Wang Yixi, Luo Xinkai, Wang Jingjie, Li Wenzhe, Cui Jian, Li Yuqian
Department of Minimally Invasive Spine and Precision Orthopedics, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
State Key Laboratory of PathogenesisPrevention and Treatment of High Incidence Diseases in Central Asia, Xinjiang Medical University, Urumqi, China.
Neurocrit Care. 2025 Jun 25. doi: 10.1007/s12028-025-02308-y.
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.
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.
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.
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.
创伤性脊髓损伤（TSCI）是一种严重的中枢神经系统损伤，尽管治疗取得了进展，但TSCI重症患者面临着较高的短期死亡率。本研究利用机器学习整合标准重症监护病房（ICU）指标，识别TSCI 7天高死亡风险患者以优化治疗。
本研究使用重症监护医学信息数据库2.2中TSCI重症患者的数据，采用Boruta和LASSO回归算法识别关键特征，使用包括自适应增强、分类增强、梯度提升机、k近邻、轻量级梯度提升机、逻辑回归、神经网络、随机森林（RF）、支持向量机和极端梯度提升在内的十种机器学习算法，建立TSCI重症患者7天死亡风险预测模型。通过受试者工作特征曲线、校准曲线、决策曲线分析、准确性、敏感性、特异性、精确性和F1分数评估模型性能，而Shapley加性解释确保模型的可解释性。使用新疆医科大学第一附属医院ICU的数据进行外部验证，进一步评估模型的通用性。
本研究分别从重症监护医学信息数据库和新疆医科大学第一附属医院ICU收集了261例和45例TSCI重症患者的数据，确定了模型开发的十个关键特征，其中RF模型在原始数据集和合成少数过采样技术平衡合成数据集的受试者工作特征曲线、校准曲线、决策曲线分析和性能指标方面始终优于其他模型。Shapley加性解释分析突出了最低体温、最低收缩压和Charlson合并症指数是RF模型中的关键预测因素。外部验证初步证明了该模型的稳健性和临床适用性，从而产生了一个在线计算器，使临床医生能够估计TSCI重症患者的7天生存概率。
RF模型在预测TSCI重症患者7天死亡风险方面表现出良好性能，表明其在支持临床决策方面的潜在效用。
