Sedative exposure and mortality in intracranial hypertensive tuberculous meningitis: a cohort study with propensity-score matching and machine learning analysis - Cui, Shijuan; Shen, Fazheng; Liang, Jianing; Li, Fan; Wang, Xiangyang; Liu, Xin; Chang, Haigang (Frontiers in Pharmacology, 2025)
BackgroundTuberculous 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.MethodsIn 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.ResultsUnadjusted 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.ConclusionProlonged 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.