Chen Xu, Yu Bin, Zhang Yaming, Wang Xin, Huang Danping, Gong Shaohui, Hu Wei
Shangrao People's Hospital, Shangrao, China.
Shangrao Municipal Hospital, Shangrao, China.
Front Neurol. 2025 Mar 19;16:1512297. doi: 10.3389/fneur.2025.1512297. eCollection 2025.
Accurately predicting the short-term in-hospital mortality risk for patients with stroke and TBI (Traumatic Brain Injury) is crucial for improving the quality of emergency medical care.
This study analyzed data from 2,125 emergency admission patients with stroke and traumatic brain injury at two Grade a hospitals in China from January 2021 to March 2024. LASSO regression was used for feature selection, and the predictive performance of logistic regression was compared with six machine learning algorithms. A 70:30 ratio was applied for cross-validation, and confidence intervals were calculated using the bootstrap method. Temporal validation was performed on the best-performing model. SHAP values were employed to assess variable importance.
The random forest algorithm excelled in predicting in-hospital 3-day mortality, achieving an AUC of 0.978 (95% CI: 0.966-0.986). Time series validation demonstrated the model's strong generalization capability, with an AUC of 0.975 (95% CI: 0.963-0.986). Key predictive factors in the final model included metabolic syndrome, NEWS2 score, Glasgow Coma Scale (GCS), whether surgery was performed, bowel movement status, potassium level (K), aspartate transaminase (AST) level, and temporal factors. SHAP value analysis further confirmed the significant contributions of these variables to the predictive outcomes. The random forest model developed in this study demonstrates good accuracy in predicting short-term in-hospital mortality rates for stroke and traumatic brain injury patients. The model integrates emergency scores, clinical signs, and key biochemical indicators, providing a comprehensive perspective for risk assessment. This approach, which incorporates emergency data, holds promise for assisting decision-making in clinical practice, thereby improving patient outcomes.
准确预测中风和创伤性脑损伤（TBI）患者的短期住院死亡率风险对于提高急诊医疗质量至关重要。
本研究分析了2021年1月至2024年3月期间中国两家甲级医院2125例中风和创伤性脑损伤急诊入院患者的数据。采用LASSO回归进行特征选择，并将逻辑回归的预测性能与六种机器学习算法进行比较。采用70:30的比例进行交叉验证，并使用自助法计算置信区间。对表现最佳的模型进行时间验证。采用SHAP值评估变量重要性。
随机森林算法在预测3天住院死亡率方面表现出色，AUC为0.978（95%CI：0.966 - 0.986）。时间序列验证表明该模型具有很强的泛化能力，AUC为0.975（95%CI：0.963 - 0.986）。最终模型中的关键预测因素包括代谢综合征、NEWS2评分、格拉斯哥昏迷量表（GCS）、是否进行手术、排便状态、血钾水平（K）、天冬氨酸转氨酶（AST）水平和时间因素。SHAP值分析进一步证实了这些变量对预测结果的显著贡献。本研究开发的随机森林模型在预测中风和创伤性脑损伤患者的短期住院死亡率方面显示出良好的准确性。该模型整合了急诊评分、临床体征和关键生化指标，为风险评估提供了全面的视角。这种纳入急诊数据的方法有望协助临床实践中的决策制定，从而改善患者预后。
