HSC Section 3 - Trauma, Critical Care and Sleep Medicine

G. Frohwitter et al. / Journal of Cranio-Maxillo-Facial Surgery 46 (2018) 1550 e 1554

Table 2 Correlation of CT-morphological parameters and preoperative ophthalmological symptoms.

Incarceration of IMR Displacement of IRM Affection of ION Intraorbital emphysema

Intraorbital bone fragments

Diplopia

0.043* 0.015* 0.729 0.537 0.792 0.155 0.085 0.665

0.021* 0.000* 0.903 0.456 0.079 0.773 0.232 0.135 0.960

0.455 0.632 0.413 0.533 0.366 0.973 0.595 0.411 0.625

0.376 0.144 0.650 0.615 0.029* 0.987 0.615 0.670 0.363

0.184 0.549 0.787 0.943 0.690 0.730 0.943 0.837 0.586

Decreased mobility Impaired visual acuity Retro-orbital hematoma Monocular hematoma

Subconjunctival haemorrhage 0.423

Contusion of the globe

Retinal oedema

Traumatic mydriasis

Abbreviations: CT ¼ computed tomography; IRM ¼ inferior rectus muscle; ION ¼ infraorbital nerve, *p < 0.05 (Fisher's exact test).

differences between the groups were shown for the following ophthalmological parameters (all Fisher's exact test): diplopia ( p ¼ 0.038), decreased mobility ( p ¼ 0.038), monocular hematoma ( p ¼ 0.019), and enophthalmos ( p ¼ 0.003). 4. Discussion Treatment decision-making for IOFF depends on various factors. To date, no reliable categorisation that takes ophthalmological ex- amination and radiological fi ndings into account exists in clinical practice. As CT scans are considered to be the gold standard in di- agnostics for facial trauma and, in particular, for orbital trauma, our aim in this study has been to evaluate speci fi c CT-morphological parameters in order to categorise IOFF and to correlate the fi nd- ings with ophthalmological parameters, such as diplopia or no perception of light. Consequently, this categorisation might help to simplify treatment options for patients who present with IOFF. When we analysed the causes of facial trauma and of IOFF in particular, assaults, domestic and sport accidents, and road traf fi c accidents were de fi ned as the most common in this study, in accordance with previous reports ( Goggin et al., 2015 ).

Themeasurement of defect size of orbital fl oor fractures has been subject tomany studies in the literature. Ploder et al. have described a computer-based method for the calculation of orbital fl oor frac- tures from coronal CT scans. The published formula sums areas of trapezoidal patches obtained by using the defect width and slice thickness for the entire length of the fracture, thereby giving the total defect size ( Ploder et al. 2001, 2002 ). Schouman et al. have also proposed a method for determining orbital fl oor defects in CT scans by using speci fi c software ( Schouman et al., 2012 ). However, a direct comparison between the use of speci fi c software and simple cal- culations of defect size on the basis of measurements fromonly a few slices reveal inferior accuracy. Both methods seem to lack speci fi city while nevertheless providing adequate sensitivity ( Goggin et al., 2015 ). For the rapid evaluation of treatment options in IOFF, the use of speci fi c software is time consuming and expensive. The measurements in this study, however, do not represent actual defect sizes, but give the maximum diameter of coronal and sagittal sec- tions. Therefore, the mentioned cut-off value of 158.9 mm 2 d de fi ning the defect size that causes clinical symptoms in IOFF d should be evaluated as a combination of the different parameters used for CT-morphological assessments of IOFF. The merger of various parameters de fi ning treatment decision making for IOFF accounts for the improved clinical and radiological assessment. Other study groups, such as Alinasab et al., have investigated whether the relative change in orbital volume is an indicator for surgical reconstruction in IOFF ( Alinasab et al., 2011 ). However, the difference in volume between the fractured and the non-fractured orbits does not seem to be a reliable and useful criterion for surgery. On the other hand, the role of the IRM and the direct correlation with an increasing risk of persistent motility restriction have been shown in other investigations ( Furuta et al., 2006; Gilbard et al., 1985 ). Shah et al. have evaluated the interaction between fracture size, soft tissue herniation, and postoperative diplopia. They have been able to show that small and medium-sized fractures are asso- ciated with the entrapment of periorbital soft tissues, including the IRM, whereas large ‘ trap-door ’ fractures show fewer impairments in extraocular motility and incidents of diplopia ( Shah et al., 2013 ). However, critical size defects of the orbital fl oor of 2 cm 2 are likely to cause clinically signi fi cant posterior displacement of the globe, resulting in enophthalmos ( Jin et al., 2000; Manson et al., 1986; Whitehouse et al., 1994 ). Fracture size and displacement and change in diameter of the IRM have been identi fi ed as being closely linked and correlated with the presence of preoperative ophthalmological symptoms. The results of our study suggest operative intervention in pa- tients who present with diplopia combined with an incarceration of IRM, whereas defects of the orbital fl oor larger than 2 cm 2 seem to bene fi t from operative reconstruction as well. However, functional outcome after non-surgical management of orbital fractures has been evaluated in a retrospective case e control study by Kunz et al., (2013) .

Fig. 3. Receiver operating characteristic (ROC) curve analysis for fracture size with regard to the development of preoperative ophthalmological symptoms. ROC curves are drawn by plotting the sensitivity (true positives from the positives) on the axis of ordinates and 1 minus the speci fi city (false positives from the negatives) at various threshold settings. An ROC curve near to the diagonal line indicates a coincidental result, given that values on the diagonal line result from an equal rate of true-positive and 1-minus-false-positive values. Accordingly, a perfect ROC curve would initially rise in a vertical manner (this would mean 100% true-positive values with 0% false values). Areas under the curves (AUCs) are one measure of the quality of a parameter inves- tigated in an ROC curve.

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