Development and Validation of a Novel Diagnostic Nomogram to Differentiate Between Spinal Tuberculosis and Pyogenic...

Area of Science:

• Orthopedics and Infectious Diseases
• Diagnostic Model Development
• Clinical Data Analysis

Background:

• Differentiating spinal tuberculosis (STB) from pyogenic spinal infection (PSI) is a significant diagnostic challenge.
• Misdiagnosis can lead to ineffective treatments, increased patient suffering, and poor outcomes.
• Accurate differentiation is crucial for appropriate clinical management.

Purpose of the Study:

• To develop a practical model using readily available clinical data for distinguishing STB from PSI.
• To create a reliable tool for clinicians to aid in diagnosis.
• To improve patient management through accurate differentiation.

Main Methods:

• Retrospective review of 211 patients with confirmed STB or PSI.
• Collection of clinical data including demographics, laboratory indices (T-SPOT.TB, CRP, ESR, albumin), and imaging findings.
• Development of a nomogram model using univariate and multivariate regression analysis, validated with ROC curves and calibration plots.

Main Results:

• Univariate analysis identified T-SPOT.TB, CRP, ESR, albumin, and imaging findings as significant factors.
• Multifactorial analysis revealed T-SPOT.TB, CRP, ESR, and albumin as strongly associated with STB.
• The developed nomogram demonstrated good performance with an AUC of 0.770 and consistent calibration.

Conclusions:

• The nomogram serves as a reliable, simple, and economical tool for differentiating STB from PSI.
• Accurate and timely distinction facilitates targeted and effective treatment strategies.
• This model can enhance clinical decision-making in spinal infections.