Development and validation of a nomogram for predicting postoperative bone nonunion in spinal tuberculosis patients

Area of Science:

• Orthopedics
• Infectious Diseases
• Surgical Oncology

Background:

• Postoperative bone nonunion is a significant complication following instrumented fusion for spinal tuberculosis.
• Preoperative prediction is crucial for preventing this complication, but current tools lack validation in infected patient cohorts.

Purpose of the Study:

• To develop and validate a multivariate nomogram for individualized preoperative risk estimation of nonunion in spinal tuberculosis patients.
• To incorporate key clinical and radiological predictors to guide preventative strategies.

Main Methods:

• A retrospective cohort of 178 spinal tuberculosis patients undergoing debridement and instrumented fusion was analyzed.
• Predictive features were identified using LASSO regression and integrated into a multivariate logistic regression model to construct a nomogram.
• Model performance was evaluated using calibration curves, AUC, and decision curve analysis (DCA).

Main Results:

• The nomogram incorporated five predictors: ALB, CRP normalization days, bone graft materials, psoas abscess, and jumping lesions.
• The nomogram demonstrated strong discrimination (AUC = 0.947) and excellent calibration, with significant clinical utility indicated by DCA.
• Calibration curves showed optimal concordance between predicted and observed outcomes.

Conclusions:

• A precise and effective nomogram was developed for identifying postoperative bone nonunion risk in spinal tuberculosis patients.
• This tool aids in early detection and prevention, thereby improving clinical decision-making and treatment optimization.