Development and validation of a nomogram to predict the risk of adjacent segment disease after transforaminal lumbar interbody fusion in patients with lumbar degenerative diseases

Abstract

Abstract Background Adjacent segment disease (ASD) is a common and serious complication that can develop in the mid- to long-term after lumbar fusion surgery. However, the underlying mechanisms of ASD are not yet fully understood. This study aimed to develop and validate a risk prediction model for ASD in patients who underwent transforaminal lumbar interbody fusion (TLIF) for lumbar degenerative diseases. Methods Patients with lumbar degenerative disease who underwent TLIF between January 2015 and December 2016 were included in the retrospective study. The participants were divided into two groups: ASD and non-ASD. Univariate and multivariate logistic regression analyses were performed to identify factors influencing ASD after TLIF surgery. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the model’s discrimination, calibration and clinical value, respectively. Results A total of 11.5% (59/513) of patients developed ASD. Higher BMI, lower BMD, higher disc grade, and reduced disc height were identified as independent risk factors for ASD after TLIF. The model demonstrated good discrimination in both the training and validation sets, with calibration and Hosmer-Lemeshow tests confirming accuracy, and DCA demonstrating clinical applicability. Conclusions The nomogram model demonstrated promise in predicting ASD in patients who underwent TLIF, aiding clinicians in selecting the most suitable surgical approach and optimizing surgical decisions.

Keywords

• Transforaminal lumbar interbody fusion
• Adjacent segment disease
• Nomogram
• Predictive model