Pulmonary TuberculosisTB-ATLASNCT07611695
Artificial Intelligence-driven Tuberculosis Landscape Analysis & Stratification Research
An observational study of Pulmonary Tuberculosis and Tuberculosis (TB), run by Huashan Hospital at 2 sites in China.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Pulmonary Tuberculosis, Tuberculosis (TB), Tuberculosis Active
- How many people
- 31,600 participants
- Where
- 2 sitesChina
- Study dates
- 1 Jun 2026to 30 Jun 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Pulmonary tuberculosis (TB) patients diagnosed and treated (or will treat) in several TB clinical centers from China
Who may be able to join
- 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;
- 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.
Who cannot join
- 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 locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China2 sites
Hunan Chest Hospital
Changsha, Hunan
Qi Wang, +8613787010006, 158723909@qq.com
Huashan Hospital Affiliated to Fudan University
Shanghai, 210000
Yang Li, +86 21-52888123, yang.li@nmcid.org.cn
About this study
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.
Read the detailed description
This study establishes TB-ATLAS (Artificial Intelligence-driven Tuberculosis Landscape Analysis \& Stratification Research), a modular framework for whole-chain TB management. The objective is to develop and validate an umbrella suite of AI-driven models to optimize clinical decision-making from initial diagnosis to post-treatment follow-up.
The core hypothesis is that multimodal patient data can stratify TB phenotypes and predict critical clinical events, enabling precision medicine. Beyond the primary focus on distinguishing Easy-to-Treat (ETT) from Hard-to-Treat (HTT) categories, the system incorporates satellite modules for pre-DST drug resistance risk, treatment adherence monitoring, adverse event (AE) early warning, and risk of post-TB lung disease (PTLD).
This study employs a retrospective-prospective cohort design. By utilizing retrospective IPD from clinical trials and real-world EHRs (>30,000 patients), the investigators apply advanced AI, including foundation models for feature representation and multi-task learning for modular development. Integration of structured clinical variables, microbiological profiles, radiomics, and host signatures ensures high-dimensional input. Model interpretability is prioritized via SHAP/LIME to ensure clinical trust. Then the performance will be evaluated using AUROC and calibration metrics. External validation will occur in a prospective cohort (n≥1,600) to assess the system's impact on predicting real-world outcomes compared to standardized care.
The expected output is the TB-ATLAS Clinical Decision Support System (AI-CDSS). By providing evidence-based guidance on regimen intensity, resistance risk, and relapse monitoring, this platform facilitates the transition from "one-size-fits-all" standardized care towards individualized precision management, significantly enhancing clinical decision-making across diverse healthcare settings.
- Keywords
- tuberculosis, artificial intelligence, predictive model, clinical decision support system
- Medical subject headings
- Tuberculosis, Pulmonary, Tuberculosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Model Development Cohort | Retrospective data used for model fitting and tuning. The training and validating sets are interchangeable due to 10-fold cross validation. | |
| External Validation Cohort | Prospective collected data for external model validation and predictive performance measurement |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: from treatment initiation to 6 months post treatment
Secondary outcomes (20)
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.
Measured: 6 months post-treatment
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.
Measured: 6 months post-treatment
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.
Measured: 6 months post-treatment
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.
Measured: 6 months post-treatment
F1-score of the Secondary Decision Models for Pre-Drug Susceptibility Testing (Pre-DST) Drug Resistance Prediction
The F1-score, calculated as the harmonic mean of precision and recall, will be used to evaluate the classification performance of the secondary decision models for pre-drug susceptibility testing (Pre-DST) drug resistance prediction. The score ranges from 0 to 1. Higher scores mean better classification performance, indicating a better predictive outcome.
Measured: 6 months post-treatment
Area Under the Receiver Operating Characteristic (AUROC) Curve of the Adherence Forecasting Model
The AUROC curve will be used to evaluate the discrimination performance of the adherence forecasting 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.
Measured: From treatment initiation until treatment completion, assessed up to 6 months
Area Under the Precision-Recall Curve (AUPRC) of the Adherence Forecasting Model
The AUPRC will be used to evaluate the prediction performance of the adherence forecasting model under data imbalance. The score ranges from 0 to 1. Higher scores mean better precision and recall performance, indicating a better predictive outcome.
Measured: From treatment initiation until treatment completion, assessed up to 6 months
F1-score of the Secondary Decision Models for Adherence Forecasting
The F1-score, calculated as the harmonic mean of precision and recall, will be used to evaluate the classification performance of the secondary decision models for adherence forecasting. The score ranges from 0 to 1. Higher scores mean better classification performance, indicating a better predictive outcome.
Measured: From treatment initiation until treatment completion, assessed up to 6 months
Area Under the Receiver Operating Characteristic (AUROC) Curve of the Treatment Response Predictive Model
The AUROC curve will be used to evaluate the overall discrimination performance of the treatment response 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.
Measured: 6 months post-treatment
Area Under the Precision-Recall Curve (AUPRC) of the Treatment Response Predictive Model
The AUPRC will be used to evaluate the prediction performance of the treatment response 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.
Measured: 6 months post-treatment
F1-score of the Secondary Decision Models for Treatment Response Prediction
The F1-score, calculated as the harmonic mean of precision and recall, will be used to evaluate the classification performance of the secondary decision models for treatment response prediction. The score ranges from 0 to 1. Higher scores mean better classification performance, indicating a better predictive outcome.
Measured: 6 months post-treatment
Area Under the Receiver Operating Characteristic (AUROC) Curve of the Adverse Event (AE) Predictive Model
The AUROC curve will be used to evaluate the overall discrimination performance of the adverse event 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.
Measured: 6 months post-treatment
Area Under the Precision-Recall Curve (AUPRC) of the Adverse Event (AE) Predictive Model
The AUPRC will be used to evaluate the prediction performance of the adverse event 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.
Measured: 6 months post-treatment
F1-score of the Secondary Decision Models for Adverse Event (AE) Prediction
The F1-score, calculated as the harmonic mean of precision and recall, will be used to evaluate the classification performance of the secondary decision models for adverse event prediction. The score ranges from 0 to 1. Higher scores mean better classification performance, indicating a better predictive outcome.
Measured: 6 months post-treatment
Area Under the Receiver Operating Characteristic (AUROC) Curve of the Relapse Predictive Model
The AUROC curve will be used to evaluate the overall discrimination performance of the relapse predictive model. Relapse is defined per the World Health Organization (WHO) standard. 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.
Measured: 6 months post-treatment
Area Under the Precision-Recall Curve (AUPRC) of the Relapse Predictive Model
The AUPRC will be used to evaluate the prediction performance of the relapse predictive model, particularly under conditions of data imbalance. Relapse is defined per the World Health Organization (WHO) standard. The score ranges from 0 to 1. Higher scores mean better precision and recall performance, indicating a better predictive outcome.
Measured: 6 months post-treatment
F1-score of the Secondary Decision Models for Relapse Prediction
The F1-score, calculated as the harmonic mean of precision and recall, will be used to evaluate the classification performance of the secondary decision models for relapse prediction. Relapse is defined per the World Health Organization (WHO) standard. The score ranges from 0 to 1. Higher scores mean better classification performance, indicating a better predictive outcome.
Measured: 6 months post-treatment
Area Under the Receiver Operating Characteristic (AUROC) Curve of the Post-Tuberculosis (TB) Lung Disease Predictive Model
The AUROC curve will be used to evaluate the overall discrimination performance of the post-tuberculosis (TB) lung disease 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.
Measured: 6 months post-treatment
Area Under the Precision-Recall Curve (AUPRC) of the Post-Tuberculosis (TB) Lung Disease Predictive Model
The AUPRC will be used to evaluate the prediction performance of the post-tuberculosis (TB) lung disease 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.
Measured: 6 months post-treatment
F1-score of the Secondary Decision Models for Post-Tuberculosis (TB) Lung Disease Prediction
The F1-score, calculated as the harmonic mean of precision and recall, will be used to evaluate the classification performance of the secondary decision models for post-tuberculosis (TB) lung disease prediction. The score ranges from 0 to 1. Higher scores mean better classification performance, indicating a better predictive outcome.
Measured: 6 months post-treatment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 May 2026
- Study start1 Jun 2026 (estimated)
- Primary completion31 Dec 2027 (estimated)
- Study completion30 Jun 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pulmonary TuberculosisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 31,600 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Huashan Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- The Hong Kong Polytechnic University
- Responsible party
- Principal investigator, Wen-hong Zhang, Huashan Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07611695
- Sponsor's protocol ID
- KY2025-1517
- Acronym
- TB-ATLAS
- Status last verified
- Apr 2026
- First submitted
- 23 Apr 2026
- Last updated
- 28 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: This study incorporates datasets from multiple previous studies and future cohort. The detailed IPD sharing plan will be discussed per types of data.
Study at a glance
From the registry record, last updated by the sponsor on 28 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-driven Tuberculosis Landscape Analysis & Stratification Research
- ClinicalTrials.gov ID
- NCT07611695
- Acronym / sponsor ID
- TB-ATLAS / KY2025-1517
- Keywords
- tuberculosis, artificial intelligence, predictive model, clinical decision support system
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 31,600 participants (estimated)
- Lead sponsor
- Huashan HospitalUniversity, hospital or other
- Collaborators
- The Hong Kong Polytechnic University
- Study start
- 1 Jun 2026 (estimated)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 30 Jun 2028 (estimated)
- First posted
- 28 May 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
This study establishes TB-ATLAS (Artificial Intelligence-driven Tuberculosis Landscape Analysis \& Stratification Research), a modular framework for whole-chain TB management. The objective is to develop and validate an umbrella suite of AI-driven models to optimize clinical decision-making from initial diagnosis to post-treatment follow-up.
The core hypothesis is that multimodal patient data can stratify TB phenotypes and predict critical clinical events, enabling precision medicine. Beyond the primary focus on distinguishing Easy-to-Treat (ETT) from Hard-to-Treat (HTT) categories, the system incorporates satellite modules for pre-DST drug resistance risk, treatment adherence monitoring, adverse event (AE) early warning, and risk of post-TB lung disease (PTLD).
This study employs a retrospective-prospective cohort design. By utilizing retrospective IPD from clinical trials and real-world EHRs (>30,000 patients), the investigators apply advanced AI, including foundation models for feature representation and multi-task learning for modular development. Integration of structured clinical variables, microbiological profiles, radiomics, and host signatures ensures high-dimensional input. Model interpretability is prioritized via SHAP/LIME to ensure clinical trust. Then the performance will be evaluated using AUROC and calibration metrics. External validation will occur in a prospective cohort (n≥1,600) to assess the system's impact on predicting real-world outcomes compared to standardized care.
The expected output is the TB-ATLAS Clinical Decision Support System (AI-CDSS). By providing evidence-based guidance on regimen intensity, resistance risk, and relapse monitoring, this platform facilitates the transition from "one-size-fits-all" standardized care towards individualized precision management, significantly enhancing clinical decision-making across diverse healthcare settings.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Model Development Cohort | – | –Retrospective data used for model fitting and tuning. The training and validating sets are interchangeable due to 10-fold cross validation. |
| External Validation Cohort | – | –Prospective collected data for external model validation and predictive performance measurement |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Pulmonary tuberculosis (TB) patients diagnosed and treated (or will treat) in several TB clinical centers from China
Key inclusion criteria (5 listed)
- 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;
- 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.
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Predictive Performance of the "Easy-to-Treat" versus "Hard-to-Treat" stratification model for pulmonary tuberculosis (PTB)
Time frame: 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).
20 secondary outcomes
Brier Score of the "Easy-to-treat" versus "Hard-to-treat" Model
Time frame: 6 months post-treatment
Calibration Slope of the "Easy-to-treat" versus "Hard-to-treat" Model
Time frame: 6 months post-treatment
Area Under the Receiver Operating Characteristic (AUROC) Curve of the Pre-Drug Susceptibility Testing (Pre-DST) Drug Resistance Predictive Model
Time frame: 6 months post-treatment
Area Under the Precision-Recall Curve (AUPRC) of the Pre-Drug Susceptibility Testing (Pre-DST) Drug Resistance Predictive Model
Time frame: 6 months post-treatment
F1-score of the Secondary Decision Models for Pre-Drug Susceptibility Testing (Pre-DST) Drug Resistance Prediction
Time frame: 6 months post-treatment
Area Under the Receiver Operating Characteristic (AUROC) Curve of the Adherence Forecasting Model
Time frame: From treatment initiation until treatment completion, assessed up to 6 months
Area Under the Precision-Recall Curve (AUPRC) of the Adherence Forecasting Model
Time frame: From treatment initiation until treatment completion, assessed up to 6 months
F1-score of the Secondary Decision Models for Adherence Forecasting
Time frame: From treatment initiation until treatment completion, assessed up to 6 months
Area Under the Receiver Operating Characteristic (AUROC) Curve of the Treatment Response Predictive Model
Time frame: 6 months post-treatment
Area Under the Precision-Recall Curve (AUPRC) of the Treatment Response Predictive Model
Time frame: 6 months post-treatment
F1-score of the Secondary Decision Models for Treatment Response Prediction
Time frame: 6 months post-treatment
Area Under the Receiver Operating Characteristic (AUROC) Curve of the Adverse Event (AE) Predictive Model
Time frame: 6 months post-treatment
Area Under the Precision-Recall Curve (AUPRC) of the Adverse Event (AE) Predictive Model
Time frame: 6 months post-treatment
F1-score of the Secondary Decision Models for Adverse Event (AE) Prediction
Time frame: 6 months post-treatment
Area Under the Receiver Operating Characteristic (AUROC) Curve of the Relapse Predictive Model
Time frame: 6 months post-treatment
Area Under the Precision-Recall Curve (AUPRC) of the Relapse Predictive Model
Time frame: 6 months post-treatment
F1-score of the Secondary Decision Models for Relapse Prediction
Time frame: 6 months post-treatment
Area Under the Receiver Operating Characteristic (AUROC) Curve of the Post-Tuberculosis (TB) Lung Disease Predictive Model
Time frame: 6 months post-treatment
Area Under the Precision-Recall Curve (AUPRC) of the Post-Tuberculosis (TB) Lung Disease Predictive Model
Time frame: 6 months post-treatment
F1-score of the Secondary Decision Models for Post-Tuberculosis (TB) Lung Disease Prediction
Time frame: 6 months post-treatment
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 May 2026.
- Individual participant dataSharing planned: Undecided. This study incorporates datasets from multiple previous studies and future cohort. The detailed IPD sharing plan will be discussed per types of data.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pulmonary Tuberculosis and Tuberculosis (TB), run by Huashan Hospital at 2 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 28 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Yang Li, MD (021-52888123, yang.li@nmcid.org.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Predictive Performance of the "Easy-to-Treat" versus "Hard-to-Treat" stratification model for pulmonary tuberculosis (PTB) (time frame: from treatment initiation to 6 months post treatment). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 May 2026.