An Early Predictive Scoring Model for In-Hospital Cardiac Arrest of Emergent Hemodialysis Patients

Chen, Shih-Hao; Cheng, Ya-Yun; Lin, Chih‐Hao

doi:10.3390/jcm10153241

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…The HB were included in the XGBoost multiple regression equation; Meanwhile, the man, PT and MBP were included in the LASSO multiple regression equation. Consistent with previous studies, our study also took age, man, HR, SPO 2 , and lower MBP values as independent predictors [32,35,36]. After CA, the occurrence of tissue ischemia and hypoxia will lead to the decrease of pH value and increase of lactate value.…”

Section: Discussionsupporting

confidence: 82%

Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -IV database based on machine learning

Sun

Ren

et al. 2023

BMC Anesthesiol

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Background Both in-hospital cardiac arrest (IHCA) and out-of-hospital cardiac arrest (OHCA) have higher incidence and lower survival rates. Predictors of in-hospital mortality for intensive care unit (ICU) admitted cardiac arrest (CA) patients remain unclear. Methods The Medical Information Mart for Intensive Care IV (MIMIC-IV) database was used to perform a retrospective study. Patients meeting the inclusion criteria were identified from the MIMIC-IV database and randomly divided into training set (n = 1206, 70%) and validation set (n = 516, 30%). Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Independent risk factors for in-hospital mortality were screened using the least absolute shrinkage and selection operator (LASSO) regression model and the extreme gradient boosting (XGBoost) in the training set. Multivariate logistic regression analysis was used to build prediction models in training set, and then validated in validation set. Discrimination, calibration and clinical utility of these models were compared using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). After pairwise comparison, the best performing model was chosen to build a nomogram. Results Among the 1722 patients, in-hospital mortality was 53.95%. In both sets, the LASSO, XGBoost,the logistic regression(LR) model and the National Early Warning Score 2 (NEWS 2) models showed acceptable discrimination. In pairwise comparison, the prediction effectiveness was higher with the LASSO,XGBoost and LR models than the NEWS 2 model (p < 0.001). The LASSO,XGBoost and LR models also showed good calibration. The LASSO model was chosen as our final model for its higher net benefit and wider threshold range. And the LASSO model was presented as the nomogram. Conclusions The LASSO model enabled good prediction of in-hospital mortality in ICU admission CA patients, which may be widely used in clinical decision-making.

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Section: Discussionsupporting

confidence: 82%

Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -IV database based on machine learning

Sun

Ren

et al. 2023

BMC Anesthesiol

View full text Add to dashboard Cite

show abstract

“…Consistent with previous studies, our study also took age, man, HR, SPO 2 , and lower MBP values as independent predictors. (24,27,28) After CA, the occurrence of tissue ischemia and hypoxia will lead to the decrease of pH value and increase of lactate value. In OHCA patients, the pH value was related to the neurological state of the patient at discharge, which may help predict the adverse neurological state of the patient at discharge.…”

Section: Discussionmentioning

confidence: 99%

Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -Ⅳ database based on machine learning

Sun

Ren

et al. 2023

Preprint

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Background: Both in-hospital cardiac arrest (IHCA) and out-of-hospital cardiac arrest (OHCA) have higher incidence and lower survival rates. Predictors of in-hospital mortality for intensive care unit (ICU) admitted cardiac arrest (CA) patients remain unclear. Methods: The Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database was used to perform a retrospective study. Patients meeting the inclusion criteria were identified from the MIMIC-Ⅳ database and randomly divided into training set (n=1206, 70%) and validation set (n=516, 30%). Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Independent risk factors for in-hospital mortality were screened using the least absolute shrinkage and selection operator (LASSO) regression model and the extreme gradient boosting (XGBoost) in the training set. Multivariate logistic regression analysis was used to build prediction models in training set, and then validated in validation set. Discrimination, calibration and clinical utility of these models were compared using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). After pairwise comparison, the best performing model was chosen to build a nomogram. Results: Among the 1722 patients, in-hospital mortality was 52.43%. In both sets, the LASSO, XGBoost and The National Early Warning Score 2 (NEWS 2) models showed acceptable discrimination. In pairwise comparison, the prediction effectiveness was higher with the LASSO and XGBoost models than with the NEWS 2 model (p<0.001). The LASSO and XGBoost models also showed good calibration. The LASSO model was chosen as our final model for its higher net benefit and was presented as the nomogram. Conclusions: The LASSO model enabled good prediction of in-hospital mortality in ICU admission CA patients, which may be widely used in clinical decision-making.

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Research Progress of In-Hospital Cardiac Ar-rest Early Warning Model Based on Machine Learning

夏来百提姑

2024

ACM

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An Early Predictive Scoring Model for In-Hospital Cardiac Arrest of Emergent Hemodialysis Patients

Cited by 3 publications

References 29 publications

Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -IV database based on machine learning

Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -IV database based on machine learning

Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -Ⅳ database based on machine learning

Research Progress of In-Hospital Cardiac Ar-rest Early Warning Model Based on Machine Learning

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