Osong Biche, Sribnick Eric, Groner Jonathan, Stanley Rachel, Schulz Lauren, Lu Bo, Cook Lawrence, Xiang Henry
Center for Pediatric Trauma Research and Center for Injury Research and Policy, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, Ohio, United States of America.
Division of Pediatric Neurosurgery, Nationwide Children's Hospital, Columbus, Ohio, United States of America.
PLoS One. 2025 Feb 3;20(2):e0316462. doi: 10.1371/journal.pone.0316462. eCollection 2025.
Older persons comprise most traumatic brain injury (TBI)-related hospitalizations and deaths and are particularly susceptible to fall-induced TBIs. The combination of increased frailty and susceptibility to clinical decline creates a significant ongoing challenge in the management of geriatric TBI. As the population ages and co-existing medical conditions complexify, so does the need to improve the quality of care for this population. Utilizing early hospital admission variables, this study will create and validate a multinomial decision tree that predicts the discharge disposition of older patients with fall-related TBI.
From the National Trauma Data Bank, we retrospectively analyzed 11,977 older patients with a fall-related TBI (2017-2021). Clinical variables included Glasgow Coma Scale (GCS) score, intracranial pressure monitor use, venous thromboembolism (VTE) prophylaxis, and initial vital signs. Outcomes included hospital discharge disposition re-categorized into home, care facility, or deceased. Data were split into two sets, where 80% developed a decision tree, and 20% tested predictive performance. We employed a conditional inference tree algorithm with bootstrap (B = 100) and grid search options to grow the decision tree and measure discrimination ability using the area under the curve (AUC) and calibration plots.
Our decision tree used seven admission variables to predict the discharge disposition of older TBI patients. Significant non-modifiable variables included total GCS and injury severity scores, while VTE prophylaxis type was the most important interventional variable. Patients who did not receive VTE prophylaxis treatment had a higher probability of death. The predictive performance of the tree in terms of AUC value (95% confidence intervals) in the training cohort for death, care, and home were 0.66 (0.65-0.67), 0.75 (0.73-0.76), and 0.77 (0.76-0.79), respectively. In the test cohort, the values were 0.64 (0.62-0.67), 0.75 (0.72-0.77), and 0.77 (0.73-0.79).
We have developed and internally validated a multinomial decision tree to predict the discharge destination of older patients with TBI. This tree could serve as a decision support tool for caregivers to manage older patients better and inform decision-making. However, the tree must be externally validated using prospective data to ascertain its predictive and clinical importance.
老年人占创伤性脑损伤（TBI）相关住院和死亡病例的大多数，并且特别容易因跌倒导致创伤性脑损伤。身体虚弱加剧与临床衰退易感性增加的共同作用，给老年创伤性脑损伤的管理带来了持续的重大挑战。随着人口老龄化以及并存的医疗状况变得复杂，改善这一人群护理质量的需求也随之增加。本研究将利用早期入院变量创建并验证一个多项决策树，以预测老年跌倒相关创伤性脑损伤患者的出院处置情况。
我们从国家创伤数据库中回顾性分析了11977例老年跌倒相关创伤性脑损伤患者（2017 - 2021年）。临床变量包括格拉斯哥昏迷量表（GCS）评分、颅内压监测器的使用、静脉血栓栓塞（VTE）预防措施以及初始生命体征。结局包括重新分类为回家、护理机构或死亡的出院处置情况。数据被分为两组，其中80%用于构建决策树，20%用于测试预测性能。我们采用带有自助法（B = 100）和网格搜索选项的条件推断树算法来生长决策树，并使用曲线下面积（AUC）和校准图来测量区分能力。
我们的决策树使用七个入院变量来预测老年创伤性脑损伤患者的出院处置情况。重要的不可改变变量包括GCS总分和损伤严重程度评分，而VTE预防类型是最重要的干预变量。未接受VTE预防治疗的患者死亡概率更高。该决策树在训练队列中对于死亡、护理机构和回家的AUC值（95%置信区间）预测性能分别为0.66（0.65 - 0.67）、0.75（0.73 - 0.76）和0.77（0.76 - 0.79）。在测试队列中，相应的值分别为0.64（0.62 - 0.67）、0.75（0.72 - 0.77）和0.77（0.73 - 0.79）。
我们已经开发并在内部验证了一个多项决策树，以预测老年创伤性脑损伤患者的出院去向。这棵决策树可以作为一种决策支持工具，帮助护理人员更好地管理老年患者并为决策提供依据。然而，必须使用前瞻性数据对该决策树进行外部验证，以确定其预测和临床重要性。
