目的:开发并内部验证椎体增强后急性相邻椎体骨折(AVF)的术前临床预测模型,以支持术前决策。以椎体扩张(AVA)评分命名。
方法:在本预后研究中,一个多中心,采用来自日本6家医院的377例患者的回顾性单级椎体增强队列,得出AVF预测模型.后向逐步选择(p<0.05)用于选择椎体扩张后长达一个月的急性AVF的术前临床和影像学预测因子。来自14个预测因子。我们根据回归系数为每个选择的变量分配一个分数,并开发了AVA评分系统。我们评估了每个截止点的敏感性和特异性,曲线下面积(AUC),和校准作为诊断性能。使用自举进行内部验证以纠正乐观情绪。
结果:在用于模型推导的377名患者中,58例(15%)术后出现急性AVF。在5点AVA评分中总结了以下多变量分析的术前措施:椎管内不稳定性(≥5mm),局灶性脊柱后凸(≥10°),症状持续时间(≥30天),椎间隙,和以前的椎骨骨折史。内部验证显示0.019的平均乐观度和0.77的校正AUC。选择≤一个点的界限来对AVF的低风险进行分类,其中137例患者中只有4例(3%)发生AVF,敏感性为92.5%,特异性为45.6%.选择≥四个点的界限来对AVF的高风险进行分类,其中38人中有22人(58%)有AVF,敏感性为41.5%,特异性为94.5%.
结论:在这项研究中,发现AVA评分是一种简单的术前方法,可用于识别术后急性AVF低风险和高风险患者.该模型可应用于个体患者,并有助于椎体扩张前的决策。引用本文:骨关节J2022;104-B(1):97-102。
OBJECTIVE: To develop and internally validate a preoperative clinical prediction model for acute adjacent vertebral fracture (AVF) after vertebral augmentation to support preoperative decision-making, named the after vertebral augmentation (AVA) score.
METHODS: In this prognostic study, a multicentre, retrospective single-level vertebral augmentation cohort of 377 patients from six Japanese hospitals was used to derive an AVF prediction model. Backward stepwise selection (p < 0.05) was used to select preoperative clinical and imaging predictors for acute AVF after vertebral augmentation for up to one month, from 14 predictors. We assigned a score to each selected variable based on the regression coefficient and developed the AVA scoring system. We evaluated sensitivity and specificity for each cut-off, area under the curve (AUC), and calibration as diagnostic performance. Internal validation was conducted using bootstrapping to correct the optimism.
RESULTS: Of the 377 patients used for model derivation, 58 (15%) had an acute AVF postoperatively. The following preoperative measures on multivariable analysis were summarized in the five-point AVA score: intravertebral instability (≥ 5 mm), focal kyphosis (≥ 10°), duration of symptoms (≥ 30 days), intravertebral cleft, and previous history of vertebral fracture. Internal validation showed a mean optimism of 0.019 with a corrected AUC of 0.77. A cut-off of ≤ one point was chosen to classify a low risk of AVF, for which only four of 137 patients (3%) had AVF with 92.5% sensitivity and 45.6% specificity. A cut-off of ≥ four points was chosen to classify a high risk of AVF, for which 22 of 38 (58%) had AVF with 41.5% sensitivity and 94.5% specificity.
CONCLUSIONS: In this study, the AVA score was found to be a simple preoperative method for the identification of patients at low and high risk of postoperative acute AVF. This model could be applied to individual patients and could aid in the decision-making before vertebral augmentation. Cite this article: Bone Joint J 2022;104-B(1):97-102.