Constructing a nomogram model for patients with cervical spondylotic myelopathy

Area of Science:

• Neurosurgery
• Orthopedics
• Medical Diagnostics

Background:

• Cervical spondylotic myelopathy (CSM) is a degenerative spinal condition affecting the neck.
• Accurate prognosis prediction is crucial for effective patient management and treatment planning.
• Current diagnostic methods often rely on imaging, but serological markers offer complementary insights.

Purpose of the Study:

• To develop and validate a predictive nomogram for patient prognosis in cervical spondylotic myelopathy (CSM).
• To identify key serological indicators associated with poor prognosis in CSM patients.
• To integrate these indicators into a practical tool for clinical decision-making.

Main Methods:

• Inclusion of 358 patients diagnosed with CSM.
• Collection of admission serological indicators including routine blood tests, biochemical tests, liver and kidney function, and coagulation function.
• Application of statistical analyses: t-test, rank-sum test, chi-square/Fisher test, and univariate/multivariate logistic regression.
• Construction and validation of a predictive nomogram using R language.

Main Results:

• Platelets (PLT), Albumin (ALB), Aspartate aminotransferase (AST), Alanine aminotransferase (ALT), and Fibrinogen (FIB) were identified as independent predictors of prognosis.
• The developed nomogram demonstrated good predictive performance with modest errors in both training and validation cohorts.
• These serological markers may predict the efficacy of Anterior Cervical Discectomy and Fusion (ACDF) in CSM patients.

Conclusions:

• Admission serological indicators like ALB, AST, ALT, FIB, and PLT are valuable predictors for CSM prognosis.
• The constructed nomogram provides a reliable tool for preoperative evaluation and patient stratification.
• Integrating serological data with imaging can enhance diagnostic accuracy and improve postoperative outcomes for CSM patients.