Neurological and Orthopaedic Surgery, University of Virginia, Charlottesville 22908, USA.
Spine (Phila Pa 1976). 2012 May 1;37(10):845-53. doi: 10.1097/BRS.0b013e31823b0892.
Retrospective, radiographical analysis of mathe-matical formulas used to predict sagittal vertical axis (SVA) after pedicle subtraction osteotomy (PSO).
Evaluate the ability of different formulas to predict SVA after PSO.
Failure to achieve optimal spinal alignment after spinal fusion correlates with poor outcomes. Numerous mathematical models have been proposed to aid preoperative PSO planning and predict postoperative SVA. Pelvic parameters have been shown to impact spinal alignment; however, many preoperative planning models fail to evaluate these. Compensatory changes within unfused spinal segments have also been shown to impact SVA. Predictive formulas that do not evaluate pelvic parameters and unfused spinal segments may erroneously guide PSO surgery. A formula that integrates pelvic tilt (PT) and spinal compensatory changes to predict optimal SVA has been previously proposed.
Comparative analysis of 5 mathematical models used to predict optimal postoperative SVA (<5 cm) after PSO was performed using a multicenter PSO database.
Radiographs of 147 patients, mean age 52 years (SD = 15 yr), who received 147 PSOs (42 thoracic and 105 lumbar) were evaluated. Mean preoperative and postoperative SVA was 108 mm (SD = 95 mm) and 30 mm (SD = 60 mm; P < 0.001), respectively. Each mathematical formula provided unique prediction for postoperative SA (Pearson R < 0.15). Formulas that neglected pelvic alignment poorly predicted final SVA and poorly correlated with optimal SVA. Formulas that evaluated pelvic morphology (pelvic incidence) had improved SVA prediction. The Lafage formulas, which incorporate PT and spinal compensatory changes, had the best SVA prediction (P < 0.05) and best correlation with optimal SVA (R = 0.75).
Preoperative planning for PSO is essential to optimize postoperative spinal alignment. Mathematical models that do not consider pelvic parameters and changes in unfused spinal segments poorly predict optimal postoperative alignment and may predispose to poor clinical outcomes. The Lafage formulas, which incorporated PT and spinal compensatory changes, best predicted optimal SVA.
回顾性分析用于预测经枢椎椎弓根切除截骨术后矢状垂直轴(SVA)的数学公式。
评估不同公式预测 PSO 术后 SVA 的能力。
脊柱融合术后未能获得理想的脊柱对线与不良结果相关。已经提出了许多数学模型来辅助脊柱融合术前规划和预测术后 SVA。骨盆参数已被证明会影响脊柱的排列;然而,许多术前规划模型未能评估这些参数。未融合的脊柱节段的代偿性变化也已被证明会影响 SVA。不评估骨盆参数和未融合的脊柱节段的预测公式可能会错误地指导 PSO 手术。先前已经提出了一种整合骨盆倾斜(PT)和脊柱代偿性变化来预测最佳 SVA 的公式。
使用多中心 PSO 数据库对用于预测 PSO 术后最佳术后 SVA(<5cm)的 5 个数学模型进行了比较分析。
对 147 名接受 147 例 PSO(42 例胸椎和 105 例腰椎)的患者的 X 光片进行了评估。平均术前和术后 SVA 分别为 108mm(SD=95mm)和 30mm(SD=60mm;P<0.001)。每个数学公式对术后 SVA 的预测都提供了独特的预测(Pearson R<0.15)。忽略骨盆排列的公式对最终 SVA 的预测效果不佳,与最佳 SVA 的相关性也较差。评估骨盆形态(骨盆入射角)的公式具有更好的 SVA 预测能力。纳入 PT 和脊柱代偿性变化的 Lafage 公式具有最佳的 SVA 预测能力(P<0.05),与最佳 SVA 的相关性也最好(R=0.75)。
PSO 的术前规划对于优化术后脊柱对线至关重要。不考虑骨盆参数和未融合的脊柱节段变化的数学模型对最佳术后对线的预测效果不佳,可能导致不良的临床结果。纳入了 PT 和脊柱代偿性变化的 Lafage 公式可以更好地预测最佳 SVA。