Wang Il-Jae, Cho Young Mo, Cho Suck Ju, Yeom Seok-Ran, Park Sung Wook, Kim So Eun, Yoon Jae Chol, Kim Yeaeun, Park Jongho
Department of Emergency Medicine, Pusan National University School of Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea.
Department of Emergency Medicine, Jeonbuk National University Hospital, Jeonju, Republic of Korea.
Emerg Med Int. 2022 May 27;2022:7994866. doi: 10.1155/2022/7994866. eCollection 2022.
This study aimed to establish a predictive model that includes physiological parameters and identify independent risk factors for severe injuries in bicycle rider accidents.
This was a multicenter observational study. For four years, we included patients with bicycle rider injuries in the Emergency Department-Based Injury In-depth Surveillance database. In this study, we regarded ICD admission or in-hospital mortality as parameters of severe trauma. Univariate and multivariate logistic regression analyses were performed to assess risk factors for severe trauma. A receiver operating characteristic (ROC) curve was generated to evaluate the performance of the regression model.
This study included 19,842 patients, of whom 1,202 (6.05%) had severe trauma. In multivariate regression analysis, male sex, older age, alcohol use, motor vehicle opponent, load state (general and crosswalk), blood pressure, heart rate, respiratory rate, and Glasgow Coma Scale were the independent factors for predicting severe trauma. In the ROC analysis, the area under the ROC curve for predicting severe trauma was 0.848 (95% confidence interval: 0.830-0.867).
We identified independent risk factors for severe trauma in bicycle rider accidents and believe that physiologic parameters contribute to enhancing prediction ability.
本研究旨在建立一个包含生理参数的预测模型，并确定自行车骑行者事故中严重损伤的独立危险因素。
这是一项多中心观察性研究。在四年时间里，我们将急诊科基于损伤深度监测数据库中自行车骑行者受伤的患者纳入研究。在本研究中，我们将国际疾病分类（ICD）入院或院内死亡率作为严重创伤的参数。进行单因素和多因素逻辑回归分析以评估严重创伤的危险因素。生成受试者工作特征（ROC）曲线以评估回归模型的性能。
本研究纳入了19842例患者，其中1202例（6.05%）有严重创伤。在多因素回归分析中，男性、年龄较大、饮酒、机动车碰撞对象、负载状态（一般和人行横道）、血压、心率、呼吸频率和格拉斯哥昏迷量表是预测严重创伤的独立因素。在ROC分析中，预测严重创伤的ROC曲线下面积为0.848（95%置信区间：0.830 - 0.867）。
我们确定了自行车骑行者事故中严重创伤的独立危险因素，并认为生理参数有助于提高预测能力。
