Segment-specific versus global cervical paraspinal muscle degeneration predicts sagittal balance and functional recovery after posterior surgery

STUDY DESIGN: Retrospective cohort study. OBJECTIVE: To compare the prognostic value of localized C4 fatty infiltration (C4_FI) versus a composite global fatty infiltration index (PC1) for cervical sagittal parameters and functional recovery following posterior cervical surgery. Paraspinal muscle degeneration impairs cervical lordosis and postoperative outcomes. Although global fatty infiltration (FI) is recognized as a predictor of malalignment, the clinical relevance of level-specific FI remains unclear. METHODS: We retrospectively analyzed 179 patients with cervical spondylotic myelopathy treated by laminoplasty from 2017-2025. Preoperative JOA scores, disease duration, surgical procedure, and follow-up duration were recorded. Preoperative radiographs assessed C2-7 lordosis (CL), sagittal vertical axis (SVA), and T1 slope. MRI quantified FI from C3-C6; PC1 represented global FI. Individual-level FI (C3-C6) was compared using AIC to identify the optimal predictive segment. Stepwise and multivariable regressions (adjusted for JOA baseline and disease duration) identified independent predictors. Incremental model value was quantified by ΔAUC, AIC, and BIC with DeLong tests. RESULTS: FI values were strongly correlated (r = 0.53-0.77). PCA identified PC1 accounting for 77.5% variance, most heavily weighted on C4_FI (loading 0.532). Among individual levels, C4_FI showed the lowest AIC for all three sagittal outcomes, supporting C4_FI as the most informative single-level FI marker among C3-C6 in this dataset. Multivariable regression showed C4_FI independently predicted CL (p = 0.015) and T1 slope (p = 0.035), while PC1 was linked to SVA. For functional recovery, C4_FI (OR = 1.12 per 1% FI, p < 0.001) was the strongest predictor. Adding FI to the clinical+sagittal model improved AUC from 0.739 to 0.808 (ΔAUC = +0.069, DeLong p < 0.001). Traditional sagittal parameters (CL, SVA, T1 slope) showed limited independent predictive value for functional outcomes. CONCLUSION: C4_FI and PC1 provide complementary but non-additive information: localized C4 degeneration was more closely associated with lordosis and T1 slope, whereas global FI appeared more related to anterior head translation.Both demonstrated stronger predictive value for functional recovery than conventional sagittal parameters. Routine FI assessment may refine risk stratification and surgical decision-making in cervical myelopathy.

Authors

Institutions

Publication Details

Journal
BMC Musculoskeletal Disorders
Published
2026-05-30
DOI
https://doi.org/10.1186/s12891-026-10005-w
Primary Topic
Cervical and Thoracic Myelopathy
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Segment-specific versus global cervical paraspinal muscle degeneration predicts sagittal balance and functional recovery after posterior surgery

Xinglin Liu, Zhiheng Zhao, Sanbao Hu, Sheyang Xu et al.
BMC Musculoskeletal Disorders
Cervical and Thoracic Myelopathy
article

Segment-specific versus global cervical paraspinal muscle degeneration predicts sagittal balance and functional recovery after posterior surgery

Xinglin Liu, Zhiheng Zhao, Sanbao Hu, Sheyang Xu, Xianglong Meng
article en

Abstract

STUDY DESIGN: Retrospective cohort study. OBJECTIVE: To compare the prognostic value of localized C4 fatty infiltration (C4_FI) versus a composite global fatty infiltration index (PC1) for cervical sagittal parameters and functional recovery following posterior cervical surgery. Paraspinal muscle degeneration impairs cervical lordosis and postoperative outcomes. Although global fatty infiltration (FI) is recognized as a predictor of malalignment, the clinical relevance of level-specific FI remains unclear. METHODS: We retrospectively analyzed 179 patients with cervical spondylotic myelopathy treated by laminoplasty from 2017-2025. Preoperative JOA scores, disease duration, surgical procedure, and follow-up duration were recorded. Preoperative radiographs assessed C2-7 lordosis (CL), sagittal vertical axis (SVA), and T1 slope. MRI quantified FI from C3-C6; PC1 represented global FI. Individual-level FI (C3-C6) was compared using AIC to identify the optimal predictive segment. Stepwise and multivariable regressions (adjusted for JOA baseline and disease duration) identified independent predictors. Incremental model value was quantified by ΔAUC, AIC, and BIC with DeLong tests. RESULTS: FI values were strongly correlated (r = 0.53-0.77). PCA identified PC1 accounting for 77.5% variance, most heavily weighted on C4_FI (loading 0.532). Among individual levels, C4_FI showed the lowest AIC for all three sagittal outcomes, supporting C4_FI as the most informative single-level FI marker among C3-C6 in this dataset. Multivariable regression showed C4_FI independently predicted CL (p = 0.015) and T1 slope (p = 0.035), while PC1 was linked to SVA. For functional recovery, C4_FI (OR = 1.12 per 1% FI, p < 0.001) was the strongest predictor. Adding FI to the clinical+sagittal model improved AUC from 0.739 to 0.808 (ΔAUC = +0.069, DeLong p < 0.001). Traditional sagittal parameters (CL, SVA, T1 slope) showed limited independent predictive value for functional outcomes. CONCLUSION: C4_FI and PC1 provide complementary but non-additive information: localized C4 degeneration was more closely associated with lordosis and T1 slope, whereas global FI appeared more related to anterior head translation.Both demonstrated stronger predictive value for functional recovery than conventional sagittal parameters. Routine FI assessment may refine risk stratification and surgical decision-making in cervical myelopathy.

BMC Musculoskeletal Disorders
Beijing Anzhen Hospital (CN)
National Natural Science Foundation of China
Zero hunger
Openalex Percentile: Top 5%
Cervical and Thoracic Myelopathy
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.