Haddad Sleiman, Pizones Javier, Raganato Riccardo, Safaee Michael M, Scheer Justin K, Pellisé Ferran, Ames Christopher P
Spine Surgery Unit, Hospital Universitario Vall d'Hebron, Barcelona, Spain.
Spine Unit, Department of Orthopedic Surgery, Hospital Universitario La Paz, Madrid, Spain
Int J Spine Surg. 2023 Jun;17(S1):S34-S44. doi: 10.14444/8502. Epub 2023 May 10.
Adult spinal deformity (ASD) surgery is still associated with high surgical risks. Machine learning algorithms applied to multicenter databases have been created to predict outcomes and complications, optimize patient selection, and improve overall results. However, the multiple data points currently used to create these models allow for 70% of accuracy in prediction. We need to find new variables that can capture the spectrum of probability that is escaping from our control. These proposed variables are based on patients' biological dimensions, such as frailty, sarcopenia, muscle and bone (tissue) sampling, serological assessment of cellular senescence, and circulating biomarkers that can measure epigenetics, inflammaging, and -omics. Many of these variables are proven to be modifiable and could be improved with proper nutrition, toxin avoidance, endurance exercise, and even surgery. The purpose of this manuscript is to describe the different future data points that can be implemented in ASD assessment to improve modeling prediction, allow monitoring their response to prerehabilitation programs, and improve patient counseling.
成人脊柱畸形（ASD）手术仍伴随着较高的手术风险。已创建应用于多中心数据库的机器学习算法，用于预测结果和并发症、优化患者选择并改善整体疗效。然而，当前用于创建这些模型的多个数据点仅能实现70%的预测准确率。我们需要找到新的变量，以捕捉那些超出我们控制范围的概率范围。这些提议的变量基于患者的生物学维度，如衰弱、肌肉减少症、肌肉和骨骼（组织）采样、细胞衰老的血清学评估以及可测量表观遗传学、炎症衰老和组学的循环生物标志物。这些变量中有许多已被证明是可改变的，通过适当的营养、避免接触毒素、耐力运动甚至手术都可以得到改善。本手稿的目的是描述不同的未来数据点，这些数据点可用于ASD评估，以改进建模预测、监测患者对康复前计划的反应并改善患者咨询。
