. The primary outcome was the in-hospital mortality rate among patients with SAE; the observed in-hospital mortality rate was 14.74% (MIMIC IV: 1112, eICU: 594). Using the least absolute shrinkage and selection operator (LASSO), we built nine ML models and a stacking ensemble model and determined the optimal model based on the area under the receiver operating characteristic curve (AUC). We used the Shapley additive explanations (SHAP) algorithm to determine the optimal model. Results The study included 9943 patients. LASSO identified 15 variables. The stacking ensemble model achieved the highest AUC on the test set (0.807) and 0.671 on external validation. SHAP analysis highlighted Glasgow Coma Scale (GCS) and age as key variables. The model ( https://sic1.shinyapps.io/SSAAEE/ ) can predict in-hospital mortality risk for patients with SAE. Conclusions We developed a stacked ensemble model with enhanced generalization capabilities using novel data to predict mortality risk in patients with SAE.

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Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated encephalopathy

Cited by 23 publications

References 43 publications

WITHDRAWN: Development of a 28-Day Mortality Risk Prediction Model for ICU Sepsis-Associated Encephalopathy Using Interpretable Machine Learning

WITHDRAWN: Development of a 28-Day Mortality Risk Prediction Model for ICU Sepsis-Associated Encephalopathy Using Interpretable Machine Learning

The nomogram to predict the occurrence of sepsis-associated encephalopathy in elderly patients in the intensive care units: A retrospective cohort study

Enhancing predictions with a stacking ensemble model for ICU mortality risk in patients with sepsis-associated encephalopathy

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