Study on Accurate Diagnosis of Pathogens in Severe Pneumonia Based on Artificial Intelligence-Driven Technology
Trial Snapshot
- Phase
- Not Applicable
- Status
- Active, not recruiting
- Sponsor
- Enrollment
- 1,000
- Locations
- 6
- Primary Endpoint
- Accuracy of the AI model for the etiological diagnosis of severe pneumonia
Study Overview
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.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts 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.
Arms & Interventions
Severe pneumonia cohort
Plans to enroll approximately 1000 adult patients meeting the diagnostic criteria for severe pneumonia.
Outcomes
Primary Outcomes
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 .
Secondary Outcomes
- Clinical and etiological differences between community-acquired pneumonia (CAP) and hospital-acquired pneumonia (HAP)(From baseline (Day 0) through Day 7 after enrollment)
- Exploration of triggering conditions for HAP and development of a predictive model(From baseline (Day 0) to Day 28 after enrollment.)
- Association between pathogen spectrum characteristics and host immune microenvironment in severe pneumonia.(From baseline (Day 0) to Day 28 after enrollment.)
- Association between dynamic evolution of immune subtypes and prognosis(From baseline (Day 0) through Day 28 after enrollment.)
- Development of a 28-day mortality prediction model based on multimodal AI fusion.(28 days after enrollment.)
Investigators
Zhenhui Zhang
professor
Guangzhou Medical University