Evidence map›Paper›PMID 40678722›Full record

ArticleFrontiers in pharmacology2025

Sedative exposure and mortality in intracranial hypertensive tuberculous meningitis: a cohort study with propensity-score matching and machine learning analysis.

Shijuan Cui, Fazheng Shen, Jianing Liang, Fan Li, Xiangyang Wang, Xin Liu, Haigang Chang

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Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Shijuan CuiDepartment of Tuberculosis Internal Medicine, The First Affiliated Hospital of Xinxiang Medical University, Weihui, Henan, China.
Fazheng ShenDepartment of Neurosurgery, The First Affiliated Hospital of Xinxiang Medical University, Weihui, Henan, China.
Jianing LiangDepartment of Neurosurgery, The First Affiliated Hospital of Xinxiang Medical University, Weihui, Henan, China.
Fan LiDepartment of Neurosurgery, The First Affiliated Hospital of Xinxiang Medical University, Weihui, Henan, China.
Xiangyang WangDepartment of Neurosurgery, The First Affiliated Hospital of Xinxiang Medical University, Weihui, Henan, China.
Xin LiuDepartment of Intensive Care Medicine, The First Affiliated Hospital of Changsha Medical University, Changsha, Hunan, China.
Haigang ChangDepartment of Neurosurgery, The First Affiliated Hospital of Xinxiang Medical University, Weihui, Henan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Tuberculous meningitis (TBM) complicated by intracranial hypertension requires aggressive neurocritical care, yet the mortality impact of sedative and antipsychotic exposure remains controversial. This study investigates the association between sedative exposure and mortality while identifying modifiable risk factors in this vulnerable population. Methods: In this retrospective cohort study, we analyzed 1,875 intracranial hypertensive TBM patients from the MIMIC-IV database (v2.0). Exposure was stratified by cumulative sedative days (>3 vs. ≤3). Primary outcomes included 200-day mortality assessed using multivariable logistic regression and Cox proportional hazards models. Propensity score matching (PSM) was performed to adjust for confounding, and machine learning (XGBoost) was used to predict mortality and evaluate feature importance. Results: Unadjusted analyses identified age (odds ratio [OR] = 1.03 per year; 95% confidence interval [CI]: 1.01-1.05), sedative duration (OR = 1.13 per day; 95%CI: 1.04-1.22), and hospital length of stay (LOS; OR = 1.02 per day; 95%CI: 1.00-1.03) as significant mortality predictors. In the PSM cohort (n = 160 matched pairs), crude mortality rates were 16% in sedated versus 2.6% in non-sedated patients (p < 0.001), though the adjusted hazard ratio was non-significant (hazard ratio [HR] = 1.12; 95%CI: 0.83-1.50). Survival curves showed 200-day survival rates of 82% (95%CI: 79%-85%) for non-sedated and 47% (95%CI: 39%-55%) for sedated patients. The XGBoost model achieved an AUC-ROC of 0.79, identifying gender (SHAP value = 0.41), age (0.38), and LOS (0.29) as top predictors of mortality. Conclusion: Prolonged sedation (>3 days) is associated with substantially reduced survival in intracranial hypertensive TBM, potentially reflecting both underlying disease severity and iatrogenic effects. Although residual confounding remains, machine learning analysis highlights the critical influence of gender and LOS on outcomes. These findings demonstrate the need for randomized trials evaluating targeted sedation minimization strategies to improve neurotuberculosis care.

Indexed as

intracranial hypertensionmachine learningmortalitysedative exposuretuberculous meningitis

Identifiers

PMID40678722
PMCPMC12267014

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.