Trends in epidemiology and management of type II odontoid fractures: 20-year experience at a model system spine injury tertiary referral center.

Overview

abstract

STUDY DESIGN: A retrospective cohort study of consecutive type II Odontoid fractures presenting to a Level 1 Regional Model Systems Spinal Cord Injury Center between June 1985 and July 2006. OBJECTIVE: To assess trends in management of type II Odontoid fractures presenting to a Level 1 Model Systems Regional Spinal Cord Injury Center over a 20-year period. SUMMARY OF BACKGROUND DATA: Type II Odontoid fracture management is controversial, and a majority of studies have had relatively small cohorts. There is no consensus regarding definitive treatment, particularly in older patients. METHODS: Medical records of 263 consecutive type II Odontoid fractures from June 1985 to July 2006 were retrospectively reviewed. Patients were excluded if they had neurologic deficits, nonacute fracture, or ambiguous fracture classification. A cohort of 192 neurologically intact, acute type II odontoid fractures were identified. Admission records were reviewed for age, date of injury, date of admission, date of discharge, mechanism of injury, associated injuries, medical comorbidities, and radiologic findings. RESULTS: There was a statistically significant increase in the rate of presentation of type II odontoid fractures with time. The average age and medical comorbidities of the patient did not change over time. The probability of operative management markedly increased over time, corresponding to a statistically significant increase in length of hospital stay for patients undergoing surgery. The discharge disposition correlated significantly to both age of the patient and associated injuries. CONCLUSIONS: The number and frequency of type II odontoid fractures compared with other spine injuries seems to be increasing over the last 2 decades, which may be correlated with the increasing number of elderly persons in the population, given that referral patterns have been unchanged at our institution. Prospective outcomes data are needed to better elucidate optimal treatment algorithms from both, an outcomes and cost-efficacy perspective.