医学
接收机工作特性
置信区间
经皮
逻辑回归
椎间盘切除术
回顾性队列研究
外科
Lasso(编程语言)
腰椎间盘突出症
腰椎
内科学
万维网
计算机科学
作者
Xin Li,Bin Pan,Lin Yu Cheng,Gen Li,Jian Liu,Feng Yuan
出处
期刊:Pain Physician
[American Society of Interventional Pain Physicians]
日期:2023-01-31
卷期号:26 (1): 81-90
被引量:3
标识
DOI:10.36076/ppj.2023.26.81
摘要
Recurrence of lumbar disc herniation (LDH) is an adverse event after percutaneous endoscopic transforaminal discectomy (PETD). Accurate prediction of the risk of recurrent LDH (rLDH) after surgery remains a major challenge for spine surgeons.To develop and validate a prognostic model based on risk factors for rLDH after PETD.Retrospective study.Inpatient surgery center.Clinical data were retrospectively collected from 645 patients with LDH who underwent PETD at the Affiliated Hospital of Xuzhou Medical University from January 1, 2017 to January 1, 2021. Predictors significantly associated with rLBH were screened according to least absolute shrinkage and selection operator (LASSO) regression, and a prognostic model was established, followed by internal model validation using the enhanced bootstrap method. The performance of the model was assessed using receiver operating characteristic (ROC) curves and calibration curves. Finally, the clinical usefulness of the model was analyzed using decision curve analysis (DCA) and clinical impact curves (CICs).Among the 645 patients included in this study, 56 experienced recurrence of LDH after PETD (8.7%). Seven factors significantly associated with rLDH were selected by LASSO regression, including age, type of herniation, level of herniation, Modic changes, Pfirrmann classification, smoking, and history of high-intensity physical work. The bias-corrected curve of the model fit well with the apparent curve, and the area under the ROC curve was 0.822 (95% confidence interval, 0.76-0.88). The DCA and CIC confirmed that the prognostic model had good clinical utility.This is a single-center study, and we used internal validation only.The prognostic model developed in this study had excellent comprehensive performance and could well predict the risk of rLDH after PETD. This model could be used to identify patients at high risk for rLDH at an early stage to individualize the patient's treatment modality and postoperative rehabilitation plan.
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