Understanding Decision Making as It Influences Treatment in Thoracolumbar Burst Fractures Without Neurological Deficit: Conceptual Framework and Methodology.

STUDY DESIGN This paper presents a description of a conceptual framework and methodology that is applicable to the manuscripts that comprise this focus issue. OBJECTIVES Our goal is to present a conceptual framework which is relied upon to better understand the processes through which surgeons make therapeutic decisions around how to treat thoracolumbar burst fractures (TL) fractures. METHODS We will describe the methodology used in the AO Spine TL A3/4 Study prospective observational study and how the radiographs collected for this study were utilized to study the relationships between various variables that factor into surgeon decision making. RESULTS With 22 expert spine trauma surgeons analyzing the acute CT scans of 183 patients with TL fractures we were able to perform pairwise analyses, look at reliability and correlations between responses and develop frequency tables, and regression models to assess the relationships and interactions between variables. We also used machine learning to develop decision trees. CONCLUSIONS This paper outlines the overall methodological elements that are common to the subsequent papers in this focus issue

Using Equipoise to Determine the Radiographic Characteristics Leading to Agreement on Best Treatment for Thoracolumbar Burst Fractures Without Neurologic Deficits.

STUDY DESIGN Retrospective analysis of prospectively collected data. OBJECTIVES Our goal was to assess radiographic characteristics associated with agreement and disagreement in treatment recommendation in thoracolumbar (TL) burst fractures. METHODS A panel of 22 AO Spine Knowledge Forum Trauma experts reviewed 183 cases and were asked to: (1) classify the fracture; (2) assess degree of certainty of PLC disruption; (3) assess degree of comminution; and (4) make a treatment recommendation. Equipoise threshold used was 77% (77:23 distribution of uncertainty or 17 vs 5 experts). Two groups were created: consensus vs equipoise. RESULTS Of the 183 cases reviewed, the experts reached full consensus in only 8 cases (4.4%). Eighty-one cases (44.3%) were included in the agreement group and 102 cases (55.7%) in the equipoise group. A3/A4 fractures were more common in the equipoise group (92.0% vs 83.7%, P < .001). The agreement group had higher degree of certainty of PLC disruption [35.8% (SD 34.2) vs 27.6 (SD 27.3), P < .001] and more common use of the M1 modifier (44.3% vs 38.3%, P < .001). Overall, the degree of comminution was slightly higher in the equipoise group [47.8 (SD 20.5) vs 45.7 (SD 23.4), P < .001]. CONCLUSIONS The agreement group had a higher degree of certainty of PLC injury and more common use of M1 modifier (more type B fractures). The equipoise group had more A3/A4 type fractures. Future studies are required to identify the role of comminution in decision making as degree of comminution was slightly higher in the equipoise group

Interobserver Reliability in the Classification of Thoracolumbar Fractures Using the AO Spine TL Injury Classification System Among 22 Clinical Experts in Spine Trauma Care.

STUDY DESIGN Reliability study utilizing 183 injury CT scans by 22 spine trauma experts with assessment of radiographic features, classification of injuries and treatment recommendations. OBJECTIVES To assess the reliability of the AOSpine TL Injury Classification System (TLICS) including the categories within the classification and the M1 modifier. METHODS Kappa and Intraclass correlation coefficients were produced. Associations of various imaging characteristics (comminution, PLC status) and treatment recommendations were analyzed through regression analysis. Multivariable logistic regression modeling was used for making predictive algorithms. RESULTS Reliability of the AO Spine TLICS at differentiating A3 and A4 injuries (N = 71) (K = .466; 95% CI .458 - .474; P < .001) demonstrated moderate agreement. Similarly, the average intraclass correlation coefficient (ICC) amongst A3 and A4 injuries was excellent (ICC = .934; 95% CI .919 - .947; P < .001) and the ICC between individual measures was moderate (ICC = .403; 95% CI .351 - .461; P < .001). The overall agreement on the utilization of the M1 modifier amongst A3 and A4 injuries was fair (K = .161; 95% CI .151 - .171; P < .001). The ICC for PLC status in A3 and A4 injuries averaged across all measures was excellent (ICC = .936; 95% CI .922 - .949; P < .001). The M1 modifier suggests respondents are nearly 40% more confident that the PLC is injured amongst all injuries. The M1 modifier was employed at a higher frequency as injuries were classified higher in the classification system. CONCLUSIONS The reliability of surgeons differentiating between A3 and A4 injuries in the AOSpine TLICS is substantial and the utilization of the M1 modifier occurs more frequently with higher grades in the system

Predictive Algorithm for Surgery Recommendation in Thoracolumbar Burst Fractures Without Neurological Deficits.

STUDY DESIGN Predictive algorithm via decision tree. OBJECTIVES Artificial intelligence (AI) remain an emerging field and have not previously been used to guide therapeutic decision making in thoracolumbar burst fractures. Building such models may reduce the variability in treatment recommendations. The goal of this study was to build a mathematical prediction rule based upon radiographic variables to guide treatment decisions. METHODS Twenty-two surgeons from the AO Knowledge Forum Trauma reviewed 183 cases from the Spine TL A3/A4 prospective study (classification, degree of certainty of posterior ligamentous complex (PLC) injury, use of M1 modifier, degree of comminution, treatment recommendation). Reviewers' regions were classified as Europe, North/South America and Asia. Classification and regression trees were used to create models that would predict the treatment recommendation based upon radiographic variables. We applied the decision tree model which accounts for the possibility of non-normal distributions of data. Cross-validation technique as used to validate the multivariable analyses. RESULTS The accuracy of the model was excellent at 82.4%. Variables included in the algorithm were certainty of PLC injury (%), degree of comminution (%), the use of M1 modifier and geographical regions. The algorithm showed that if a patient has a certainty of PLC injury over 57.5%, then there is a 97.0% chance of receiving surgery. If certainty of PLC injury was low and comminution was above 37.5%, a patient had 74.2% chance of receiving surgery in Europe and Asia vs 22.7% chance in North/South America. Throughout the algorithm, the use of the M1 modifier increased the probability of receiving surgery by 21.4% on average. CONCLUSION This study presents a predictive analytic algorithm to guide decision-making in the treatment of thoracolumbar burst fractures without neurological deficits. PLC injury assessment over 57.5% was highly predictive of receiving surgery (97.0%). A high degree of comminution resulted in a higher chance of receiving surgery in Europe or Asia vs North/South America. Future studies could include clinical and other variables to enhance predictive ability or use machine learning for outcomes prediction in thoracolumbar burst fractures

Expert Opinion, Real-World Classification, and Decision-Making in Thoracolumbar Burst Fractures Without Neurologic Deficits?

STUDY DESIGN Retrospective analysis of prospectively collected data. OBJECTIVES To compare decision-making between an expert panel and real-world spine surgeons in thoracolumbar burst fractures (TLBFs) without neurological deficits and analyze which factors influence surgical decision-making. METHODS This study is a sub-analysis of a prospective observational study in TL fractures. Twenty two experts were asked to review 183 CT scans and recommend treatment for each fracture. The expert recommendation was based on radiographic review. RESULTS Overall agreement between the expert panel and real-world surgeons regarding surgery was 63.2%. In 36.8% of cases, the expert panel recommended surgery that was not performed in real-world scenarios. Conversely, in cases where the expert panel recommended non-surgical treatment, only 38.6% received non-surgical treatment, while 61.4% underwent surgery. A separate analysis of A3 and A4 fractures revealed that expert panel recommended surgery for 30% of A3 injuries and 68% of A4 injuries. However, 61% of patients with both A3 and A4 fractures received surgery in the real world. Multivariate analysis demonstrated that a 1% increase in certainty of PLC injury led to a 4% increase in surgery recommendation among the expert panel, while a .2% increase in the likelihood of receiving surgery in the real world. CONCLUSION Surgical decision-making varied between the expert panel and real-world treating surgeons. Differences appear to be less evident in A3/A4 burst fractures making this specific group of fractures a real challenge independent of the level of expertise