A nomogram for predicting adjacent segment disease after anterior cervical surgery

Abstract

Adjacent segment disease (ASD) is a recognized complication following anterior cervical surgery and may adversely affect long-term clinical outcomes. This study aimed to develop and internally validate a prognostic nomogram incorporating clinical and radiological variables to predict the risk of ASD after anterior cervical surgery. In this single-center retrospective cohort study, 234 patients who underwent anterior cervical surgery were included. The primary endpoint was the development of ASD during follow-up. Eleven candidate predictors were evaluated. Least absolute shrinkage and selection operator (LASSO) regression was applied for variable selection, followed by multivariable logistic regression to identify independent predictors. A nomogram was constructed, and its performance was assessed using the area under the receiver operating characteristic curve (AUROC), calibration analysis, and decision curve analysis. Internal validation was conducted using bootstrap resampling. ASD developed in 26 patients (11.1%). LASSO regression identified age, treatment modality (cage), mJOA score, disc height, and Cobb angle as the most predictive variables. In multivariable analysis, cage use was independently associated with an increased risk of ASD (odds ratio = 3.56; 95% CI: 1.46–8.68; P = .005). Increased disc height and Cobb angle were also significantly associated with ASD. The nomogram demonstrated good discriminative performance (AUROC = 0.78) with satisfactory calibration and no evidence of overfitting after internal validation. This nomogram provides an individualized risk prediction model for ASD following anterior cervical surgery. By integrating key clinical and radiological parameters, it may assist clinicians in identifying high-risk patients and optimizing postoperative management strategies.