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OpenTrials
Completed

NCT Number: NCT07328997

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

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.

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Key information

Conditions

ARDS (Acute Respiratory Distress Syndrome) AI (Artificial Intelligence) Acute Lung Injury Lung Diseases Lung Injury Respiratory Tract Diseases

Age range

18 year–100 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Department of critical care medicine, Zhongshan Hospital, Fudan University

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

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

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

Treatment and study plan

CT Scan

Diagnostic Test

CT scan

Primary outcomes

  1. Accuracy of ARDS severity classification

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

    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.

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

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

    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.

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

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

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

Secondary outcomes

  1. Comparative performance improvement over baseline AI models.

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

    Absolute performance improvement of the proposed model compared with three commonly used baseline artificial intelligence models across ARDS severity classification, treatment plan matching, and 28-day mortality prediction.

  2. Calibration performance of 28-day mortality prediction.

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

    Calibration of the mortality prediction model assessed using calibration curves and calibration statistics to evaluate agreement between predicted and observed 28-day in-hospital mortality.

  3. Model interpretability based on imaging and clinical feature contributions.

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

    Quantification of the relative contributions of imaging-derived features and clinical variables to mortality prediction using Shapley Additive Explanations (SHAP). Feature importance will be analyzed overall and stratified by ARDS severity.

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

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

    Association between concordance of model-recommended interventions and actual clinical treatments and 28-day in-hospital mortality, evaluated using multivariable logistic regression adjusted for key imaging-derived structural metrics.

Sponsors and collaborators

Lead sponsor

Shanghai Zhongshan Hospital

Other

Registry information

Important dates

Study start
2024
Primary completion
2025
Study completion
2025
First posted
Jan 9, 2026
Registry last updated
Jan 9, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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