Artificial Intelligence-driven Tuberculosis Landscape Analysis & Stratification Research
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
AgeNot listed
SponsorHuashan Hospital
About this trial
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 criteria
This trial does not accept healthy volunteersQualifiers
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.
Patient with clinically diagnosed or bacteriologically confirmed pulmonary tuberculosis (TB) who is planning to start TB treatment;
Disqualifiers
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.
Trial population
Pulmonary tuberculosis (TB) patients diagnosed and treated (or will treat) in several TB clinical centers from China
Trial design
Design model
Cohort
Time perspective
Other
Treatments tested in this trial
Not listed
Trial groups
31,600 Participants
are grouped into 2 trial groupsGroup A: Model Development Cohort
Group B: External Validation Cohort
Trial outcomes
Primary outcomes
1
Predictive Performance of the "Easy-to-Treat" versus "Hard-to-Treat" stratification model for pulmonary tuberculosis (PTB)
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).
Time frame
from treatment initiation to 6 months post treatmentSecondary outcomes
1
Brier Score of the "Easy-to-treat" versus "Hard-to-treat" Model
The Brier score will be used to assess the overall prediction accuracy and reliability of the model. It measures the mean squared difference between the predicted probabilities and the actual observed outcomes. The score ranges from 0 to 1, where 0 represents perfect accuracy and 1 represents total inaccuracy. Lower scores mean better model performance, indicating a better predictive outcome.
Time frame
6 months post-treatment2
Calibration Slope of the "Easy-to-treat" versus "Hard-to-treat" Model
The calibration slope will be calculated to evaluate the agreement between the model's predicted probabilities and the actual observed outcomes. An ideal calibration slope value is 1. Values closer to 1 indicate better calibration performance, meaning the predicted probabilities perfectly reflect the true risk, which indicates a better predictive outcome.
Time frame
6 months post-treatment3
Area Under the Receiver Operating Characteristic (AUROC) Curve of the Pre-Drug Susceptibility Testing (Pre-DST) Drug Resistance Predictive Model
The AUROC curve will be used to evaluate the discrimination performance of the pre-DST (Drug Susceptibility Testing) drug resistance predictive model. The score ranges from 0 to 1, where 0.5 indicates random guessing and 1 represents perfect discrimination. Higher scores mean better discrimination performance, indicating a better predictive outcome.
Time frame
6 months post-treatment4
Area Under the Precision-Recall Curve (AUPRC) of the Pre-Drug Susceptibility Testing (Pre-DST) Drug Resistance Predictive Model
The AUPRC will be used to evaluate the prediction performance of the pre-drug susceptibility testing (Pre-DST) drug resistance predictive model, particularly under conditions of data imbalance. The score ranges from 0 to 1. Higher scores mean better precision and recall performance, indicating a better predictive outcome.
Time frame
6 months post-treatmentSponsors and contacts
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Huashan Hospital
Lead sponsor
The Hong Kong Polytechnic University
Collaborator
Source ClinicalTrials.gov
This trial is not recruiting at the moment. You can still explore other options: