成人脊柱畸形手术后,单靠脊柱对准能预测机械并发症吗?对齐,骨质量和软组织的机器学习比较。

IF 3 2区 医学 Q2 CLINICAL NEUROLOGY
Sameer Sundrani, Derek J Doss, Graham W Johnson, Harsh Jain, Omar Zakieh, Adam M Wegner, Julian G Lugo-Pico, Amir M Abtahi, Byron F Stephens, Scott L Zuckerman
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引用次数: 0

摘要

目的:机械并发症是成人脊柱畸形(ASD)手术后常见的并发症。虽然在ASD手术中实现理想的脊柱对齐是至关重要的,但仅对齐可能不能完全解释所有的机械并发症。作者试图确定哪种输入组合产生最敏感和特定的机器学习模型,以使用术后对齐、骨质量和软组织数据来预测机械并发症。方法:对2009年至2021年接受ASD手术的患者进行回顾性队列研究。纳入标准为融合≥5级,矢状/冠状畸形,随访至少2年。主要暴露变量为1)对准,通过l1 -骨盆角±3°、L4-S1前凸、矢状垂直轴、骨盆倾斜和冠状垂直轴在矢状面和冠状面进行评估;2)骨质量,通过双能x线吸收仪扫描的t评分评估;3)软组织,通过棘旁肌与椎体的比值和脂肪浸润来评估。主要结局为机械并发症。除了每个模型中的人口统计数据外,还训练了7个具有所有领域(对齐、骨质量和软组织)组合的机器学习模型。计算各模型的阳性预测值(PPV)。结果:231例平均年龄64±17岁接受ASD手术的患者(24%为男性)中,147例(64%)出现至少一种机械并发症。单独对准的模型表现较差,PPV为0.85。然而,具有对齐、骨质量和软组织的模型获得了0.90的高PPV,敏感性为0.67,特异性为0.84。此外,仅具有对齐的模型未能预测100例并发症中的15例,而具有所有三个域的模型仅未能预测100例中的10例。结论:这些结果支持并非所有机械故障都是由对准单独解释的概念。作者发现,排列、骨质量和软组织的组合提供了最准确的预测ASD手术后机械并发症的方法。虽然实现最佳对齐是必不可少的,但需要额外的数据,包括骨和软组织,以尽量减少机械并发症。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Does alignment alone predict mechanical complications after adult spinal deformity surgery? A machine learning comparison of alignment, bone quality, and soft tissue.

Objective: Mechanical complications are a vexing occurrence after adult spinal deformity (ASD) surgery. While achieving ideal spinal alignment in ASD surgery is critical, alignment alone may not fully explain all mechanical complications. The authors sought to determine which combination of inputs produced the most sensitive and specific machine learning model to predict mechanical complications using postoperative alignment, bone quality, and soft tissue data.

Methods: A retrospective cohort study was performed in patients undergoing ASD surgery from 2009 to 2021. Inclusion criteria were a fusion ≥ 5 levels, sagittal/coronal deformity, and at least 2 years of follow-up. The primary exposure variables were 1) alignment, evaluated in both the sagittal and coronal planes using the L1-pelvic angle ± 3°, L4-S1 lordosis, sagittal vertical axis, pelvic tilt, and coronal vertical axis; 2) bone quality, evaluated by the T-score from a dual-energy x-ray absorptiometry scan; and 3) soft tissue, evaluated by the paraspinal muscle-to-vertebral body ratio and fatty infiltration. The primary outcome was mechanical complications. Alongside demographic data in each model, 7 machine learning models with all combinations of domains (alignment, bone quality, and soft tissue) were trained. The positive predictive value (PPV) was calculated for each model.

Results: Of 231 patients (24% male) undergoing ASD surgery with a mean age of 64 ± 17 years, 147 (64%) developed at least one mechanical complication. The model with alignment alone performed poorly, with a PPV of 0.85. However, the model with alignment, bone quality, and soft tissue achieved a high PPV of 0.90, sensitivity of 0.67, and specificity of 0.84. Moreover, the model with alignment alone failed to predict 15 complications of 100, whereas the model with all three domains only failed to predict 10 of 100.

Conclusions: These results support the notion that not every mechanical failure is explained by alignment alone. The authors found that a combination of alignment, bone quality, and soft tissue provided the most accurate prediction of mechanical complications after ASD surgery. While achieving optimal alignment is essential, additional data including bone and soft tissue are necessary to minimize mechanical complications.

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来源期刊
Neurosurgical focus
Neurosurgical focus CLINICAL NEUROLOGY-SURGERY
CiteScore
6.30
自引率
0.00%
发文量
261
审稿时长
3 months
期刊介绍: Information not localized
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