Division of Neurosurgery, Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand.
Division of Neurosurgery, Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand.
Am J Emerg Med. 2024 Mar;77:194-202. doi: 10.1016/j.ajem.2023.12.037. Epub 2023 Dec 29.
Traumatic brain injury (TBI) is a major cause of death and functional disability in the general population. The nomogram is a clinical prediction tool that has been researched for a wide range of medical conditions. The purpose of this study was to identify prognostic factors associated with in-hospital mortality. The secondary objective was to develop a clinical nomogram for TBI patients' in-hospital mortality based on prognostic factors.
A retrospective cohort study was conducted to analyze 14,075 TBI patients who were admitted to a tertiary hospital in southern Thailand. The total dataset was divided into the training and validation datasets. Several clinical characteristics and imaging findings were analyzed for in-hospital mortality in both univariate and multivariable analyses using the training dataset. Based on binary logistic regression, the nomogram was developed and internally validated using the final predictive model. Therefore, the predictive performances of the nomogram were estimated by the validation dataset.
Prognostic factors associated with in-hospital mortality comprised age, hypotension, antiplatelet, Glasgow coma scale score, pupillary light reflex, basilar skull fracture, acute subdural hematoma, subarachnoid hemorrhage, midline shift, and basal cistern obliteration that were used for building nomogram. The predictive performance of the nomogram was estimated by the training dataset; the area under the receiver operating characteristic curve (AUC) was 0.981. In addition, the AUCs of bootstrapping and cross-validation methods were 0.980 and 0.981, respectively. For the temporal validation with an unseen dataset, the sensitivity, specificity, accuracy, and AUC of the nomogram were 0.90, 0.88, 0.88, and 0.89, respectively.
A nomogram developed from prognostic factors had excellent performance; thus, the tool had the potential to serve as a screening tool for prognostication in TBI patients. Furthermore, future research should involve geographic validation to examine the predictive performances of the clinical prediction tool.
创伤性脑损伤（TBI）是普通人群中死亡和功能残疾的主要原因。列线图是一种已针对多种医疗状况进行研究的临床预测工具。本研究的目的是确定与住院死亡率相关的预后因素。次要目的是根据预后因素为 TBI 患者的住院死亡率制定临床列线图。
对泰国南部一家三级医院收治的 14075 例 TBI 患者进行回顾性队列研究。将整个数据集分为训练数据集和验证数据集。使用训练数据集对所有患者进行单变量和多变量分析，以分析与住院死亡率相关的多种临床特征和影像学发现。基于二项逻辑回归，使用最终预测模型开发并在内部验证列线图。然后，使用验证数据集评估列线图的预测性能。
与住院死亡率相关的预后因素包括年龄、低血压、抗血小板、格拉斯哥昏迷量表评分、瞳孔光反射、颅底骨折、急性硬膜下血肿、蛛网膜下腔出血、中线移位和基底池闭塞，这些因素用于构建列线图。使用训练数据集评估列线图的预测性能；受试者工作特征曲线下面积（AUC）为 0.981。此外，bootstrap 和交叉验证方法的 AUC 分别为 0.980 和 0.981。对于使用未见数据集进行的时间验证，列线图的灵敏度、特异度、准确度和 AUC 分别为 0.90、0.88、0.88 和 0.89。
基于预后因素开发的列线图具有优异的性能；因此，该工具有可能作为 TBI 患者预后的筛查工具。此外，未来的研究应包括地理验证，以检验临床预测工具的预测性能。
