Nyam Tee-Tau Eric, Tu Kuan-Chi, Chen Nai-Ching, Wang Che-Chuan, Liu Chung-Feng, Kuo Ching-Lung, Liao Jen-Chieh
Department of Neurosurgery, Chi Mei Medical Center, Tainan 711, Taiwan.
Center of General Education, Chia Nan University of Phamacy and Science, Tainan 717, Taiwan.
Diagnostics (Basel). 2024 Dec 25;15(1):20. doi: 10.3390/diagnostics15010020.
Traumatic brain injury (TBI) research often focuses on mortality rates or functional recovery, yet the critical need for long-term care among patients dependent on institutional or Respiratory Care Ward (RCW) support remains underexplored. This study aims to address this gap by employing machine learning techniques to develop and validate predictive models that analyze the prognosis of this patient population.
Retrospective data from electronic medical records at Chi Mei Medical Center, encompassing 2020 TBI patients admitted to the ICU between January 2016 and December 2021, were collected. A total of 44 features were included, utilizing four machine learning models and various feature combinations based on clinical significance and Spearman correlation coefficients. Predictive performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and validated with the DeLong test and SHAP (SHapley Additive exPlanations) analysis.
Notably, 236 patients (11.68%) were transferred to long-term care centers. XGBoost with 27 features achieved the highest AUC (0.823), followed by Random Forest with 11 features (0.817), and LightGBM with 44 features (0.813). The DeLong test revealed no significant differences among the best predictive models under various feature combinations. SHAP analysis illustrated a similar distribution of feature importance for the top 11 features in XGBoost, with 27 features, and Random Forest with 11 features.
Random Forest, with an 11-feature combination, provided clinically meaningful predictive capability, offering early insights into long-term care trends for TBI patients. This model supports proactive planning for institutional or RCW resources, addressing a critical yet often overlooked aspect of TBI care.
创伤性脑损伤（TBI）研究通常侧重于死亡率或功能恢复，但对于依赖机构或呼吸护理病房（RCW）支持的患者的长期护理的迫切需求仍未得到充分探索。本研究旨在通过采用机器学习技术来开发和验证预测模型，以分析该患者群体的预后，从而填补这一空白。
收集了奇美医学中心电子病历中的回顾性数据，这些数据涵盖了2016年1月至2021年12月期间入住重症监护病房的2020名TBI患者。共纳入44个特征，基于临床意义和斯皮尔曼相关系数，使用四种机器学习模型和各种特征组合。使用受试者操作特征（ROC）曲线的曲线下面积（AUC）评估预测性能，并通过德龙检验和SHAP（SHapley加法解释）分析进行验证。
值得注意的是，236名患者（11.68%）被转移到长期护理中心。具有27个特征的XGBoost模型的AUC最高（0.823），其次是具有11个特征的随机森林模型（0.817），以及具有44个特征的LightGBM模型（0.813）。德龙检验显示，在各种特征组合下，最佳预测模型之间没有显著差异。SHAP分析表明，XGBoost模型中前11个特征、具有27个特征的模型以及具有11个特征的随机森林模型的特征重要性分布相似。
具有11个特征组合的随机森林模型提供了具有临床意义的预测能力，能够为TBI患者的长期护理趋势提供早期见解。该模型支持对机构或RCW资源进行前瞻性规划，解决了TBI护理中一个关键但经常被忽视的方面。
