方法:通过决策树的预测算法。
目的:人工智能(AI)仍然是一个新兴领域,以前尚未用于指导胸腰椎爆裂骨折的治疗决策。建立这样的模型可以减少治疗建议的可变性。这项研究的目的是建立一个基于影像学变量的数学预测规则,以指导治疗决策。
方法:来自AO知识论坛创伤的22名外科医生回顾了来自脊柱TLA3/A4前瞻性研究的183例病例(分类,后韧带复合体(PLC)损伤的确定性程度,使用M1改性剂,粉碎程度,治疗建议)。审稿人\'地区被归类为欧洲,北美/南美和亚洲。分类和回归树用于创建模型,该模型将根据放射学变量预测治疗建议。我们应用了决策树模型,该模型考虑了数据非正态分布的可能性。用于验证多变量分析的交叉验证技术。
结果:模型的准确性为82.4%。算法中包含的变量为PLC损伤的确定性(%),粉碎程度(%),M1修饰符的使用和地理区域。算法表明,如果患者的PLC损伤的确定性超过57.5%,那么就有97.0%的机会接受手术。如果PLC损伤的确定性较低,粉碎率高于37.5%,在欧洲和亚洲,患者接受手术的概率为74.2%,而在北美/南美,患者接受手术的概率为22.7%.在整个算法中,M1修饰剂的使用使接受手术的概率平均增加了21.4%.
结论:本研究提出了一种预测分析算法来指导无神经功能缺损的胸腰椎爆裂骨折的治疗决策。超过57.5%的PLC损伤评估是接受手术的高度预测(97.0%)。与北美/南美相比,高度粉碎导致欧洲或亚洲接受手术的机会更高。未来的研究可能包括临床和其他变量,以增强预测能力或使用机器学习来预测胸腰椎爆裂骨折的结果。
METHODS: Predictive algorithm via decision tree.
OBJECTIVE: 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.
CONCLUSIONS: 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.