Severe Pneumonia
Conditions
Keywords
Severe Pneumonia, Etiological diagnosis, Immunophenotyping
Brief 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.
Interventions
None listed
Sponsors
Guangzhou Medical University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy of the AI model for the etiological diagnosis of severe pneumonia | 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 | 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 . |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Clinical and etiological differences between community-acquired pneumonia (CAP) and hospital-acquired pneumonia (HAP) | From baseline (Day 0) through Day 7 after enrollment | 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. |
| Exploration of triggering conditions for HAP and development of a predictive model | From baseline (Day 0) to Day 28 after enrollment. | 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. |
| Association between pathogen spectrum characteristics and host immune microenvironment in severe pneumonia. | From baseline (Day 0) to Day 28 after enrollment. | 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. |
| Association between dynamic evolution of immune subtypes and prognosis | From baseline (Day 0) through Day 28 after enrollment. | 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. |
| Development of a 28-day mortality prediction model based on multimodal AI fusion. | 28 days after enrollment. | 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. |
Countries
China
Contacts
PRINCIPAL_INVESTIGATORZhen-hui Zhang, PhD
Second Affiliated Hospital of Guangzhou Medical University
STUDY_CHAIRZi-feng Yang
State Key Laboratory of Respiratory Disease
Outcome results
None listed