| 刘传田,周坤生,黄干,等.基于MRI椎旁肌参数和临床特征构建腰椎间盘突出症行脊柱内镜减压手术预后不良的列线图预测模型.骨科,2026,17(1): 31-38. |
| 基于MRI椎旁肌参数和临床特征构建腰椎间盘突出症行脊柱内镜减压手术预后不良的列线图预测模型 |
| Construction of a nomogram prediction model for poor prognosis in patients with lumbar disc herniation undergoing percutaneous endoscopic transforaminal discectomy based on MRI paravertebral muscle parameters and clinical characteristics |
| 投稿时间：2025-04-29 |
| DOI：10.3969/j.issn.1674-8573.2026.01.007 |
| 中文关键词: 腰椎间盘突出症 经皮内镜椎间孔入路椎间盘切除术 椎旁肌 列线图 预后预测 骨骼肌指数 |
| 英文关键词: Lumbar disc herniation Percutaneous endoscopic transforaminal discectomy Paravertebral muscle Nomogram Prognostic prediction Skeletal muscle index |
| 基金项目:六安市科技计划项目（2024lakjey13） |
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| 中文摘要: |
| 目的 基于MRI椎旁肌参数及临床特征分析腰椎间盘突出症（LDH）病人行经皮内镜椎间孔入路椎间盘切除术（PETD）手术预后不良的独立预测因子，并构建出列线图预测模型。方法 回顾性纳入2021年1月至2023年12月于我院行PETD手术的138例LDH病人，术后1年根据改良MacNab标准将病人分为预后良好组与预后不良组。收集病人临床资料及MRI椎旁肌参数，多裂肌、竖脊肌和腰大肌的骨骼肌指数（SMI）、椎间盘角等。通过单因素分析、多因素Logistic回归筛选独立风险因素，构建列线图预测模型，并采用受试者工作特征（ROC）曲线、校准曲线及决策曲线（DCA）评估模型效能。结果 预后不良发生率为13.77%（19/138）。多因素分析显示，糖尿病[OR=15.296，95% CI（1.264，26.051），P=0.032]、Ⅱ型Modic改变[OR=54.366，95% CI（3.662，102.143），P=0.004]、患侧多裂肌SMI指数[OR=0.975，95% CI（0.959，0.992），P=0.004]、竖脊肌SMI指数[OR=0.978，95% CI（0.964，0.993），P=0.003]是独立预测因素。基于此构建了列线图预测模型。ROC曲线验证该列线图预测模型的区分度，曲线下面积为0.891；该模型的校准曲线与Hosmer-Lemeshow检验（P=0.802）证实其预测准确性较高，DCA曲线显示该模型的临床净获益显著。结论 糖尿病、Ⅱ型Modic改变及患侧多裂肌和竖脊肌SMI指数是LDH病人行PETD手术预后不良的独立预测因素，基于此构建的列线图预测模型实现了术前风险的可视化量化评估，该模型具有较高的区分效能与校准度，并为临床决策提供了可靠依据，具有较高的临床参考价值。 |
| 英文摘要: |
| Objective To analyze the independent predictive factors for poor prognosis in patients with lumbar disc herniation (LDH) undergoing percutaneous endoscopic transforaminal discectomy (PETD) based on MRI paravertebral muscle parameters and clinical characteristics, and to construct a nomogram prediction model. Methods A retrospective study was conducted, including 138 LDH patients who underwent PETD from January 2021 to December 2023. Patients were divided into a good prognosis group and a poor prognosis group based on the modified MacNab criteria at one year postoperatively. Clinical data and MRI paravertebral muscle parameters [skeletal muscle index (SMI) of multifidus, erector spinae, and psoas major, intervertebral disc angle, etc.] were collected. Univariate analysis and multivariate Logistic regression were performed to identify independent risk factors and construct the nomogram prediction model. The model's performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). Results The incidence of poor prognosis was 13.77% (19/138). Multivariate analysis revealed that diabetes [OR=15.296, 95% CI (1.264, 26.051), P=0.032], type Ⅱ Modic changes [OR=54.366, 95% CI (3.662, 102.143), P=0.004], and the SMI index of the affected multifidus [OR=0.975, 95% CI (0.959, 0.992), P=0.004] and erector spinae [OR=0.978, 95% CI (0.964, 0.993), P=0.003] were independent predictive factors. Based on these findings, a nomogram prediction model was constructed. The ROC curve validated the discrimination ability of the nomogram prediction model, with an AUC of 0.891. The calibration curve and Hosmer-Lemeshow test (P=0.802) confirmed the model's high predictive accuracy, and the DCA curve demonstrated significant clinical net benefit. Conclusion Diabetes, type Ⅱ Modic changes, and the SMI indices of the affected multifidus and erector spinae are independent predictive factors for poor prognosis in LDH patients undergoing PETD. The constructed nomogram prediction model enables visual and quantitative assessment of preoperative risk, demonstrating high discrimination and calibration capabilities, providing reliable evidence for clinical decision-making, and holding significant clinical reference value. |
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