Prediction of Severe Injury in Bicycle Rider Accidents: A Multicenter Observational Study
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 inj...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Emergency Medicine International |
| Online Access: | http://dx.doi.org/10.1155/2022/7994866 |
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| author | Il-Jae Wang Young Mo Cho Suck Ju Cho Seok-Ran Yeom Sung Wook Park So Eun Kim Jae Chol Yoon Yeaeun Kim Jongho Park |
| author_facet | Il-Jae Wang Young Mo Cho Suck Ju Cho Seok-Ran Yeom Sung Wook Park So Eun Kim Jae Chol Yoon Yeaeun Kim Jongho Park |
| author_sort | Il-Jae Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-aa0664d7c1964d3782d60f09c7cc689f |
| institution | Kabale University |
| issn | 2090-2859 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Emergency Medicine International |
| spelling | doaj-art-aa0664d7c1964d3782d60f09c7cc689f2025-02-03T01:19:59ZengWileyEmergency Medicine International2090-28592022-01-01202210.1155/2022/7994866Prediction of Severe Injury in Bicycle Rider Accidents: A Multicenter Observational StudyIl-Jae Wang0Young Mo Cho1Suck Ju Cho2Seok-Ran Yeom3Sung Wook Park4So Eun Kim5Jae Chol Yoon6Yeaeun Kim7Jongho Park8Department of Emergency MedicineDepartment of Emergency MedicineDepartment of Emergency MedicineDepartment of Emergency MedicineDepartment of Emergency MedicineDepartment of Emergency MedicineDepartment of Emergency MedicineDepartment of Health Care ManagementDivision of Health AdministrationIntroduction. 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.http://dx.doi.org/10.1155/2022/7994866 |
| spellingShingle | Il-Jae Wang Young Mo Cho Suck Ju Cho Seok-Ran Yeom Sung Wook Park So Eun Kim Jae Chol Yoon Yeaeun Kim Jongho Park Prediction of Severe Injury in Bicycle Rider Accidents: A Multicenter Observational Study Emergency Medicine International |
| title | Prediction of Severe Injury in Bicycle Rider Accidents: A Multicenter Observational Study |
| title_full | Prediction of Severe Injury in Bicycle Rider Accidents: A Multicenter Observational Study |
| title_fullStr | Prediction of Severe Injury in Bicycle Rider Accidents: A Multicenter Observational Study |
| title_full_unstemmed | Prediction of Severe Injury in Bicycle Rider Accidents: A Multicenter Observational Study |
| title_short | Prediction of Severe Injury in Bicycle Rider Accidents: A Multicenter Observational Study |
| title_sort | prediction of severe injury in bicycle rider accidents a multicenter observational study |
| url | http://dx.doi.org/10.1155/2022/7994866 |
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