| 高升,孙军战,袁伟,等.经椎间孔腰椎椎体间融合术后出现假关节形成的风险因素分析及预测模型的构建与验证.骨科,2026,17(4): 343-349. |
| 经椎间孔腰椎椎体间融合术后出现假关节形成的风险因素分析及预测模型的构建与验证 |
| Risk factors for pseudarthrosis after transforaminal lumbar interbody fusion: Development and validation of a prediction model |
| 投稿时间：2025-07-23 |
| DOI：10.3969/j.issn.1674-8573.2026.04.007 |
| CN KeyWords: 经椎间孔腰椎椎体间融合术 假关节 列线图 骨质疏松 椎间隙高度 风险因素 |
| EN KeyWords: Transforaminal lumbar interbody fusion Pseudarthrosis Nomogram Osteoporosis Disc height Risk factors |
| Fund Project:联勤保障部队第九〇一医院科研项目（2025YGDZ10） |
|
| View Counts: 266 |
| PDF Download Counts: 83 |
| CN Abstract: |
| 目的 探讨经椎间孔腰椎椎体间融合术（transforaminal lumbar interbody fusion，TLIF）术后假关节形成的风险因素，构建并验证个体化预测模型，为临床风险分层干预提供依据。方法 回顾性分析2021年2月至2024年7月于我院行TLIF治疗的182例（234椎）的临床资料。收集年龄、性别、吸烟史、骨质疏松等基线特征，手术参数以及平均椎间隙高度丢失（△MDH）、上位椎弓根螺钉角度变化（△SSA）等术后影像动力学指标。先后通过单因素分析与多因素Logistic回归筛选独立风险因素，建立列线图（Nomogram）模型，并采用Bootstrap法（1 000次重抽样）进行内部验证，通过受试者工作特征（receiver operating characteristic，ROC）曲线、校准曲线和决策曲线分析评估模型性能。结果 182例患者TLIF术后假关节形成的发生率为9.34%（17/182）。多因素分析显示：骨质疏松症（OR=17.61，95% CI：2.60~119.45，P=0.003）、吸烟史（OR=12.95，95% CI：2.01~83.35，P=0.007）、△MDH（OR=25.08，95% CI：3.16~199.02，P=0.002）及△SSA（OR=20.08，95% CI：3.36~120.14，P=0.001）是TLIF术后假关节形成的独立风险因素。据此构建的Nomogram模型具有优异区分度（AUC=0.97，95% CI：0.94~1.00）、校准度（Hosmer-Lemeshow检验结果：P=0.992）及临床实用性（阈值概率为0.2~0.8时净获益显著提升）。结论 本研究建立的Nomogram模型整合了骨质疏松症、吸烟史、△MDH与△SSA等风险因子，可精准量化TLIF术后假关节形成风险，为高危患者围手术期和术后干预提供决策支持。 |
| EN Abstract: |
| Objective To investigate the risk factors for pseudarthrosis after transforaminal lumbar interbody fusion (TLIF), and to develop and validate an individualized prediction model to provide a clinical basis for risk-stratified intervention. Methods The clinical data of 182 patients involving 234 fusion segments who underwent TLIF in our hospital from February 2021 to July 2024 were retrospectively analyzed. Baseline characteristics (including age, sex, smoking history, and osteoporosis), surgical parameters, and postoperative imaging dynamics indexes, including loss of middle disc height (△MDH) and change in superior screw angle (△SSA), were collected. Univariate and multivariate Logistic regression analyses were performed sequentially to screen independent risk factors. A Nomogram prediction model was established and internally validated using the Bootstrap resampling with 1 000 repetitions. The performance of the model was evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. Results The incidence of pseudarthrosis after TLIF was 9.34% (17/182). Multivariate analysis showed that osteoporosis (OR=17.61, 95% CI: 2.60-119.45, P=0.003), smoking history (OR=12.95, 95% CI: 2.01-83.35, P=0.007), △MDH (OR=25.08, 95% CI: 3.16-199.02, P=0.002), and △SSA (OR=20.08, 95% CI: 3.36-120.14, P=0.001) were independent risk factors for pseudarthrosis after TLIF. The constructed Nomogram model demonstrated excellent discrimination (AUC=0.97, 95% CI: 0.94-1.00), calibration (Hosmer-Lemeshow test, P=0.992), and clinical utility (with significantly increased net benefit at threshold probabilities of 0.2-0.8). Conclusion The Nomogram model developed in this study, which integrates osteoporosis, smoking history, △MDH, and △SSA, can accurately quantify the risk of pseudarthrosis after TLIF, providing decision support for perioperative and postoperative interventions in high-risk patients. |
|
查看全文
Download PDF Reader |
| CLose |
|
|
|
