Neurocritical care · 2025
Development and Validation of Machine Learning Models for Predicting 7-Day Mortality in Critically Ill Patients with Traumatic Spinal Cord Injury: A Multicenter Retrospective Study.
СтатьяМногоцентровое исследованиеВалидационное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Wang Yixi, Luo Xinkai, Wang Jingjie, Li Wenzhe, Cui Jian, Li Yuqian
Аннотация
BACKGROUND: Traumatic spinal cord injury (TSCI), a severe central nervous system injury, despite treatment advances, critically ill patients with TSCI face high short-term mortality. This study leverages machine learning to integrate standard intensive care unit (ICU) indicators, identifying 7-day high-mortality risk patients with TSCI to optimize treatment.
METHODS: Using critically ill patients with TSCI data from the Medical Information Mart for Intensive Care 2.2 database, this study employs the Boruta and LASSO regression algorithms to identify key features, developing a 7-day mortality risk prediction model in critically ill patients with TSCI using ten machine learning algorithms including Adaptive Boosting, Categorical Boosting, Gradient Boosting Machine, k-Nearest Neighbors, Light Gradient Boosting Machine, Logistic Regression, Neural Network, Random Forest (RF), Support Vector Machine, and Extreme Gradient Boosting. Model Performance is evaluated via receiver operating characteristic curves, calibration curves, decision curve analysis, accuracy, sensitivity, specificity, precision, and F1 score, whereas Shapley Additive Explanations ensure model interpretability. External validation with ICU data from the First Affiliated Hospital of Xinjiang Medical University further assesses the model's generalizability.
RESULTS: This study, collecting data from 261 and 45 critically ill patients with TSCI from the Medical Information Mart for Intensive Care database and the First Affiliated Hospital of Xinjiang Medical University's ICU, respectively, identified ten key features for model development, in which the RF model consistently outperformed others across raw and Synthetic Minority Over-sampling Technique-balanced synthetic datasets in receiver operating characteristic curves, calibration curves, decision curve analysis, and performance metrics. Shapley Additive Explanation analysis highlighted minimum body temperature, lowest systolic blood pressure, and Charlson Comorbidity Index as critical predictors in the RF model. External validation initially demonstrated the model's robustness and clinical applicability, leading to an online calculator that enables clinicians to estimate the 7-day survival probability of critically ill patients with TSCI.
CONCLUSIONS: The RF model exhibits favorable performance in predicting 7-day mortality risk among critically ill patients with TSCI, indicating its potential utility in supporting clinical decision-making.
METHODS: Using critically ill patients with TSCI data from the Medical Information Mart for Intensive Care 2.2 database, this study employs the Boruta and LASSO regression algorithms to identify key features, developing a 7-day mortality risk prediction model in critically ill patients with TSCI using ten machine learning algorithms including Adaptive Boosting, Categorical Boosting, Gradient Boosting Machine, k-Nearest Neighbors, Light Gradient Boosting Machine, Logistic Regression, Neural Network, Random Forest (RF), Support Vector Machine, and Extreme Gradient Boosting. Model Performance is evaluated via receiver operating characteristic curves, calibration curves, decision curve analysis, accuracy, sensitivity, specificity, precision, and F1 score, whereas Shapley Additive Explanations ensure model interpretability. External validation with ICU data from the First Affiliated Hospital of Xinjiang Medical University further assesses the model's generalizability.
RESULTS: This study, collecting data from 261 and 45 critically ill patients with TSCI from the Medical Information Mart for Intensive Care database and the First Affiliated Hospital of Xinjiang Medical University's ICU, respectively, identified ten key features for model development, in which the RF model consistently outperformed others across raw and Synthetic Minority Over-sampling Technique-balanced synthetic datasets in receiver operating characteristic curves, calibration curves, decision curve analysis, and performance metrics. Shapley Additive Explanation analysis highlighted minimum body temperature, lowest systolic blood pressure, and Charlson Comorbidity Index as critical predictors in the RF model. External validation initially demonstrated the model's robustness and clinical applicability, leading to an online calculator that enables clinicians to estimate the 7-day survival probability of critically ill patients with TSCI.
CONCLUSIONS: The RF model exhibits favorable performance in predicting 7-day mortality risk among critically ill patients with TSCI, indicating its potential utility in supporting clinical decision-making.