Abstract：
OBJECTIVE: This study aims to utilize machine learning (ML) and logistic regression (LR) models to predict surgical outcomes among patients with traumatic brain injury (TBI) based on admission examination, assisting in making optimal surgical treatment decision for these patients.
METHODS: We conducted a retrospective review of patients hospitalized in our department for moderate-to-severe TBI. Patients admitted between October 2011 and October 2022 were assigned to the training set, while patients admitted between November 2022 and May 2023 were designated as the external validation set. Five ML algorithms and LR model were employed to predict the postoperative Glasgow Outcome Scale (GOS) status at discharge using clinical and routine blood data collected upon admission. The Shapley (SHAP) plot was utilized for interpreting the models.
RESULTS: A total of 416 patients were included in this study, and they were divided into the training set (n = 396) and the external validation set (n = 47). The ML models, using both clinical and routine blood data, were able to predict postoperative GOS outcomes with area under the curve (AUC) values ranging from 0.860 to 0.900 during the internal cross-validation and from 0.801 to 0.890 during the external validation. In contrast, the LR model had the lowest AUC values during the internal and external validation (0.844 and 0.567, respectively). When blood data was not available, the ML models achieved AUCs of 0.849 to 0.870 during the internal cross-validation and 0.714 to 0.861 during the external validation. Similarly, the LR model had the lowest AUC values (0.821 and 0.638, respectively). Through repeated cross-validation analysis, we found that routine blood data had a significant association with higher mean AUC values in all ML and LR models. The SHAP plot was used to visualize the contributions of all predictors and highlighted the significance of blood data in the lightGBM model.
CONCLUSIONS: The study concluded that ML models could provide rapid and accurate predictions for postoperative GOS outcomes at discharge following moderate-to-severe TBI. The study also highlighted the crucial role of routine blood tests in improving such predictions, and may contribute to the optimization of surgical treatment decision-making for patients with TBI.
METHODS: We conducted a retrospective review of patients hospitalized in our department for moderate-to-severe TBI. Patients admitted between October 2011 and October 2022 were assigned to the training set, while patients admitted between November 2022 and May 2023 were designated as the external validation set. Five ML algorithms and LR model were employed to predict the postoperative Glasgow Outcome Scale (GOS) status at discharge using clinical and routine blood data collected upon admission. The Shapley (SHAP) plot was utilized for interpreting the models.
RESULTS: A total of 416 patients were included in this study, and they were divided into the training set (n = 396) and the external validation set (n = 47). The ML models, using both clinical and routine blood data, were able to predict postoperative GOS outcomes with area under the curve (AUC) values ranging from 0.860 to 0.900 during the internal cross-validation and from 0.801 to 0.890 during the external validation. In contrast, the LR model had the lowest AUC values during the internal and external validation (0.844 and 0.567, respectively). When blood data was not available, the ML models achieved AUCs of 0.849 to 0.870 during the internal cross-validation and 0.714 to 0.861 during the external validation. Similarly, the LR model had the lowest AUC values (0.821 and 0.638, respectively). Through repeated cross-validation analysis, we found that routine blood data had a significant association with higher mean AUC values in all ML and LR models. The SHAP plot was used to visualize the contributions of all predictors and highlighted the significance of blood data in the lightGBM model.
CONCLUSIONS: The study concluded that ML models could provide rapid and accurate predictions for postoperative GOS outcomes at discharge following moderate-to-severe TBI. The study also highlighted the crucial role of routine blood tests in improving such predictions, and may contribute to the optimization of surgical treatment decision-making for patients with TBI.
摘要：
目的：这项研究旨在利用机器学习（ML）和逻辑回归（LR）模型来预测基于入院检查的创伤性脑损伤（TBI）患者的手术结果。协助为这些患者做出最佳的手术治疗决定。
方法：我们对我科中重度TBI住院患者进行了回顾性分析。2011年10月至2022年10月期间收治的患者被分配到训练组，而2022年11月至2023年5月期间收治的患者被指定为外部验证集。采用五种ML算法和LR模型，使用入院时收集的临床和常规血液数据预测出院时的术后格拉斯哥预后量表（GOS）状态。Shapley(SHAP)图用于解释模型。
结果：本研究共纳入416例患者，并将它们分为训练集（n=396）和外部验证集（n=47）。ML模型，使用临床和常规血液数据，能够预测术后GOS结局,其曲线下面积(AUC)值在内部交叉验证期间为0.860~0.900,在外部验证期间为0.801~0.890.相比之下,LR模型在内部和外部验证期间的AUC值最低(分别为0.844和0.567).当没有血液数据时，ML模型在内部交叉验证期间的AUC为0.849~0.870,在外部验证期间的AUC为0.714~0.861.同样，LR模型的AUC值最低(分别为0.821和0.638).通过反复的交叉验证分析,我们发现,在所有ML和LR模型中,血常规数据与较高的平均AUC值显著相关.SHAP图用于可视化所有预测因子的贡献，并强调了血液数据在lightGBM模型中的重要性。
结论：该研究得出结论，ML模型可以为中度至重度TBI后出院时的术后GOS结局提供快速准确的预测。该研究还强调了常规血液检查在改善此类预测方面的关键作用，并可能有助于TBI患者手术治疗决策的优化。
方法：我们对我科中重度TBI住院患者进行了回顾性分析。2011年10月至2022年10月期间收治的患者被分配到训练组，而2022年11月至2023年5月期间收治的患者被指定为外部验证集。采用五种ML算法和LR模型，使用入院时收集的临床和常规血液数据预测出院时的术后格拉斯哥预后量表（GOS）状态。Shapley(SHAP)图用于解释模型。
结果：本研究共纳入416例患者，并将它们分为训练集（n=396）和外部验证集（n=47）。ML模型，使用临床和常规血液数据，能够预测术后GOS结局,其曲线下面积(AUC)值在内部交叉验证期间为0.860~0.900,在外部验证期间为0.801~0.890.相比之下,LR模型在内部和外部验证期间的AUC值最低(分别为0.844和0.567).当没有血液数据时，ML模型在内部交叉验证期间的AUC为0.849~0.870,在外部验证期间的AUC为0.714~0.861.同样，LR模型的AUC值最低(分别为0.821和0.638).通过反复的交叉验证分析,我们发现,在所有ML和LR模型中,血常规数据与较高的平均AUC值显著相关.SHAP图用于可视化所有预测因子的贡献，并强调了血液数据在lightGBM模型中的重要性。
结论：该研究得出结论，ML模型可以为中度至重度TBI后出院时的术后GOS结局提供快速准确的预测。该研究还强调了常规血液检查在改善此类预测方面的关键作用，并可能有助于TBI患者手术治疗决策的优化。
