Chen Yuhui, Chen Li, Xian Liang, Liu Haibing, Wang Jiaxing, Xia Shaohuai, Wei Liangfeng, Xia Xuewei, Wang Shousen
Department of Neurosurgery, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China.
Fujian Provincial Clinical Medical Research Center for Minimally Invasive Diagnosis and Treatment of Neurovascular Diseases, Fuzhou, Fujian, China.
Neurol Ther. 2025 Feb;14(1):157-175. doi: 10.1007/s40120-024-00678-7. Epub 2024 Nov 4.
Open traumatic brain injury (OTBI) is associated with high mortality and morbidity; however, the classification of these injuries and the determination of patient prognosis remain uncertain, hindering the selection of optimal treatment strategies. This study aimed to develop and validate a novel OTBI classification system and a prognostic model for poor prognosis.
This retrospective study included patients with isolated OTBI who received treatment at three large medical centers in China between January 2020 and June 2022 as the training set. Data on patients with OTBI collected at the Fuzong Clinical Medical College of Fujian Medical University between July 2022 and June 2023 were used as the validation set. Clinical parameters, including clinical data at admission, radiological and laboratory findings, details of surgical methods, and prognosis were collected. Prognosis was assessed through a dichotomized Glasgow Outcome Scale (GOS). A novel OTBI classification was proposed, categorizing patients based on a combination of intracranial hematoma and midline shift observed on imaging, and logistic regression analyses were performed to identify risk factors associated with poor prognosis and to investigate the association between the novel OTBI classification and prognosis. Finally, a nomogram suitable for clinical application was established and validated.
Multivariable logistic regression analysis identified OTBI classification type C (p < 0.001), a Glasgow Coma Scale score (GCS) ≤ 8 (p < 0.001), subarachnoid hemorrhage (SAH) (p = 0.004), subdural hematoma (SDH) (p = 0.011), and coagulopathy (p = 0.020) as independent risk factors for poor prognosis. The addition of the OTBI classification to a model containing all the other identified prognostic factors improved the predictive ability of the model (Z = 1.983; p = 0.047). In the validation set, the model achieved an area under the curve (AUC) of 0.917 [95% confidence interval (CI) = 0.864-0.970]. The calibration curve closely approximated the ideal curve, indicating strong predictive performance of the model.
The implementation of our proposed OTBI classification system and its use alongside the other prognostic factors identified here may improve the prediction of patient prognosis and aid in the selection of the most suitable treatment strategies.
开放性颅脑损伤（OTBI）与高死亡率和高发病率相关；然而，这些损伤的分类以及患者预后的判定仍不明确，这阻碍了最佳治疗策略的选择。本研究旨在开发并验证一种新型的OTBI分类系统以及一个针对不良预后的预测模型。
这项回顾性研究纳入了2020年1月至2022年6月在中国三家大型医疗中心接受治疗的孤立性OTBI患者作为训练集。将2022年7月至2023年6月在福建医科大学附属第一临床医学院收集的OTBI患者数据用作验证集。收集临床参数，包括入院时的临床数据、影像学和实验室检查结果、手术方法细节以及预后情况。通过二分格拉斯哥预后量表（GOS）评估预后。提出了一种新型的OTBI分类方法，根据影像学上观察到的颅内血肿和中线移位的组合对患者进行分类，并进行逻辑回归分析以确定与不良预后相关的危险因素，并研究新型OTBI分类与预后之间的关联。最后，建立并验证了一个适用于临床应用的列线图。
多变量逻辑回归分析确定OTBI分类C型（p < 0.001）、格拉斯哥昏迷量表评分（GCS）≤8（p < 0.001）、蛛网膜下腔出血（SAH）（p = 0.004）、硬膜下血肿（SDH）（p = 0.011）和凝血病（p = 0.020）为不良预后的独立危险因素。将OTBI分类添加到包含所有其他已确定的预后因素的模型中可提高模型的预测能力（Z = 1.983；p = 0.047）。在验证集中，该模型的曲线下面积（AUC）为0.917 [95%置信区间（CI）= 0.864 - 0.970]。校准曲线与理想曲线密切近似，表明该模型具有强大的预测性能。
实施我们提出的OTBI分类系统并将其与本文确定的其他预后因素一起使用，可能会改善对患者预后的预测，并有助于选择最合适的治疗策略。
