A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Characteristics of Associated Injuries in Children and Teenagers With Craniofacial Fractures
Tekijät: Kirvelä Aura, Snäll Johanna, Suominen Auli, Puolakkainen Tero, Thorén Hanna
Julkaisuvuosi: 2023
Journal: Journal of Craniofacial Surgery
Tietokannassa oleva lehden nimi: The Journal of craniofacial surgery
Lehden akronyymi: J Craniofac Surg
Vuosikerta: 34
Numero: 6
Aloitussivu: 1625
Lopetussivu: 1628
ISSN: 1049-2275
eISSN: 1536-3732
DOI: https://doi.org/10.1097/SCS.0000000000009343
Verkko-osoite: https://oce.ovid.com/article/00001665-202309000-00007/HTML
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/181320281
The incidence of pediatric craniofacial fractures and heterogeneity of fractures is known to increase with age. This study aimed to determine the occurrence of associated injuries (AIs) to craniofacial fractures and identify differences in patterns of and predictors for AIs in children and teenagers. A 6-year retrospective cross-sectional cohort study was designed and implemented. The study population included 397 patients aged 19 years or less diagnosed with craniofacial fracture at Helsinki University Hospital from 2013 to 2018. Boys (71.0%) and teenagers (64.7%) were predominated. Associated injuries were more common in teenagers than children. Teenagers had more often AI in 2 or more organ systems. Assault and intoxication by alcohol were observed only in teenagers and predominantly boys. A total of 27.0% of all patients sustained AIs. In 18.1%, brain injury was reported. In children, motor vehicle accident (MVA) was an independent predictor for AI. In teenagers, independent predictors for AI were female sex, isolated cranial fracture, combined cranial fracture, and high-energy trauma mechanism. Injury patterns and AI related to craniofacial fractures in the pediatric population are age-specific, requiring multidisciplinary collaboration in the diagnosis, treatment, and follow-up of such trauma. Predictors for AIs increase in complexity with age, and the role of sex as a predictor is evident in teenagers.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
