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<p>Tuberculous meningitis (TBM) remains a major cause of central nervous system infection in endemic regions and is frequently complicated by hydrocephalus requiring cerebrospinal fluid (CSF) diversion. Timely surgical decision-making is critical, particularly in resource-limited settings. The objective of this study was to characterize the clinical profile and management of TBM in a tertiary center and retrospectively validate a logistic regression–based model for guiding surgical intervention in TBM-associated hydrocephalus. A retrospective review was conducted on 175 adult patients with TBM admitted to the Philippine General Hospital from January 2021 to May 2025. Clinical, laboratory, and radiologic data were analyzed. The predictive model was applied to determine its diagnostic performance in identifying patients requiring surgical intervention. The mean age of our patients was 37 ± 15 years, with male predominance (<i>n</i> = 107, 61%). Most patients (<i>n</i> = 147, 84%) presented with Modified Vellore grades 3–4, and hydrocephalus was observed in 70% (<i>n</i> = 122). CSF diversion was performed in 30% of cases (<i>n</i> = 53). Overall mortality was 44% (<i>n</i> = 77), primarily due to septic shock and brain herniation. The model demonstrated good discriminative ability (Area Under the Curve 0.84, 95% CI 0.71–0.86). A recalibrated g-score threshold of &gt; 0.20 from the original 0.04 improved diagnostic balance (sensitivity 91.2%, specificity 60.8%, positive predictive value 32%, negative predictive value 96%). Altered sensorium (OR 6.89) and persistent headache (OR 4.26) were the strongest predictors of surgery. The validated model provides a simple and clinically practical framework for guiding surgical decision-making in TBM-associated hydrocephalus, with potential utility in resource-limited settings.</p>

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Guiding neurosurgical intervention in tuberculous meningitis: evaluation of clinical features, laboratory parameters, and outcomes with a treatment algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月22日

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  • Ena Elizabeth L. Naoe,
  • Edmund John B. Cayanong,
  • Safrollah M. Guinal,
  • Kurt Joshua E. Baladjay,
  • John Rex C. Butial,
  • Frances Lois U. Ngo,
  • Juan Silvestre G. Pascual,
  • Kathleen Joy O. Khu,
  • Julette Marie F. Batara,
  • Roland Dominic G. Jamora

摘要

Tuberculous meningitis (TBM) remains a major cause of central nervous system infection in endemic regions and is frequently complicated by hydrocephalus requiring cerebrospinal fluid (CSF) diversion. Timely surgical decision-making is critical, particularly in resource-limited settings. The objective of this study was to characterize the clinical profile and management of TBM in a tertiary center and retrospectively validate a logistic regression–based model for guiding surgical intervention in TBM-associated hydrocephalus. A retrospective review was conducted on 175 adult patients with TBM admitted to the Philippine General Hospital from January 2021 to May 2025. Clinical, laboratory, and radiologic data were analyzed. The predictive model was applied to determine its diagnostic performance in identifying patients requiring surgical intervention. The mean age of our patients was 37 ± 15 years, with male predominance (n = 107, 61%). Most patients (n = 147, 84%) presented with Modified Vellore grades 3–4, and hydrocephalus was observed in 70% (n = 122). CSF diversion was performed in 30% of cases (n = 53). Overall mortality was 44% (n = 77), primarily due to septic shock and brain herniation. The model demonstrated good discriminative ability (Area Under the Curve 0.84, 95% CI 0.71–0.86). A recalibrated g-score threshold of > 0.20 from the original 0.04 improved diagnostic balance (sensitivity 91.2%, specificity 60.8%, positive predictive value 32%, negative predictive value 96%). Altered sensorium (OR 6.89) and persistent headache (OR 4.26) were the strongest predictors of surgery. The validated model provides a simple and clinically practical framework for guiding surgical decision-making in TBM-associated hydrocephalus, with potential utility in resource-limited settings.