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Purpose <p>This study aimed to identify risk factors associated with Gram-positive (G+) cocci infection in spontaneous spinal infection (SSI) and to develop a practical diagnostic nomogram to support early pathogen-oriented risk stratification before microbiological confirmation.</p> Methods <p>A retrospective cohort study was conducted on 223 patients with SSI. To ensure robust feature selection while minimizing overfitting, candidate predictors identified from preliminary screening were entered into a LASSO logistic regression model for further selection. The selected predictors were then incorporated into a multivariate logistic regression model to construct the diagnostic nomogram. Model discrimination and calibration were evaluated using the area under the receiver operating characteristic curve (AUC) and calibration curves, respectively. Internal validation was performed via bootstrap resampling (500 iterations). Clinical utility was further assessed using decision curve analysis (DCA) and the clinical impact curve (CIC).</p> Results <p>Patients were classified into G+ (<i>n</i> = 101) and non-G+ (<i>n</i> = 122) groups. Fever, history of spinal surgery, C-reactive protein (CRP), albumin (ALB), and the degree of intervertebral space height loss were identified as five independent predictors of G+ infection. The resulting nomogram demonstrated excellent discrimination and good calibration, achieving an AUC of 0.884 (95% CI: 0.841–0.926). DCA and CIC suggested potential clinical value of the model within the present cohort, showing a favorable net benefit across a range of threshold probabilities.</p> Conclusion <p>The proposed nomogram provides a potentially reliable tool for predicting G+ cocci infection in patients with SSI. By integrating clinical biomarkers and radiologic features, the model enables early prediction of G+ infection and may support preliminary pathogen-oriented risk stratification before microbiological confirmation.</p>

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Risk factors and a diagnostic nomogram for early prediction of gram-positive coccal etiology in spontaneous spinal infection

  • 期刊论文

  • 发表日期：

    2026年5月27日

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  • Changshun Huang,
  • Xi Chen,
  • Shouxiang Kuang,
  • Rongpan Dang,
  • Yang Li,
  • Guodong Wang,
  • Jianmin Sun,
  • Fengge Zhou,
  • Hongdong Tan,
  • Chenggui Zhang

摘要

Purpose

This study aimed to identify risk factors associated with Gram-positive (G+) cocci infection in spontaneous spinal infection (SSI) and to develop a practical diagnostic nomogram to support early pathogen-oriented risk stratification before microbiological confirmation.

Methods

A retrospective cohort study was conducted on 223 patients with SSI. To ensure robust feature selection while minimizing overfitting, candidate predictors identified from preliminary screening were entered into a LASSO logistic regression model for further selection. The selected predictors were then incorporated into a multivariate logistic regression model to construct the diagnostic nomogram. Model discrimination and calibration were evaluated using the area under the receiver operating characteristic curve (AUC) and calibration curves, respectively. Internal validation was performed via bootstrap resampling (500 iterations). Clinical utility was further assessed using decision curve analysis (DCA) and the clinical impact curve (CIC).

Results

Patients were classified into G+ (n = 101) and non-G+ (n = 122) groups. Fever, history of spinal surgery, C-reactive protein (CRP), albumin (ALB), and the degree of intervertebral space height loss were identified as five independent predictors of G+ infection. The resulting nomogram demonstrated excellent discrimination and good calibration, achieving an AUC of 0.884 (95% CI: 0.841–0.926). DCA and CIC suggested potential clinical value of the model within the present cohort, showing a favorable net benefit across a range of threshold probabilities.

Conclusion

The proposed nomogram provides a potentially reliable tool for predicting G+ cocci infection in patients with SSI. By integrating clinical biomarkers and radiologic features, the model enables early prediction of G+ infection and may support preliminary pathogen-oriented risk stratification before microbiological confirmation.