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Updated: Jan 7, 2026
An Automated Culture System for Use in Preclinical Testing of Host-Directed Therapies for Tuberculosis
Published on: August 16, 2021
Machine learning-guided clinical pharmacist interventions improve treatment outcomes in tuberculosis patients: a
Dang Yi1, Huanqing Liu2, Qian Lei3
1Department of Clinical Research, Xianyang Hospital of Yan'an University, Xianyang, Shaanxi, China.
Background:
The heterogeneity in tuberculosis (TB) treatment responses necessitates a precision medicine approach. This study employed machine learning techniques to identify patient subtypes and optimize clinical pharmacist interventions.
Methods:
We conducted a prospective cohort study involving 467 TB patients (218 in the intervention group receiving machine learning-guided pharmacist care and 249 in the control group receiving standard care). Primary outcomes included time to sputum conversion (smear, culture, TB-RNA) and duration of hospitalization; secondary outcomes encompassed adverse event rates (hepatotoxicity, renal impairment, etc.), cost-effectiveness, and biomarker dynamics. Patient stratification was performed using unsupervised learning (k-means/PCA) on clinical and laboratory parameters. Treatment outcomes were assessed via Kaplan-Meier survival analysis and Cox proportional hazards modeling, with prespecified subgroup analyses by risk clusters. Post hoc analyses (e.g., correlation heatmaps of biomarkers) were explicitly labeled as exploratory. Cost-effectiveness was evaluated using incremental cost per quality-adjusted hospital day saved (ICER).
Results:
Machine learning identified 2 distinct patient subtypes (inflammatory vs. immunologic profiles). The intervention group showed significantly shorter hospital stays (primary outcome: median 49.0 vs. 57.0 days; log-rank p = 0.040). Adverse event rates were lower in the intervention group (26.1% vs. 27.7%). Cost analysis demonstrated potential savings of 5,000 CNY per patient in the intervention group. Limitations: Single-center design and modest sample size may limit generalizability. Unmeasured confounders (e.g., socioeconomic factors) could influence outcomes. Post hoc biomarker correlations require validation in independent cohorts.
Conclusion:
Machine learning-guided pharmacist interventions improved TB treatment outcomes and reduced costs. Future multicenter studies should validate subtype-specific benefits.
Clinical Trial Registration:
https://www.chictr.org.cn/ identifier ChiCTR2300074328.
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