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.

Trial Health

On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment

1,000

participants targeted

Target at P75+ for all trials

Timeline

20mo left

Started Sep 2025

Typical duration for all trials

Geographic Reach

1 country

6 active sites

Status

active not recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

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Study Timeline

Key milestones and dates

2.3 years study duration

Study Progress30%

Sep 2025Dec 2027

Study Start

First participant enrolled

September 18, 2025

Completed

6 months until next milestone

First Submitted

Initial submission to the registry

March 5, 2026

Completed

5 days until next milestone

First Posted

Study publicly available on registry

March 10, 2026

Completed

10 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

December 31, 2026

Expected

1 year until next milestone

Study Completion

Last participant's last visit for all outcomes

December 31, 2027

Last Updated

March 12, 2026

Status Verified

March 1, 2026

Enrollment Period

1.3 years

First QC Date

March 5, 2026

Last Update Submit

March 10, 2026

Conditions

Severe Pneumonia

Keywords

Severe PneumoniaEtiological diagnosisImmunophenotyping

Outcome Measures

Primary Outcomes (2)

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.
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 .
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)
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.

Other Outcomes (1)

Exploration of immune subtype-specific biomarkers and their diagnostic efficacy
From baseline (Day 0) through Day 7 after enrollment.

Study Arms (1)

Severe pneumonia cohort

Plans to enroll approximately 1000 adult patients meeting the diagnostic criteria for severe pneumonia.

Eligibility Criteria

Age18 Years+

Sexall

Healthy VolunteersNo

Age GroupsAdult (18-64), Older Adult (65+)

Sampling MethodNon-Probability Sample

Study Population

Adult patients admitted to the ICU with a diagnosis of severe pneumonia who meet the specified inclusion and exclusion criteria.

You may qualify if:

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.

You may not qualify if:

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.

Contact the study team to confirm eligibility.