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Introduction. This study aimed to establish a predictive model that includes physiological parameters and identify independent risk factors for severe injuries in bicycle rider accidents. Methods. 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. Results. 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). Conclusion. We identified independent risk factors for severe trauma in bicycle rider accidents and believe that physiologic parameters contribute to enhancing prediction ability.

Purpose:The purpose of this study was to investigate (1) the association among helmet wearing, incidence rate of traumatic brain injury (TBI), and in-hospital mortality; TBI was diagnosed when the head Abbreviated Injury Scale (AIS) was ≥1, and as severe TBI when head AIS was ≥3; and (2) the association between helmet type and incidence rate of TBI, severe TBI, and in-hospital mortality of motorcycle accidents based on the newly revised Emergency Department-based Injury In-depth Surveillance (EDIIS) data. Methods: Data collected from EDIIS between January 1, 2020 and December 31, 2020 were analyzed. The final study population comprised 1,910 patients, who were divided into two groups: helmet wearing group and unhelmeted group. In addition, the correlation between helmet type and motorcycle accident was determined in 596 patients who knew the exact type of helmet they wore. A total of 710 patients who wore helmet but did not know the type were excluded from this analysis. Multivariate logistic regression was performed in both the groups to investigate the factors affecting the primary (occurrence of TBIs) and secondary outcomes (severe TBI and in-hospital mortality). Results: The prevalence of Injury Severity Scores, TBIs, and severe TBIs as well as in-hospital mortality were the highest in the unhelmeted group. Additionally, the results from the group that wore and knew the type of helmet worn indicated that wearing a full-face helmet decreased the incidence of TBIs in comparison to a half-face helmet. Conclusions:The wearing of a helmet in motorcycle accidents is very important as it plays a role in reducing the occurrence of TBIs and severe TBIs and in-hospital mortality. The use of a full-face helmet lowered the incidence of TBIs.

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