Severe PneumoniaAI-PneumoDxNCT07461714
AI-Driven Accurate Diagnosis of Pathogens in Severe Pneumonia
An observational study of Severe Pneumonia, run by Guangzhou Medical University at 6 sites in China.
Trial summary
Severe pneumonia (SP) is a critical illness characterized by complex etiology, rapid progression, and high mortality. Its precision diagnosis and treatment face two core challenges. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Severe Pneumonia
- How many people
- 1,000 participants
- Where
- 6 sitesChina
- Study dates
- 18 Sep 2025to 31 Dec 2027
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adult patients admitted to the ICU with a diagnosis of severe pneumonia who meet the specified inclusion and exclusion criteria.
Who may be able to join
- Age ≥ 18 years;
- Admitted to the ICU, meeting the diagnostic criteria for severe pneumonia;
- ICU stay > 72 hours;
- The patient or legal representative provides informed consent.
Who cannot join
- Age < 18 years;
- Expected survival time < 1 day;
- Already hospitalized in a general ward for ≥4 weeks or already treated in the ICU for ≥2 weeks;
- Pregnant or lactating women;
- Presence of contraindications to bronchoalveolar lavage;
- Participation in another clinical study or deemed unsuitable by the investigator.
Trial locations and contacts
6 sites in 1 country.
6 sites in 1 country
China6 sites
Guangzhou First People's Hospital
Guangzhou, Guangdong, 510000
the Affiliated Panyu Central Hospital of Guangzhou Medical University
Guangzhou, Guangdong, 510000
the Fourth Affiliated Hospital of Guangzhou Medical University
Guangzhou, Guangdong, 510000
the Guangzhou Red Cross Hospital
Guangzhou, Guangdong, 510000
the Second Affiliated Hospital of Guangzhou Medical University
Guangzhou, Guangdong, 510000
the Third Affiliated Hospital of Guangzhou Medical University
Guangzhou, Guangdong, 510000
About this study
Official title: Study on Accurate Diagnosis of Pathogens in Severe Pneumonia Based on Artificial Intelligence-Driven Technology
Severe pneumonia (SP) is a critical illness characterized by complex etiology, rapid progression, and high mortality. Its precision diagnosis and treatment face two core challenges. First, traditional etiological diagnostic methods (such as culture, serology, PCR) suffer from low detection rates, long turnaround times, and limited pathogen spectrum coverage, making it difficult to meet the clinical need for early, rapid, and precise diagnosis. Even with the application of next-generation sequencing, challenges remain in result interpretation and distinguishing colonization, contamination, and true infection. Second, host immune responses are highly heterogeneous, and there is currently a lack of a subtyping system that can systematically reveal its dynamic evolution and guide precise immunomodulatory therapy. Research on viral severe pneumonia (VSP) indicates that patients exhibit a complex immune imbalance characterized by coexisting hyperactivation of innate immune cells and exhaustion/suppression of adaptive immune cells. Furthermore, this immune heterogeneity may transcend the traditional binary framework, with at least three potential immune subtypes showing significant differences in mortality rates. Therefore, the investigators propose that: By constructing a severe pneumonia cohort and developing an artificial intelligence model that integrates multimodal clinical data (clinical, imaging, microbiological), host multidimensional etiological data (e.g., metagenomic sequencing), and immunomics data (T/B cell immune repertoire, transcriptomics, etc.), it can, on one hand, achieve more accurate and faster etiological diagnosis of severe pneumonia compared to traditional methods; on the other hand, it can identify immune endotypes with distinct immune features, different clinical outcomes, and varied responses to immunomodulatory therapies (e.g., targeting hyperinflammatory or immunosuppressed subtypes). Ultimately, this integrated model system is expected to provide a scientific tool for the individualized treatment and clinical decision-making in severe pneumonia, guiding precise immune intervention to improve patient prognosis.
- Conditions
- Severe Pneumonia
- Keywords
- Severe Pneumonia, Etiological diagnosis, Immunophenotyping
- Medical subject headings
- Pneumonia
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 |
|---|---|---|
| Severe pneumonia cohort | Plans to enroll approximately 1000 adult patients meeting the diagnostic criteria for severe pneumonia. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of the AI model for the etiological diagnosis of severe pneumonia
The primary outcome is the accuracy of the constructed artificial intelligence model in diagnosing the etiology of severe pneumonia. Accuracy is defined as the proportion of correct predictions made by the model out of the total number of samples. It is calculated using the formula: Accuracy = (Number of Correct Predictions) / (Total Number of Samples). The AI model will integrate multimodal data including clinical, imaging, and microbiological features. The diagnostic performance of the model will be compared against a gold standard.
Measured: From baseline (Day 0) to Day 7 after enrollment.
Identification and characterization of immune subtypes in severe pneumonia
The primary outcome is the identification of distinct immune subtypes in patients with severe pneumonia using an artificial intelligence model that integrates multimodal data, including clinical parameters, imaging, and immunomics. The study aims to reveal the dynamic evolution of host immune responses. The model will identify at least 3 potential immune subtypes (such as immune hyperactivation, immunosuppression, and mixed types) with significant differences in clinical outcomes like mortality .
Measured: From baseline (Day 0) to Day 28 after enrollment.
Secondary outcomes (5)
Clinical and etiological differences between community-acquired pneumonia (CAP) and hospital-acquired pneumonia (HAP)
Through the multicenter cohort, the study aims to systematically compare the differences in clinical outcomes, pathogen spectrum, and immune response characteristics between CAP and HAP patients. This comparison will help clarify the distinct clinical features and etiological backgrounds of these two types of pneumonia.
Measured: From baseline (Day 0) through Day 7 after enrollment
Exploration of triggering conditions for HAP and development of a predictive model
The study aims to explore the triggering conditions for hospital-acquired pneumonia (HAP), specifically identifying key predictive indicators for nosocomial and secondary infections, and to establish a predictive model for HAP. This will involve analyzing clinical, microbiological, and host immune data from the multicenter cohort to identify risk factors and early warning signs.
Measured: From baseline (Day 0) to Day 28 after enrollment.
Association between pathogen spectrum characteristics and host immune microenvironment in severe pneumonia.
The study will systematically collect serological tests, microbial culture, PCR, and metagenomic sequencing data to comprehensively characterize the pathogen spectrum of severe pneumonia. By integrating this with host immune indicators (such as lymphocyte subsets, cytokines) and clinical outcomes, the study aims to investigate how different pathogens (e.g., bacteria, viruses, fungi, and mixed infections) specifically drive host immune responses. This outcome seeks to reveal the dynamic association between "pathogen-host immunity-clinical outcome", providing a basis for targeted therapy.
Measured: From baseline (Day 0) to Day 28 after enrollment.
Association between dynamic evolution of immune subtypes and prognosis
This outcome explores the association between the dynamic evolution trajectory of immune subtypes and patient prognosis. Cox proportional hazards models will be used to analyze the independent relationship between subtypes and prognosis.
Measured: From baseline (Day 0) through Day 28 after enrollment.
Development of a 28-day mortality prediction model based on multimodal AI fusion.
The study aims to build an intelligent prognostic prediction model for severe pneumonia by integrating multimodal data, including clinical baseline information, scoring systems (APACHE II, SOFA), imaging features (CT/X-ray), laboratory indicators, and dynamic immune data. This is an exploratory research endpoint to determine if the AI model can predict 28-day all-cause mortality in patients with severe pneumonia.
Measured: 28 days after enrollment.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Mar 2026
- Study start18 Sep 2025
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Severe PneumoniaStudies 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
- Prospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Guangzhou Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Zhenhui Zhang, Guangzhou Medical University
- Investigators
- Zhen-hui Zhang, PhD, Principal investigator, Second Affiliated Hospital of Guangzhou Medical UniversityZi-feng Yang, Study chair, State Key Laboratory of Respiratory Disease
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07461714
- Sponsor's protocol ID
- ICU-2025-001
- Other IDs
- GZNL2024B01008
- Acronym
- AI-PneumoDx
- Status last verified
- Mar 2026
- First submitted
- 5 Mar 2026
- Last updated
- 12 Mar 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: A final decision regarding IPD sharing has not been made. Any future data sharing will comply with ethical guidelines, participant privacy protection, and relevant regulatory requirements.
Study at a glance
From the registry record, last updated by the sponsor on 12 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Study on Accurate Diagnosis of Pathogens in Severe Pneumonia Based on Artificial Intelligence-Driven Technology
- ClinicalTrials.gov ID
- NCT07461714
- Acronym / sponsor ID
- AI-PneumoDx / ICU-2025-001
- Conditions
- Severe Pneumonia
- Keywords
- Severe Pneumonia, Etiological diagnosis, Immunophenotyping
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Guangzhou Medical UniversityUniversity, hospital or other
- Study start
- 18 Sep 2025 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 10 Mar 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.
Severe pneumonia (SP) is a critical illness characterized by complex etiology, rapid progression, and high mortality. Its precision diagnosis and treatment face two core challenges. First, traditional etiological diagnostic methods (such as culture, serology, PCR) suffer from low detection rates, long turnaround times, and limited pathogen spectrum coverage, making it difficult to meet the clinical need for early, rapid, and precise diagnosis. Even with the application of next-generation sequencing, challenges remain in result interpretation and distinguishing colonization, contamination, and true infection. Second, host immune responses are highly heterogeneous, and there is currently a lack of a subtyping system that can systematically reveal its dynamic evolution and guide precise immunomodulatory therapy. Research on viral severe pneumonia (VSP) indicates that patients exhibit a complex immune imbalance characterized by coexisting hyperactivation of innate immune cells and exhaustion/suppression of adaptive immune cells. Furthermore, this immune heterogeneity may transcend the traditional binary framework, with at least three potential immune subtypes showing significant differences in mortality rates. Therefore, the investigators propose that: By constructing a severe pneumonia cohort and developing an artificial intelligence model that integrates multimodal clinical data (clinical, imaging, microbiological), host multidimensional etiological data (e.g., metagenomic sequencing), and immunomics data (T/B cell immune repertoire, transcriptomics, etc.), it can, on one hand, achieve more accurate and faster etiological diagnosis of severe pneumonia compared to traditional methods; on the other hand, it can identify immune endotypes with distinct immune features, different clinical outcomes, and varied responses to immunomodulatory therapies (e.g., targeting hyperinflammatory or immunosuppressed subtypes). Ultimately, this integrated model system is expected to provide a scientific tool for the individualized treatment and clinical decision-making in severe pneumonia, guiding precise immune intervention to improve patient prognosis.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Severe pneumonia cohort | – | –Plans to enroll approximately 1000 adult patients meeting the diagnostic criteria for severe pneumonia. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients admitted to the ICU with a diagnosis of severe pneumonia who meet the specified inclusion and exclusion criteria.
Key inclusion criteria (4 listed)
- Age ≥ 18 years;
- Admitted to the ICU, meeting the diagnostic criteria for severe pneumonia;
- ICU stay > 72 hours;
- The patient or legal representative provides informed consent.
Key exclusion criteria (6 listed)
- Age < 18 years;
- Expected survival time < 1 day;
- Already hospitalized in a general ward for ≥4 weeks or already treated in the ICU for ≥2 weeks;
- Pregnant or lactating women;
- Presence of contraindications to bronchoalveolar lavage;
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
Accuracy of the AI model for the etiological diagnosis of severe pneumonia
Time frame: From baseline (Day 0) to Day 7 after enrollment.
The primary outcome is the accuracy of the constructed artificial intelligence model in diagnosing the etiology of severe pneumonia. Accuracy is defined as the proportion of correct predictions made by the model out of the total number of samples. It is calculated using the formula: Accuracy = (Number of Correct Predictions) / (Total Number of Samples). The AI model will integrate multimodal data including clinical, imaging, and microbiological features. The diagnostic performance of the model will be compared against a gold standard.
Identification and characterization of immune subtypes in severe pneumonia
Time frame: From baseline (Day 0) to Day 28 after enrollment.
The primary outcome is the identification of distinct immune subtypes in patients with severe pneumonia using an artificial intelligence model that integrates multimodal data, including clinical parameters, imaging, and immunomics. The study aims to reveal the dynamic evolution of host immune responses. The model will identify at least 3 potential immune subtypes (such as immune hyperactivation, immunosuppression, and mixed types) with significant differences in clinical outcomes like mortality .
5 secondary outcomes
Clinical and etiological differences between community-acquired pneumonia (CAP) and hospital-acquired pneumonia (HAP)
Time frame: From baseline (Day 0) through Day 7 after enrollment
Exploration of triggering conditions for HAP and development of a predictive model
Time frame: From baseline (Day 0) to Day 28 after enrollment.
Association between pathogen spectrum characteristics and host immune microenvironment in severe pneumonia.
Time frame: From baseline (Day 0) to Day 28 after enrollment.
Association between dynamic evolution of immune subtypes and prognosis
Time frame: From baseline (Day 0) through Day 28 after enrollment.
Development of a 28-day mortality prediction model based on multimodal AI fusion.
Time frame: 28 days after enrollment.
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 12 Mar 2026.
- Individual participant dataSharing planned: Undecided. A final decision regarding IPD sharing has not been made. Any future data sharing will comply with ethical guidelines, participant privacy protection, and relevant regulatory requirements.
Related trials for Severe Pneumonia
RecruitingPhase 3
Randomized, Embedded, Multifactorial Adaptive Platform Trial for Community- Acquired Pneumonia
Respiratory Tract Diseases, Influenza (Pandemic)
RecruitingObservational study
Detection and Characterization of Lower Respiratory Infections in Critically Ill Patients
Severe Pneumonia
RecruitingPhase 4
Opportunistic Pneumococcal Immunisation Trial in MALnutrition
Severe Acute Malnutrition in Childhood, Pneumococcal Disease
Recruiting
A Study on Integrated Chinese and Western Medicine for Severe Pneumonia Caused by Multidrug-Resistant Gram-Negative Bacterial Infections
Severe Pneumonia Caused by Multidrug-resistant Gram-negative Bacterial Infection
Active, not recruitingObservational study
Impact of a High-Protein Diet on Clinical Outcomes in Mechanically Ventilated Patients With Severe Pneumonia
Mechanically Ventilated Patients, Severe Pneumonia
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. This study is not recruiting at the moment.
Zhen-hui Zhang, PhD principal investigator
Second Affiliated Hospital of Guangzhou Medical University
Zi-feng Yang study chair
State Key Laboratory of Respiratory Disease
6 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Severe Pneumonia, run by Guangzhou Medical University at 6 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 4 inclusion and 6 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: under way, not looking for participants, as of the sponsor's last update on 12 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 6 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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of the AI model for the etiological diagnosis of severe pneumonia (time frame: From baseline (Day 0) to Day 7 after enrollment.). The study lists 2 primary outcomes in all. 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 12 Mar 2026; AzTrials last refreshed its copy on 7 Nov 2026.