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Title:

Results of a Clinical Scoring System Regarding Symptoms and Surgical Treatment of Isolated Unilateral Zygomatico-Orbital Fractures: A Single-Centre Retrospective Analysis of 461 Cases.

Document type:
Article; Journal Article
Author(s):
Ritschl, Lucas M; Wittmann, Matthias; von Bomhard, Achim; Koerdt, Steffen; Unterhuber, Tobias; Kehl, Victoria; Deppe, Herbert; Wolff, Klaus-Dietrich; Mücke, Thomas; Fichter, Andreas M
Abstract:
Systematic assessment of computed tomography (CT) scans and clinical symptoms is necessary to quickly indicate the correct treatment of zygomatico-orbital (ZMO) fractures. For this purpose, a clinical scoring system (=Clinical Score) was developed and correlated with CT scans to analyse its validity. Every operated, isolated, and unilateral ZMO fracture between January 2012 and December 2016 was screened retrospectively, including patient and treatment data. All available CT scans were analysed, and the grade of dislocation was measured for each case and plane. Four hundred and sixty-one cases were included and showed a median surgery time of 66.0 min (5.0−361.0) and a median postoperative hospital stay of three days (0−25). The distribution of gender, aetiologies and age groups was significantly different (each p = 0.001), and the aetiology had a significant influence on the Clinical Score (p = 0.038). The degree of dislocation in the coronary and sagittal planes correlated significantly with the Clinical Score with regard to the orbital involvement (p < 0.001, ρ = 0.566; p < 0.001, ρ = 0.609). The simple, quick, and easy-to-apply Clinical Score showed a significant correlation with the most important planes in CT scans as well as with the clinical course. It may facilitate fast risk stratification of the patient. However, the validity of the proposed score in determining indications must now be evaluated in a prospective setting, including both operated and non-operated fractures.
Journal title abbreviation:
J Clin Med
Year:
2022
Journal volume:
11
Journal issue:
8
Fulltext / DOI:
doi:10.3390/jcm11082187
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/35456282
TUM Institution:
Institut für KI und Informatik in der Medizin (Prof. Rückert); Lehrstuhl für Medizinische Informatik (Prof. Boeker)
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