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.
Study Type
OBSERVATIONAL
Enrollment
400
CT scan
Department of critical care medicine, Zhongshan Hospital, Fudan University
Shanghai, Fengling Rd, China
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.
Time frame: 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.
Time frame: 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.
Time frame: Up to 28 days from ICU admission, or until hospital discharge, whichever occurs first.
Comparative performance improvement over baseline AI models.
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.
Time frame: 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.
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.
Time frame: Up to 28 days from ICU admission, or until hospital discharge, whichever occurs first.
Model interpretability based on imaging and clinical feature contributions.
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.
Time frame: 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.
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.
Time frame: Up to 28 days from ICU admission, or until hospital discharge, whichever occurs first.
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