Discussion
This population-based study identified significant differences in demographics, injury patterns, and outcomes based on fracture diagnosis for patients with fractures to the second vertebrae. These variations in injuries and populations are indicative of differences in surgical management of the fracture. Our regression analysis identified between 9 and 16 variables that were independently associated with operative intervention. When considering decision tree modeling, only three covariates were determinants of surgical management for odontoid type II and odontoid I/III fractures, and these determinants were similar, by hierarchy: patient age, whether the fracture was displaced, and associated cervical injuries of either cervical subluxation or ligament sprain. The decision tree for non-odontoid fractures had more determinants for surgery, but most patients did not have cervical subluxation and were differentiated by presence of cervical ligament sprain and age. Thus, the overarching determinants for surgery of C2 fractures were the patient’s age and the cervical injuries themselves, and not more general injury characteristics such as the cause of injury, presenting vital signs, and severe concomitant injuries (polytrauma), nor patient demographics like sex, race, and comorbidities, nor hospital characteristics like trauma level and transfer status.
This large registry analysis illustrates the epidemiology of C2 fractures in the USA. Radovanovic et al previously described patterns of C2 fracture in geriatric patients treated in London, England.13 Odontoid type II fractures were the majority of fractures (57%), which is higher than the 43% we reported, possibly due to their studies’ older age inclusion criteria. Also contrary to our findings, they reported few differences between fracture types with respect to cause of injury, demographics, and outcomes. They also reported only 10.6% were injured in an MVC, which is lower than the 31% rate in our population and lower than a prior NTDB analysis of octogenarians where 17% were injured by MVC. Using the Swedish national registry, Robinson et al report that in geriatric patients, approximately 63% of C2 fractures are odontoid type II and 26% are odontoid type III, and in younger patients, approximately 34% are odontoid type II, 17% are odontoid type III, and 24% are hangman’s fractures.3 Our C2 population was 43% odontoid type II fractures, which were within the range of these Swedish studies.
Surgical rates of C2 fractures vary widely. Regarding age, in Sweden, the surgical rate was 22% among ≥70 years14; in England, it was 27% among ≥65 years13; in New Zealand, it was 11% among ≥70 years,15 and in the NTDB, it was previously reported to be 10% for octogenarians.16 The surgical rate among non-geriatric patients in Sweden was higher, at approximately 35%.17 In our population, surgical management peaked in patients aged 50–69 years at 18% and was lowest in patients ≥80 years old (8% surgically managed).
Few studies have compared the surgical rate by fracture diagnosis. In the Swedish registry, the surgical rate was 40% across all C2 fractures but was 53% with odontoid type II injuries.3 A Norwegian study of 336 patients reported surgical rates of 32% with odontoid type II fractures but only 4% with odontoid type III fractures.18 The surgical rate in our study was low at 14%, and the disparity with our data compared with other regions may be driven by the lower rate of surgical management for patients with odontoid type II fractures, at only 17.5%.
We used regression analysis and decision tree modeling to determine surgical versus non-operative management, as opposed to other studies that describe preferred treatment guidelines based on a review of the literature. Carvalho et al recommend surgical treatment for type III fractures with >5 mm displacement and type II fractures with >4 mm to 6 mm displacement and who are non-geriatric.9 Nourbakhsh and Hanson prefer conservative management for type I and III fractures regardless of age, and surgical management of type II fractures with >4 mm displacement.11 Wagner et al take a more general approach and prefer surgical management of geriatric patients with odontoid type II fractures.5 Rizvi et al examined compliance with their recommendation to operate on younger patients with displaced odontoid type II fractures, older patients with type II fractures regardless of displacement, and all displaced type III fractures regardless of age; the non-compliance rate of 36% was largely driven by age, and the authors conclude that age should play a larger consideration in decision trees for treatment choice.18 Our findings agree with the aforementioned studies, that displacement of the odontoid fracture and the patients’ age both play a role in whether surgical or non-operative management is preferred. The most favored age cut-off in our study was <80 years old for odontoid type II fractures and <85 years old for odontoid type I/III fractures, which is older than geriatric age in the aforementioned studies (often described as the ‘old old’ or octogenarians). Our analysis was more specific than prior studies because it modeled each fracture diagnosis separately and analyzed age continuously.
The gestalt that odontoid type II fractures are surgically managed, and hangman’s fractures and odontoid type I/III fractures are non-operatively managed does not appear to be supported by our analysis, as seen in the overall surgical rates were 17.5% and 11.1%, respectively. Still, when comparing the decision trees for odontoid type II and type I/III fractures, the estimated surgical rates were approximately twofold higher for odontoid type II fractures compared with odontoid type I/III fractures in the old old cohort, 9.1% versus 3.8% surgery, and in the younger cohort, 24.9% versus 13.7% surgery; as well as in the younger cohort who had a displaced fracture, 29.3% versus 16.3% surgery.
One of the studies’ biggest limitations is that the models’ findings should not be considered the standard of care or to guide optimal treatment decisions. Rather, these models demonstrate current practice patterns in the USA by C2 fracture diagnosis. This study is limited in determining whether patients were mismanaged or inappropriately selected for surgery, in part because there are no long-term (postdischarge) outcomes in the NTDB. Additional study is needed to compare outcomes by surgical management of C2 fractures based on the variables identified in this study (fracture diagnosis, fracture displacement, patients’ age, and associated cervical injuries), but also to adjust for variables that would influence outcomes in trauma patients such as abnormal vital signs, severe concomitant injuries, and comorbidities.
Second, the degree of fracture displacement was not available. Other studies recommended surgery based on whether the fracture was displaced >4, 5, or 6 mm. Displacement was a significant determinant for classifying into operative management, and our model fit statistics would likely have been improved had we been able to examine the degree of displacement. However, it should be noted that displacement of C2 fractures can be difficult to assess because it may change with the patient’s position and posture and even with respiration.9 Third, nearly 20% of patients with C2 fracture did not have more detailed diagnoses and were considered ‘unspecified’, and these patients were excluded from our analysis comparing types of C2 fractures. Fourth, 3% of patients had more than one C2 fracture diagnosis; patients with an odontoid type II fracture were characterized in that group even when there was also an odontoid type I/III or non-odontoid fracture, and patients with an odontoid type I/III fracture were characterized in that group even when they also had a non-odontoid fracture. We performed a sensitivity analysis to exclude the 3% of patients with more than one C2 fracture diagnosis, and there were no differences in the decision tree models (order and variables of nodes) or the model fit statistics (data not shown). Finally, the NTDB only includes data from contributing trauma centers, and these results might not be generalizable to non-participating hospitals. There were 183 of approximately 198 (93%) level I trauma centers contributing data and 206 of approximately 252 (82%) level II trauma centers contributing data, but far fewer III/IV and non-ACS verified centers contributed data.