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

Dandurand, Charlotte; Fallah, Nader; Öner, Cumhur F; Bransford, Richard J; Schnake, Klaus; Vaccaro, Alexander R; Benneker, Lorin M; Vialle, Emiliano; Schroeder, Gregory D; Rajasekaran, Shanmuganathan; El-Skarkawi, Mohammad; Kanna, Rishi M; Aly, Mohamed; Holas, Martin; Canseco, Jose A; Muijs, Sander; Popescu, Eugen Cezar; Tee, Jin Wee; Camino-Willhuber, Gaston; Joaquim, Andrei Fernandes; ... (2024). Predictive Algorithm for Surgery Recommendation in Thoracolumbar Burst Fractures Without Neurological Deficits. Global spine journal, 14(1_suppl), 56S-61S. Sage 10.1177/21925682231203491

Preview

Text dandurand-et-al-2024-predictive-algorithm-for-surgery-recommendation-in-thoracolumbar-burst-fractures-without.pdf - Published Version Available under License Creative Commons: Attribution-Noncommercial-No Derivative Works (CC-BY-NC-ND). Download (791kB) | Preview

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.

Interest & Impact

Downloads

0 since deposited on 12 Feb 2024
0 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item