Reliability and application of the new morphological classification system for chronic symptomatic osteoporotic thoracolumbar fracture

Abstract

Abstract Background We propose a new classification system for chronic symptomatic osteoporotic thoracolumbar fracture (CSOTF) based on fracture morphology. Research on CSOTF has increased in recent years; however, the lack of a standard classification system has resulted in inconvenient communication, research, and treatment. Previous CSOTF classification studies exhibit different symptoms, with none being widely accepted. Methods Imaging data of 368 patients with CSOTF treated at our hospital from January 2010 to June 2017 were systematically analyzed to develop a classification system. Imaging examinations included dynamic radiography, computed tomography scans, and magnetic resonance imaging. Ten investigators methodically studied the classification system grading in 40 cases on two occasions, examined 1 month apart. Kappa coefficients (κ) were calculated to determine intraobserver and interobserver reliability. Based on the radiographic characteristics, the patients were divided into 5 types, and different treatments were suggested for each type. Clinical outcome evaluation included using the visual analog score (VAS), the Oswestry disability index (ODI), and the American Spinal Injury Association (ASIA) impairment scale. Results The new classification system for CSOTF was divided into types I–V according to whether the CSOTF exhibited dynamic instability, spinal stenosis or kyphosis deformity. Intra- and interobserver reliability were excellent for all types (κ = 0.83 and 0.85, respectively). The VAS score and ODI of each type were significantly improved at the final follow-up compared with those before surgery. In all patients with neurological impairment, the ASIA grading after surgery was significantly improved compared with that before surgery (P < 0.001). Conclusions The new classification system for CSOTF demonstrated excellent reliability in this initial assessment. The treatment algorithm based on the classification can result in satisfactory improvement of clinical efficacy for the patients of CSOFT.