Comparison of the Predictive Value of Different Segmental Bone Quality Assessments for Cage Subsidence Following Anterior Cervical Discectomy and Fusion
Year: 2025
Type: article
Study design: Observational Study
Abstract: OBJECTIVE: To compare the predictive value of different segmental bone quality assessments for cage subsidence following single-level and multilevel anterior cervical discectomy and fusion (ACDF) and determine the best one. METHODS: From 2019 to 2023, we reviewed patients who underwent single- or multilevel ACDF. The segmental endplate bone quality (SEBQ) score, segmental vertebral bone quality (SVBQ) score, and segmental Hounsfield units (SHU) value were measured using preoperative magnetic resonance imaging and computed tomography scans. Cage subsidence was defined as a loss of segment height greater than 3 mm. Risk factors for cage subsidence were identified through logistic regression analysis. The predictive value of the different segmental bone quality assessments for cage subsidence was evaluated using receiver operating characteristic curves. RESULTS: This study included 128 patients, of whom 61 underwent single-level procedures and 67 underwent multilevel procedures. Logistic regression analysis showed that the SEBQ score, SVBQ score, and SHU value were significantly associated with subsidence (P < 0.05). In the single-level group, the area under the curve values for SEBQ score, SVBQ score, and SHU values in predicting subsidence were 0.823, 0.748, and 0.793, respectively. In the multilevel group, the area under the curve values for SEBQ score, SVBQ score, and SHU values predicting subsidence were 0.843, 0.805, and 0.738, respectively. CONCLUSIONS: All segmental bone quality assessments can predict cage subsidence after ACDF. Regardless of whether the procedure is single- or multilevel, the SEBQ score demonstrated superior predictive value compared to the SHU value and SVBQ score.
Source: World Neurosurgery
Authors: Chengkun Zhao, Shijie Wang, Jingjing Zhang, Hegang Niu, Yun Cao, Cailiang Shen, Yinshun Zhang
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,943
Awards: G2007928080, G3264004318
OpenAlex ID: W4414422807
