Risk Prediction and Management for Central Nervous System Infection After Resection for Gliomas—The 8-Year Experience from a Tertiary Medical Center
2024

Risk Prediction and Management for CNS Infection After Glioma Surgery

Sample size: 337 publication 10 minutes Evidence: moderate

Author Information

Author(s): Zhang Xin, Zheng Zhiyao, Guo Xiaopeng, Wang Hai, Gong Le, Wang Yu, Guo Fuping, Ma Wenbin

Primary Institution: Peking Union Medical College Hospital, Chinese Academy of Medical Sciences

Hypothesis

What are the risk factors for central nervous system infection (CNSI) following glioma resection?

Conclusion

Key risk factors for CNSI were identified, and an effective predictive model was established, providing important references for clinical decision-making and CNSI management.

Supporting Evidence

  • CNSI incidence was 14.9%.
  • Independent risk factors included ventricular opening, postoperative systemic infection, maximum diameter ≥ 5 cm, and preoperative peripheral blood monocyte percentage ≥ 10%.
  • The predictive model showed good performance (C statistic = 0.797, AUC = 0.731).
  • CNSI had no significant impact on long-term prognosis.

Takeaway

This study looked at patients who had brain surgery for tumors and found out what makes them more likely to get infections afterward. They created a tool to help doctors predict who might get sick.

Methodology

Retrospective analysis of 435 glioma resection cases to assess CNSI risk factors and develop a predictive model.

Potential Biases

The study did not obtain informed consent from all patients, which may introduce bias.

Limitations

This study is a single-center retrospective analysis and only 107 patients had available survival data for survival analysis, which may limit the generalizability and statistical power of the results.

Participant Demographics

Among the 337 patients, 43.3% were female, with a median age of 47 years.

Statistical Information

P-Value

p<0.01

Confidence Interval

1.01–1.41

Statistical Significance

p<0.05

Digital Object Identifier (DOI)

10.3390/jcm13247733

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