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Objective <p>Community-acquired central nervous system (CNS) infections remain a major cause of morbidity and mortality, particularly in resource-limited settings. Early prognostication is critical but challenged by delays in definitive diagnosis. This study aimed to develop and externally validate a simple prognostic model to support early risk stratification and intervention.</p> Methods <p>We conducted a prospective multicenter cohort study in China (NCT04722328), enrolling 1,060 adults with suspected CNS infections. Patients from four hospitals were included in the training group (<i>n</i> = 742) for model development, while patients from three independent hospitals formed the external validation group (<i>n</i> = 318). Independent predictors were identified using least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression. A nomogram was constructed to estimate individualized risk. Model performance was assessed via area under the receiver operating characteristic curve (AUC), concordance index (C-index), calibration plots, decision curve analysis (DCA), and clinical impact curves (CICs).</p> Results <p>Six predictors were independently associated with poor outcomes: absence of headache, altered consciousness, respiratory failure, hypoproteinemia, low hemoglobin, and hyperglycemia. The model demonstrated strong discrimination in the training group (AUC 0.811; 95% CI 0.774–0.849) and excellent calibration, with DCA indicating clear clinical benefit. External validation confirmed robust performance (AUC 0.855; 95% CI 0.800–0.910), supporting the model’s generalizability.</p> Conclusion <p>We established a prognostic model to support early identification of severe cases and guide timely comprehensive management in adult patients with suspected community-acquired CNS infections.</p> Clinical registration <p>ClinicalTrials.gov (NCT04722328).</p>

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A prognostic model for early risk stratification in adult community-acquired suspected CNS infections: multicenter development and external validation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月17日

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  • Fei Tian,
  • Zhaoyang Liu,
  • Weibi Chen,
  • Lili Cui,
  • Dawei Shan,
  • Huimin Zhang,
  • Shuting Chai,
  • Gang Liu,
  • Linlin Fan,
  • Guofeng Li,
  • Le Yang,
  • Jiatang Zhang,
  • Jiahua Zhao,
  • Fengqin Hou,
  • Jianxin Du,
  • Xinyu Huan,
  • Ying Lv,
  • Xun Huang,
  • Rongrong Zhang,
  • Liyong Wu,
  • Yingfeng Wu,
  • Yan Zhang

摘要

Objective

Community-acquired central nervous system (CNS) infections remain a major cause of morbidity and mortality, particularly in resource-limited settings. Early prognostication is critical but challenged by delays in definitive diagnosis. This study aimed to develop and externally validate a simple prognostic model to support early risk stratification and intervention.

Methods

We conducted a prospective multicenter cohort study in China (NCT04722328), enrolling 1,060 adults with suspected CNS infections. Patients from four hospitals were included in the training group (n = 742) for model development, while patients from three independent hospitals formed the external validation group (n = 318). Independent predictors were identified using least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression. A nomogram was constructed to estimate individualized risk. Model performance was assessed via area under the receiver operating characteristic curve (AUC), concordance index (C-index), calibration plots, decision curve analysis (DCA), and clinical impact curves (CICs).

Results

Six predictors were independently associated with poor outcomes: absence of headache, altered consciousness, respiratory failure, hypoproteinemia, low hemoglobin, and hyperglycemia. The model demonstrated strong discrimination in the training group (AUC 0.811; 95% CI 0.774–0.849) and excellent calibration, with DCA indicating clear clinical benefit. External validation confirmed robust performance (AUC 0.855; 95% CI 0.800–0.910), supporting the model’s generalizability.

Conclusion

We established a prognostic model to support early identification of severe cases and guide timely comprehensive management in adult patients with suspected community-acquired CNS infections.

Clinical registration

ClinicalTrials.gov (NCT04722328).