Letter: Validation and Comparison of Common Thoracolumbar Injury Classification Treatment Algorithms and a Novel Modification

Le résumé fourni par la source

To the Editor: We read with great interest the article “Validation and Comparison of Common Thoracolumbar Injury Classification Treatment Algorithms and a Novel Modification” by Withreowet et al.1 This is a retrospective single-institution review of 476 patients with acute thoracolumbar fractures. The purpose of this study was to evaluate the actual and recommended treatments based on 3 different injury classifications and severity scores: thoracolumbar injury classification system (TLICS), Thoracolumbar AOSpine Injury Severity Score (TL AOSIS), and mTLICS, which adds 2 points for vertebral body height loss >50% and spinal canal stenosis >50% at the fracture site.1 The mTLICS demonstrated enhanced sensitivity and accuracy relative to TLICS, as well as superior accuracy and specificity compared with TL AOSIS.1 The sensitivity of TL AOSIS was higher than that of TLICS. Although we appreciate the authors' efforts in conducting such a large population study, we would like to add a few comments to the discussion.1 The study emphasizes a significant limitation of the historical classification schemes for thoracolumbar fractures in defining “bony instability” and guiding the decision making in burst fractures.2,3 TLICS assigned 2 points to all burst fractures, regardless of their severity. TLAOSIS differentiated the severity of burst fractures by the presence of double endplate fractures (A4) vs a single endplate (A3).3 However, previous research failed to demonstrate a difference in outcome between A3 and A4 fractures.4 In clinical practice, vertebral height loss, canal stenosis, and a variety of kyphotic angles are used as severity markers for burst fractures; however, none have been integrated into classification schemes.1 In addition, beyond the characterization of instability, patient-specific variables may affect the decision making, such as osteoporosis, smoking, age, and occupation.5 The lack of an agreed-upon definition for bony instability might be attributed to several reasons. First, most of these criteria were granted “face validity” by expert consensus; however, their true “prognostic validity,” ie, correlation with treatment outcome, has yet to be validated.3,6 Such validation is limited by inherent challenges in studying the treatment outcome of thoracolumbar fractures, including the difficulty in randomization, the need for disease-specific outcome measures, and limited reliability in distinguishing A3 and A4 fractures.2,3,7 Second, integrating all potential severity markers into the classification schemes will reduce their reliability and practicality for everyday use.6 That is why the AO Spine classification opted for using a single severity marker, “double endplate fracture,” to favor reliability and practicality. In addition, it is difficult to integrate quantitative parameters such as vertebral height loss and canal stenosis because of their inherent measurement errors, the variability of measurement techniques, and the lack of an agreed-upon cutoff point.8 Biomechanical studies have provided valuable insights into the instability criteria for burst fractures.9 Lanagarana et al described 2 major pathomechanical types of burst fractures related to axial loading with flexion or axial loading with shear movement.9 Axial loading with shear led to facet loading and transfer of energy to posterior elements, causing a vertical laminar fracture, interpedicular widening, and canal compromise.7 Those 3 parameters have been shown in previous research to correlate with neurological deficits or failure of nonoperative treatment.5,10 By contrast, Li et al observed that axial loading in flexion had the greatest effect on vertebral height loss and kyphotic angle.11 The preceding discussion prompts a significant inquiry into the philosophy underlying classification systems for thoracolumbar fractures and their influence on decision-making processes.3,12 Classifying thoracolumbar fractures into specific morphological subtypes may offer definitive guidance for treatment, such as distinguishing types B and C injuries from type A injuries or compression from burst fractures.13 However, classifying burst fractures into morphological subgroups is inadequate for informing treatment decisions because of their morphological and pathomechanical variability.4 The decision-making process depends on numerous radiological severity indicators that are difficult to incorporate into any classification system.3 The classification schemes could provide a general guide for the decision-making process. For example, A4 fractures are more severe and necessitate surgery than A3 fractures.3 However, it is necessary to develop a treatment algorithm that incorporates all potential variables in addition to the classification scheme for individual decision making.14 Combining all these variables may be advantageous through the implementation of a machine learning algorithm.3,14

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.