Wang Bing, Liu Yanping, Xing Jingjing, Zhang Hailong, Ye Sheng
Emergency Department, The Second Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, China.
Department of Emergency and Critical Care Medicine, Wannan Medical College, Wuhu, Anhui, China.
Heliyon. 2024 Aug 31;10(17):e37295. doi: 10.1016/j.heliyon.2024.e37295. eCollection 2024 Sep 15.
Traumatic brain injury (TBI) is among the leading causes of death and disability globally. Identifying and assessing the risk of in-hospital mortality in traumatic brain injury patients at an early stage is challenging. This study aimed to develop a model for predicting in-hospital mortality in TBI patients using prehospital data from China.
We retrospectively included traumatic brain injury patients who sustained injuries due to external forces and were treated by pre-hospital emergency medical services (EMS) at a tertiary hospital. Data from the pre-hospital emergency database were analyzed, including demographics, trauma mechanisms, comorbidities, vital signs, clinical symptoms, and trauma scores. Eligible patients were randomly divided into a training set (241 cases) and a validation set (104 cases) at a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were employed to identify independent risk factors. Analyzed the discrimination, calibration, and net benefit of the nomogram across both groups.
17.40 % (42/241) of TBI patients died in the hospital in the training set, while 18.30 % (19/104) in the validation set. After analysis, chest trauma (odds ratio [OR] = 4.556, 95 % confidence interval [CI] = 1.861-11.152, = 0.001), vomiting (OR = 2.944, 95%CI = 1.194-7.258, = 0.019), systolic blood pressure (OR = 0.939, 95%CI = 0.913-0.966, < 0.001), SpO (OR = 0.778, 95%CI = 0.688-0.881, < 0.001), and heart rate (OR = 1.046, 95%CI = 1.015-1.078, = 0.003) were identified as independent risk factors for in-hospital mortality in TBI patients. The nomogram based on the five factors demonstrated well-predictive power, with an area under the curve (AUC) of 0.881 in the training set and 0.866 in the validation set. The calibration curve and decision curve analysis showed that the predictive model exhibited good consistency and covered a wide range of threshold probabilities in both sets.
The nomogram based on prehospital data demonstrated well-predictive performance for in-hospital mortality in TBI patients, helping prehospital emergency physicians identify and assess severe TBI patients earlier, thereby improving the efficiency of prehospital emergency care.
创伤性脑损伤（TBI）是全球死亡和残疾的主要原因之一。早期识别和评估创伤性脑损伤患者的院内死亡风险具有挑战性。本研究旨在利用中国的院前数据建立一个预测TBI患者院内死亡的模型。
我们回顾性纳入了因外力受伤并在三级医院接受院前急救医疗服务（EMS）治疗的创伤性脑损伤患者。分析院前急救数据库中的数据，包括人口统计学、创伤机制、合并症、生命体征、临床症状和创伤评分。符合条件的患者按7:3的比例随机分为训练集（241例）和验证集（104例）。采用最小绝对收缩和选择算子（LASSO）和多因素逻辑回归来识别独立危险因素。分析两组列线图的区分度、校准度和净效益。
训练集中17.40%（42/241）的TBI患者在医院死亡，验证集中为18.30%（19/104）。经过分析，胸部创伤（比值比[OR]=4.556，95%置信区间[CI]=1.861-11.152，P=0.001）、呕吐（OR=2.944，95%CI=1.194-7.258，P=0.019）、收缩压（OR=0.939，95%CI=0.913-0.966，P<0.001）、血氧饱和度（OR=0.778，95%CI=0.688-0.881，P<0.001）和心率（OR=1.046，95%CI=1.015-1.078，P=0.003）被确定为TBI患者院内死亡的独立危险因素。基于这五个因素的列线图显示出良好的预测能力，训练集中曲线下面积（AUC）为0.881，验证集中为0.866。校准曲线和决策曲线分析表明，预测模型在两组中均表现出良好的一致性，并且涵盖了广泛的阈值概率。
基于院前数据的列线图对TBI患者的院内死亡具有良好的预测性能，有助于院前急救医生更早地识别和评估重度TBI患者，从而提高院前急救效率。
