Exploration of Diagnosis and Treatment Strategies and Prognostic Prediction Models for Acute Respiratory Distress Syndrome Based on Radiographic Evaluations Assessed by Artificial Intelligence

NCT07328997 | Completed DMC

• 1 other identifier: B2024-180
• Study Type: observational
• Target: 400
• Locations: 1 country
• Sites: 1

Brief Summary

By using multi-center chest CT data, an intelligent assessment model for the severity of ARDS was constructed. Based on CT quantitative features and clinical characteristics, a prediction model for short-term critical events (such as mechanical ventilation decisions, prone position strategies, death, ECMO use, etc.) was established. The disease was staged and quantified, and a diagnosis and risk stratification model for ARDS was developed to assist in guiding the diagnosis and treatment strategies for ARDS.

Conditions

AI (Artificial Intelligence); ARDS (Acute Respiratory Distress Syndrome)

Keywords

ARDS; chest CT; AI models

Outcome Measures

Primary Outcomes (3)

• Accuracy of ARDS severity classification

  Accuracy of the artificial intelligence-based model in classifying ARDS severity (mild, moderate, or severe), using the reference clinical classification defined by the 2023 global ARDS criteria as the ground truth.

  Baseline, defined as within 24 hours of index chest CT acquisition during ICU admission.

• Treatment plan matching rate between model-recommended and actual clinical management.

  Concordance rate between model-recommended treatment strategies and actual clinical management decisions across five predefined intervention modalities: mechanical ventilation, high-flow nasal oxygen therapy, non-invasive ventilation, prone positioning, and neuromuscular blockade.

  Baseline, defined as within 24 hours of index chest CT acquisition during ICU admission.

• Accuracy of 28-day in-hospital mortality prediction.

  Accuracy of the model in predicting all-cause in-hospital mortality within 28 days, based on integrated chest CT imaging features and clinical variables.

  Up to 28 days from ICU admission, or until hospital discharge, whichever occurs first.

Secondary Outcomes (4)

• Comparative performance improvement over baseline AI models.

  Baseline for severity classification and treatment plan matching; up to 28 days from ICU admission for mortality prediction

• Calibration performance of 28-day mortality prediction.

  Up to 28 days from ICU admission, or until hospital discharge, whichever occurs first.

• Model interpretability based on imaging and clinical feature contributions.

  Baseline for feature extraction; up to 28 days from ICU admission for outcome association analysis.

• Association between treatment concordance and 28-day in-hospital mortality.

  Up to 28 days from ICU admission, or until hospital discharge, whichever occurs first.

Study Arms (1)

Training group, testing group, validation group

The study adopts a stratified random sampling strategy with an 8:2 split to construct training and internal validation datasets, together with an independent external test cohort from a separate center. No randomization of clinical interventions or treatments is involved. The model will be developed and evaluated using observational data derived from real-world clinical pathways and outcomes, with the objectives of assessing performance in disease severity stratification, treatment recommendation, and mortality prediction. Model performance will be compared with established ICU severity scores and existing AI-based approaches according to a prespecified statistical analysis plan.

Diagnostic Test: CT scan

Interventions

CT scan DIAGNOSTIC_TEST

CT scan

Training group, testing group, validation group

Eligibility Criteria

• Age: 18 Years - 100 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The study population consists of adult patients diagnosed with acute respiratory distress syndrome (ARDS) who were admitted to the intensive care units (ICUs) of three tertiary comprehensive hospitals in China. Eligible participants are retrospectively identified from electronic medical records between January 2020 and December 2024. All included patients meet the predefined inclusion and exclusion criteria based on the 2023 Global ARDS Definition and have available chest CT imaging and corresponding clinical data. This cohort represents a real-world ICU population with diverse etiologies of ARDS and varying degrees of disease severity.

You may qualify if:

• Meets the diagnostic criteria for ARDS
• Be admitted to the intensive care unit
• There are chest CT images

You may not qualify if:

• Age less than 18 years old
• Missing medical records
• No chest CT images

Sponsors & Collaborators

• Shanghai Zhongshan Hospital: lead

Study Sites (1)

Department of critical care medicine, Zhongshan Hospital, Fudan University

Shanghai, Fengling Rd, 200032, P. R., China

Location

Related Publications (30)

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MeSH Terms

Conditions

Acute Lung Injury

Interventions

Tomography, X-Ray Computed

Condition Hierarchy (Ancestors)

Lung Injury; Lung Diseases; Respiratory Tract Diseases

Intervention Hierarchy (Ancestors)

Image Interpretation, Computer-Assisted; Diagnostic Imaging; Diagnostic Techniques and Procedures; Diagnosis; Radiographic Image Enhancement; Image Enhancement; Photography; Radiography; Tomography, X-Ray; Tomography

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 13, 2025
• First Posted: January 9, 2026
• Study Start: May 31, 2024
• Primary Completion: November 30, 2025
• Study Completion: November 30, 2025
• Last Updated: January 9, 2026

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF
• Time Frame: Individual participant data will be available starting 12 months after publication of the primary results and will remain accessible for at least 5 years.
• Access Criteria: Individual participant data will be shared with qualified researchers for scientifically sound proposals. Requests must include a methodologically appropriate analysis plan and an institutional review board (IRB) or ethics committee approval when required. Data will be shared only in de-identified form. All requests will be reviewed by the principal investigator and the study steering committee, who will evaluate the scientific rationale, feasibility, and compliance with data-protection regulations. Upon approval, data will be accessed through a secure, password-protected data-sharing platform.

Locations

CN (1)