Development and validation of a predictive nomogram for cage migration after posterior lumbar interbody fusion: a...

Posterior lumbar interbody fusion (PLIF) is widely used for lumbar diseases, but cage migration (CM) remains a frequent and serious complication. Identifying risk factors for CM is crucial to improving surgical outcomes.

A retrospective analysis was conducted on 517 patients who underwent PLIF surgery at Nanyang Central Hospital between January 2020 and December 2024. Patients were divided into CM (n = 46) and non-CM groups (n = 471) based on postoperative imaging findings. Demographic characteristics, intraoperative parameters, and radiographic data were collected. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of CM. A nomogram model was constructed based on significant variables, and its performance was evaluated using a receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

The overall incidence of CM was 8.9%. Univariate analysis revealed significant differences in the rates of osteoporosis, spondylolisthesis, Modic changes, PI-LL mismatch, and preoperative hemoglobin levels between the two groups (p < 0.05). Multivariate analysis identified osteoporosis (OR = 4.186, 95% CI: 1.654-11.421), spondylolisthesis (OR = 8.199, 95% CI: 3.191-22.937), Modic type I change (OR = 8.279, 95% CI: 3.135-23.891), PI-LL (OR = 1.306, 95% CI: 1.192-1.453), and preoperative hemoglobin level (OR = 0.822, 95% CI: 0.753-0.888) as independent risk factors for CM. A nomogram incorporating these five variables showed good discrimination (AUC=0.874, 95% CI: 0.813-0.935) and calibration (Hosmer-Lemeshow test, χ ² = 7.530, P = 0.481). DCA demonstrated a favorable net benefit across a threshold probability range of 0.05 to 0.81.

The developed nomogram model may assist clinicians in preoperative risk stratification and in formulating personalized surgical and perioperative strategies.