Study on Accurate Diagnosis of Pathogens in Severe Pneumonia Based on Artificial Intelligence-Driven Technology
Trial ID: NCT07461714
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-09-18
Completion date: 2027-12-31
Sponsor: Guangzhou Medical University
Conditions: Severe Pneumonia
Locations: Guangzhou First People's Hospital; the Affiliated Panyu Central Hospital of Guangzhou Medical University; the Fourth Affiliated Hospital of Guangzhou Medical University; the Guangzhou Red Cross Hospital; the Second Affiliated Hospital of Guangzhou Medical University; the Third Affiliated Hospital of Guangzhou Medical University
Countries: China; China; China; China; China; China
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

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. 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.

Eligibility criteria:
Inclusion Criteria:

* 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.

Exclusion Criteria:

* 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.

Outcomes:
Accuracy of the AI model for the etiological diagnosis of severe pneumonia; Identification and characterization of immune subtypes in severe pneumonia