关键词:

腰椎融合术, 邻椎病, 脊柱-骨盆参数, 椎间盘特征, 风险模型

Abstract:

Objective　To construct and validate a risk model for adjacent segment disease (ASD) after lumbar fusion surgery by analyzing measurements of the spinal pelvic sagittal plane. Methods　A retrospective analysis was conducted on the data of 197 patients who underwent transforaminal lumbar interbody fusion at the First Affiliated Hospital of Xi'an Jiaotong University Yulin Hospital from October 2020 to October 2023. After one year of follow-up, the patients finally included in the study were divided into an ASD group and a non-ASD group. The general data, surgical data, and preoperative spinal-pelvic sagittal imaging parameters [thoracic kyphosis (TK), lumbar lordosis (LL), segmental lordosis (SL), sacral slope (SS), pelvic tilt (PT), pelvic incident angle (PI), relative height of intervertebral space, etc] of the two groups were compared. Multivariate logistic regression analysis was used to analyze the correlations between preoperative spinal-pelvic sagittal imaging parameters and the occurrence of ASD after lumbar fusion surgery. A risk prediction model was constructed using R software, and the model's discrimination was evaluated through the receiver operating characteristic curve (ROC). The Bootstrap method was used for 1,000 repeated samplings to verify the predictive performance of the model. Statistical analysis was performed using independent sample t test and χ² test. Results　Of the 197 patients, 185 completed the follow-up (93.90%). Among them, 43 patients (23.24%) were in the ASD group, including 26 males and 17 females, with an age of (49.44±10.12) years old; 142 patients (76.75%) were in the non-ASD group, including 88 males and 54 females, with an age of (50.13±10.19) years old. Multivariate logistic regression analysis indicated that PT, SL, TK, PI-LL, and interference from adjacent segments during surgery were the influencing factors for ASD after lumbar fusion surgery (all P<0.05). The ROC analysis of the risk prediction model constructed based on these indicators showed that the area under the curve of the model to predict the postoperative ASD risk was 0.970, with a sensitivity of 97.7% and a specificity of 85.9%, indicating that the model had good discriminative ability. Bootstrap validation showed that the deviation calibration curve of the model was in good agreement with the ideal curve, with a mean absolute error (MAE) of 0.031. Conclusions　Spinal pelvic sagittal balance plays an important role in the growth and development of the spine, and PT, SL, TK, PI-LL, and interference from adjacent segments during surgery are influencing factors for ASD after lumbar fusion surgery. This study provides clinical evidences for predicting the risk of ASD after lumbar fusion surgery.

Key words:

Lumbar fusion surgery,  Adjacent segment disease,  Spinal pelvic parameters,  Intervertebral disc characteristics,  Risk model