Development and Validation of a Nomogram for Predicting Recurrence in Patients Previously Treated Conservatively for Cervical Spondylosis.

BACKGROUND

Cervical spondylosis frequently recurs after conservative treatment, but its predictors remain unclear. This study aimed to identify factors associated with recurrence and develop a predictive nomogram.

METHODS

This retrospective analysis used data from a multicenter cross-sectional survey (six Chinese cities, March-November 2025). We identified 668 patients who reported remission after systematic conservative treatment and retrospectively collected their post-treatment recurrence status via self-report. Potential predictors included demographics, lifestyle, clinical history, and radiographic findings. The sam ple was randomly split into training (70%) and validation (30%) sets. Multivariable logistic regression identified independent predictors, and a nomogram was constructed. Model performance was evaluated using AUC, calibration, and decision curve analysis. Supplementary analyses examined episode frequency and time to recurrence.

RESULTS

Six independent predictors were identified: exercise duration per session (OR=0.992), neck length (OR=0.850), disc space narrowing (OR=0.478), disease duration (overall P<0.001), pre-treatment attack frequency (overall P<0.001), and PCS score (OR=0.943). Compared with disease duration <3 months, 3-11 months increased risk (OR=2.691); pre-treatment attacks ≥6/month raised risk (OR=4.608). The nomogram showed good discrimination (training AUC=0.760) and net benefit. Longer exercise was associated with delayed recurrence, while longer disease duration and frequent prior episodes increased recurrence risk; disc narrowing paradoxically predicted lower recurrence.

CONCLUSION

Post-treatment recurrence is associated with multifactorial factors, including modifiable elements like exercise and physical health, as well as disease chronicity. Imaging changes may prompt protective behaviors. These findings support individualized risk stratification, but causal inference requires prospective validation.