Pulmonary TuberculosisTuberculosis (TB)Tuberculosis Active
Study summary
The goal of this observational study is to establish and validate a comprehensive AI-driven clinical decision support system (AI-CDSS) in whole-chain management for pulmonary tuberculosis (TB) patients. The main question it aims to answer is:
How is the predictive performance of this system in terms of multiple key links during TB diagnosis and treatment? Can real-world benefits be derived from this system? This AI framework supports clinicians in making smarter decisions, ultimately improving cure rates and ensuring that every patient receives the most effective, personalized care possible.
Eligibility
Sex
ALL
Min age
—
Max age
—
Healthy volunteers
No
Inclusion Criteria for Model Development Cohort:
* Patient with clinically diagnosed or bacteriologically confirmed pulmonary tuberculosis (TB) who received TB treatment;
* Initiation of TB treatment on or after January 1, 2021;
* Complete key diagnosis and treatment data available in the electronic medical record system.
Inclusion Criteria for External Validation Cohort:
* Patient with clinically diagnosed or bacteriologically confirmed pulmonary tuberculosis (TB) who is planning to start TB treatment;
* Voluntary participation with signed informed consent form (for adults ≥18 years); parental / guardian consent and co-signed informed consent form are required for minors aged ≤ 18 years.
Exclusion Criteria:
* Co-morbidity confounding: the presence of other active, life-threatening disease (e.g. late-stage malignancy, non-HIV severe immunodeficiency) for which the expected survival or priority of treatment may substantially interfere with the attribution of TB treatment outcomes;
* Extremely poor treatment adherence: documented evidence indicating that the patient either never initiated treatment or was permanently lost to follow-up within the early treatment period (\<2 weeks), precluding the collection of any valid outcome data.
Primary outcome measure(s)
Predictive Performance of the "Easy-to-Treat" versus "Hard-to-Treat" stratification model for pulmonary tuberculosis (PTB) — from treatment initiation to 6 months post treatment The Area Under the Receiver Operating Characteristic curve (AUROC) of the model for discriminating between PTB patients classified as "Easy-to-Treat" versus "Hard-to-Treat".
"Easy-to-Treat" patients are defined as patients with PTB who can achieve favorable outcome when treated with a short-course regimen (≤4 months for drug-sensitive TB, ≤6 months for rifampin-resistant TB).
"Hard-to-Treat" patients are defined as patients with PTB who will experience unfavorable outcome on short-course treatment (≤4 months for drug-sensitive TB, ≤6 months for rifampin-resistant TB).
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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