Streamlining the Journey of Research Into Clinical Practice: Making Your Patients and Practice Flourish Optimizing Management and Minimizing Risk of Osteoporotic Vertebral Fractures - Perspectives of the AO Spine KF Trauma and Infection Group Key Opinion Leaders.

STUDY DESIGN Literature review with clinical recommendations. OBJECTIVE To highlight important studies about osteoporotic spinal fractures (OF) that may be integrated into clinical practice based on the assessment of the AO Spine KF Trauma and Infection group key opinion leaders. METHODS 4 important studies about OF that may affect current clinical practice of spinal surgeons were selected and reviewed with the aim of providing clinical recommendations to streamline the journey of research into clinical practice. Recommendations were graded as strong or conditional following the GRADE methodology. RESULTS 4 studies were selected. Article 1: a validation of the Osteoporotic Fracture (OF)-score to treat OF fractures. Conditional recommendation to incorporate the OF score in the management of fractures to improve clinical results. Article 2: a randomized multicenter study comparing romosozumab/alendronate vs alendronate to decrease the incidence of new vertebral fractures. Strong recommendation that the group receiving romosozumab/alendronate had a decreased risk of new OF when compared with the alendronate only group only. Article 3: a systematic literature review of spinal orthoses in the management of. Conditional recommendation to prescribe a spinal orthosis to decrease pain and improve quality of life. Article 4: post-traumatic deformity after OF. A conditional recommendation that middle column injury and pre-injury use of steroids may lead to high risk of post-traumatic deformity after OF. CONCLUSIONS Management of patients with OF is still complex and challenging. This review provides some recommendations that may help surgeons to better manage these patients and improve their clinical practice

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\u27 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

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 \u3c .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 \u3c .001) and the ICC between individual measures was moderate (ICC = .403; 95% CI .351 – .461; P \u3c .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 \u3c .001). The ICC for PLC status in A3 and A4 injuries averaged across all measures was excellent (ICC = .936; 95% CI .922 – .949; P \u3c .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

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

Compressive Strength Estimation of Waste Marble Powder Incorporated Concrete Using Regression Modelling

A tremendous volumetric increase in waste marble powder as industrial waste has recently resulted in high environmental concerns of water, soil and air pollution. In this paper, we exploit the capabilities of machine learning to compressive strength prediction of concrete incorporating waste marble powder for future use. Experimentation has been carried out using different compositions of waste marble powder in concrete and varying water binder ratios of 0.35, 0.40 and 0.45 for the analysis. Effect of different dosages of superplasticizer has also been considered. In this paper, different regression algorithms to analyse the effect of waste marble powder on concrete, viz., multiple linear regression, K-nearest neighbour, support vector regression, decision tree, random forest, extra trees and gradient boosting, have been exploited and their efficacies have been compared using various statistical metrics. Experiments reveal random forest as the best model for compressive strength prediction with an R2 value of 0.926 and mean absolute error of 1.608. Further, shapley additive explanations and variance inflation factor analysis showcase the capabilities of the best achieved regression model in optimizing the use of marble powder as partial replacement of cement in concrete

The Influence of Comminution and Posterior Ligamentous Complex Integrity on Treatment Decision Making in Thoracolumbar Burst Fractures Without Neurologic Deficit?

STUDY DESIGN A prospective study. OBJECTIVE to evaluate the impact of vertebral body comminution and Posterior Ligamentous Complex (PLC) integrity on the treatment recommendations of thoracolumbar fractures among an expert panel of 22 spine surgeons. METHODS A review of 183 prospectively collected thoracolumbar burst fracture computed tomography (CT) scans by an expert panel of 22 trauma spine surgeons to assess vertebral body comminution and PLC integrity. This study is a sub-study of a prospective observational study of thoracolumbar burst fractures (Spine TL A3/A4). Each expert was asked to grade the degree of comminution and certainty about the PLC disruption from 0 to 100, with 0 representing the intact vertebral body or intact PLC and 100 representing complete comminution or complete PLC disruption, respectively. RESULTS ≥45% comminution had a 74% chance of having surgery recommended, while <25% comminution had an 86.3% chance of non-surgical treatment. A comminution from 25 to 45% had a 57% chance of non-surgical management. ≥55% PLC injury certainity had a 97% chance of having surgery, and ≥45-55% PLC injury certainty had a 65%. <20% PLC injury had a 64% chance of having non-operative treatment. A 20 to 45% PLC injury certainity had a 56% chance of non-surgical management. There was fair inter-rater agreement on the degree of comminution (ICC .57 [95% CI 0.52-.63]) and the PLC integrity (ICC .42 [95% CI 0.37-.48]). CONCLUSION The study concludes that vetebral comminution and PLC integrity are major dterminant in decision making of thoracolumbar fractures without neurological deficit. However, more objective, reliable, and accurate methods of assessment of these variables are warranted

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

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