Abstract

Background ContextOdontoid fractures are relatively common. However, the literature is unclear how these fractures are best managed in many scenarios. As such, care is varied and poorly characterized. PurposeTo investigate the trends and predictive factors of surgical versus nonsurgical treatment and anterior versus posterior stabilization of odontoid fractures. Study Design/SettingRetrospective database cohort study. Patient SampleAdult patients with odontoid fractures between 2010 and 2021. Outcome MeasuresYearly trends and predictors of odontoid fracture management. MethodsAdult patients with odontoid fractures were abstracted from the large, national, administrative M161Ortho Pearldiver dataset. For operative versus nonoperative care of odontoid fractures, yearly rates were determined (since 2016 based on coding limitations). For anterior versus posterior stabilization, yearly rates were determined (2010-2021). Univariate and multivariable analyses were performed for both sets of comparisons. ResultsFor assessment of nonsurgical versus surgical management from 2016 to 2021, a total of 42,754 patients with odontoid fracture were identified, of which surgical intervention was done for 7.9%. Predictive factors of surgical intervention included being managed by a neurosurgeon (OR:1.29), being from Midwest United States (OR:1.35 relative to West), male sex (OR:1.20), and decreasing age (OR: 0.82 per decade) (p<0.001 for each). Of those undergoing surgical intervention, 33.6% had anterior surgery while 66.4% had posterior surgery (anterior surgery decreased from 36.4% in 2010 to 27.2% in 2021, p<0.001). Predictive factors of undergoing anterior versus posterior approach include having a neurosurgeon surgeon (OR:1.98), being from the Southern (OR:1.61 relative to Northeast), and having Medicare insurance (OR: 1.31) (p<0.001 for each). ConclusionsThe overall rate of surgery for odontoid fractures has remained similar over the past years. Of those undergoing surgery, less are being done from anterior. While these decisions were predicted by some clinical factors, both also correlated with non-clinical factors suggesting room for more consistent algorithms.

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