With the increasing use of sustainable energy sources, the electric scooter has become a widely used vehicle. The aim of the study is to analyse the types of facial fracture related to road traffic fi c accidents to outline the need for dedicated road rules. An observational, retrospective, multicentre study was carried out at the Maxillofacial Surgery Units of six Italian hospitals. Fifty patients (mean age was 34.76 years) from January 2020 to January 2024 were enrolled. The severity of trauma was evaluated by the Facial Injury Severity Scale (FISS) by Bagheri et al. Most of the accidents occurred during the day and the weekend in spring or summer; 24 drivers collided with infrastructures or pedestrians, while 26 involved other vehicles. A total of 33 vehicles were rented, and 17 were privately owned. A total of 43 subjects were not wearing helmets, fi ve patients were drunk, and three patients took drugs. In order of frequency, the facial fractures involved: zygomaticomaxillary-orbital complex (ZMOC) (n = 16), mandibular condyle (n = 13), nasal bone (n = 11), orbit fl oor (n = 8), and mandibular body (n = 7). Fractures such as Le Fort I (n = 4), naso-orbito-ethmoidal NOE (n = 4) and mandibular ramus (n = 4) were less common. Other types of facial fracture were rare. Thirty patients reported multiple facial fractures. The vast majority of the cases showed a low severity grade FISS score. Fifteen patients suffered polytrauma. The mean hospitalisation time was 8.3 days. As accidents with electric scooters are increasing, it is important to characterise the most frequent facial fractures to improve patient management and encourage the introduction of new road rules. (c) 2024 The British Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

New generation vehicles: the impact of electric scooter trauma on the severity of facial fractures assessed by FISS score. A multicentre study

Boschetti C. E.
Writing – Original Draft Preparation
;
Magliulo R.
;
Canet Lopez E.;Chirico F.
Membro del Collaboration Group
;
Santagata M.
Membro del Collaboration Group
;
Tartaro G.
2024

Abstract

With the increasing use of sustainable energy sources, the electric scooter has become a widely used vehicle. The aim of the study is to analyse the types of facial fracture related to road traffic fi c accidents to outline the need for dedicated road rules. An observational, retrospective, multicentre study was carried out at the Maxillofacial Surgery Units of six Italian hospitals. Fifty patients (mean age was 34.76 years) from January 2020 to January 2024 were enrolled. The severity of trauma was evaluated by the Facial Injury Severity Scale (FISS) by Bagheri et al. Most of the accidents occurred during the day and the weekend in spring or summer; 24 drivers collided with infrastructures or pedestrians, while 26 involved other vehicles. A total of 33 vehicles were rented, and 17 were privately owned. A total of 43 subjects were not wearing helmets, fi ve patients were drunk, and three patients took drugs. In order of frequency, the facial fractures involved: zygomaticomaxillary-orbital complex (ZMOC) (n = 16), mandibular condyle (n = 13), nasal bone (n = 11), orbit fl oor (n = 8), and mandibular body (n = 7). Fractures such as Le Fort I (n = 4), naso-orbito-ethmoidal NOE (n = 4) and mandibular ramus (n = 4) were less common. Other types of facial fracture were rare. Thirty patients reported multiple facial fractures. The vast majority of the cases showed a low severity grade FISS score. Fifteen patients suffered polytrauma. The mean hospitalisation time was 8.3 days. As accidents with electric scooters are increasing, it is important to characterise the most frequent facial fractures to improve patient management and encourage the introduction of new road rules. (c) 2024 The British Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11591/543188
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