An observational study in ARDS (Acute Respiratory Distress Syndrome), Respiratory Failure and Mechanical Ventilation, sponsored by Ruijin Hospital. Not yet recruiting at 1 site in China. Open to participants aged 18 Years to 90 Years. Per ClinicalTrials.gov, last updated 2026-10-27.
Sponsored by Ruijin Hospital · Observational
Acute Respiratory Distress Syndrome (ARDS) is a highly heterogeneous condition, making precision mechanical ventilation challenging. Electrical Impedance Tomography (EIT) provides real-time monitoring of regional lung ventilation, but interpreting its complex spatiotemporal data relies on subjective clinical experience. This multicenter ambispective cohort study aims to develop and validate an all-in-one Artificial Intelligence Foundation Model (AutoEIT). The study consists of two phases: (1) A Retrospective Phase (data from 2020 to 2025, N=1000) to train the AutoEIT model using continuous EIT images and synchronous high-frequency ventilator waveforms; and (2) A Prospective Temporal Phase (January 2026 to December 2026, N=200) to rigorously test the model's performance in a real-world clinical setting. The AutoEIT model is a deep multi-task learning framework designed to simultaneously perform five core clinical tasks: (1) classify ARDS subphenotypes, (2) recommend individualized positive end-expiratory pressure (PEEP), (3) detect occult patient-ventilator asynchrony, (4) predict ventilation/perfusion (V/Q) ratio, and (5) continuously estimate the non-invasive PaO₂/FiO₂ ratio. Ultimately, this system aims to reduce ventilator-induced lung injury (VILI) and improve precision respiratory care.
This is a multicenter ambispective cohort study designed to develop and prospectively validate an all-in-one Artificial Intelligence Foundation Model (AutoEIT) for the precision management of Acute Respiratory Distress Syndrome (ARDS). The study comprises two distinct phases:
Phase 1 - Retrospective Training and Internal Validation (2020-2025): A total of 1000 adult ARDS patients who received invasive mechanical ventilation and continuous Electrical Impedance Tomography (EIT) monitoring across three participating tertiary hospitals will be enrolled. High-frequency ventilator waveforms (≥ 200 Hz), synchronized EIT image sequences (≥ 20 frames/second), and concurrent arterial blood gas (ABG) measurements will be extracted from electronic health records. This dataset will be used to train the shared spatiotemporal encoder and five task-specific prediction heads of the AutoEIT foundation model using a deep multi-task learning framework. Internal validation will be performed via 5-fold cross-validation, with a 70/30 training-to-validation split.
Phase 2 - Prospective Temporal and External Validation (January 2026 - December 2026): A total of 200 adult ARDS patients will be prospectively enrolled at the same centers. For this critical validation phase, millisecond-level synchronization between the EIT system, mechanical ventilator, and clinical events (e.g., ABG sampling timestamps) will be enforced prior to data recording. The AutoEIT model will run in the background in a fully blinded manner (i.e., clinicians will not receive any model-generated outputs), and its five-task predictions will be compared against the clinical ground truths at the end of the monitoring period.
The Five Core Clinical Tasks of the AutoEIT Model:
ARDS Subphenotype Classification: The model will classify patients into hyperinflammatory versus hypoinflammatory phenotypes using latent class analysis (LCA) as the reference standard. Performance will be evaluated by Area Under the Receiver Operating Characteristic curve (AUC-ROC), with a predefined clinically acceptable threshold of AUC-ROC ≥ 0.80.
Individualized Positive End-Expiratory Pressure (PEEP) Recommendation: The model will recommend an optimal PEEP level derived from the intersection of lung collapse and overdistension curves during a standardized decremental PEEP trial. Accuracy will be quantified by Root Mean Square Error (RMSE) in cmH₂O, with a predefined acceptable threshold of ≤ 2.0 cmH₂O.
Occult Patient-Ventilator Asynchrony Detection: The model will detect asynchrony events (including pendelluft, breath-stacking, and ineffective triggering) in real-time, validated against blinded expert review of synchronized video, EIT, and waveform data. Performance will be assessed by AUC-ROC, with a predefined threshold of AUC-ROC ≥ 0.85.
Ventilation/Perfusion (V/Q) Ratio Prediction: The model will continuously estimate the regional V/Q ratio, with reference values obtained from the multiple inert gas elimination technique (MIGET) or a validated surrogate. Performance will be measured by Pearson Correlation Coefficient (PCC), with a predefined acceptable threshold of PCC ≥ 0.70.
Non-invasive PaO₂/FiO₂ Ratio Estimation: The model will predict the PaO₂/FiO₂ ratio without invasive ABG sampling, compared against simultaneous ABG measurements. Accuracy will be evaluated by RMSE (dimensionless), with a predefined acceptable threshold of ≤ 20 units. Bland-Altman limits of agreement and mean bias will be reported as descriptive secondary analyses.
Statistical Analysis Plan:
For the retrospective phase, model training will be conducted using PyTorch with an Adam optimizer and a learning rate of 1e-4. Early stopping with a patience of 10 epochs will be applied to prevent overfitting. For the prospective validation phase, all five primary outcomes will be assessed using a per-patient analysis. AUC-ROC will be calculated with 95% confidence intervals using DeLong's method; RMSE will be reported with bootstrap 95% confidence intervals (1000 resamples); PCC will be computed with 95% confidence intervals using Fisher's z-transformation. Subgroup analyses will be performed according to ARDS severity (moderate vs. severe), age group (≥ 60 vs. \< 60 years), and primary etiology (pulmonary vs. extrapulmonary). All statistical tests will be two-sided with a significance level of α = 0.05. Missing data will be handled via multiple imputation by chained equations (MICE) if the proportion of missingness is \< 20%; otherwise, complete-case analysis will be performed.
Sample Size Justification: The retrospective cohort of 1000 patients is derived from the available data from 2020 to 2025 at the three participating centers and is considered adequate for deep learning model training based on prior similar EIT-AI studies. The prospective cohort of 200 patients was calculated to provide > 80% power to detect a moderate effect size (Cohen's d = 0.5) for the primary outcomes, assuming a two-sided α of 0.05 and a 15% dropout rate. This sample size also ensures sufficient event counts (≥ 10 events per predictor variable) for the Cox proportional hazards models used in secondary mortality analyses.
Study Governance: The study is sponsored by Ruijin Hospital, with collaborating centers including Peking Union Medical College Hospital and Fujian Provincial Hospital. An independent Data Monitoring Committee (DMC) will oversee patient safety and interim analyses. The study protocol has been approved by the Ruijin Hospital Ethics Committee (Approval No. 202647), and all patients or their legal surrogates will provide written informed consent prior to enrollment in the prospective phase.
382 studies on the registry are indexed under Acute Lung Injury; 73 are open to participants now.
This study's planned enrollment of 1,200 is above the median of 100 across 131 observational studies indexed under Acute Lung Injury.
Browse Acute Lung Injury studies →Ruijin Hospital is the lead sponsor of 679 studies on the registry; 387 are open to participants now.
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Adult ARDS patients admitted to participating ICUs who received invasive mechanical ventilation and EIT monitoring
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Exclusion Criteria:
Diagnostic Test: AutoEIT Model Training
Diagnostic Test: AutoEIT Blinded Validation
Adult ARDS patients admitted to participating ICUs between January 2020 and December 2025 who received invasive mechanical ventilation and EIT monitoring. Data from this cohort will be used exclusively to train the shared spatiotemporal encoder and the five task specific prediction heads of the AutoEIT foundation model.
Adult ARDS patients prospectively enrolled between January 2026 and December 2026. For this cohort, strict millisecond-level synchronization between the EIT system, mechanical ventilator, and clinical events (e.g., ABG sampling) will be enforced. The AutoEIT model will run in the background (blinded to treating clinicians) to generate the 5-task predictions, which will later be compared against actual clinical ground truths.
Diagnostic Performance of ARDS Subphenotype Classification
Area Under the Receiver Operating Characteristic curve (AUC-ROC) of the AutoEIT model for classifying ARDS subphenotypes into hyperinflammatory versus hypoinflammatory phenotypes, using latent class analysis (LCA) based on clinical and biomarker data as the reference ground truth. AUC-ROC will be calculated with 95% confidence intervals using DeLong's method. A predefined threshold of AUC-ROC ≥ 0.80 will be considered clinically acceptable.Unit of Measure: AUC-ROC (dimensionless, 0 to 1)
Time frame: At baseline, within 24 hours of ARDS diagnosis or Intensive Care Unit (ICU) admission.
Accuracy of Optimal PEEP Recommendation
Root Mean Square Error (RMSE) between the AutoEIT-recommended positive end-expiratory pressure (PEEP) and the clinically optimal PEEP derived from the intersection point of lung collapse and overdistension curves obtained during a standardized decremental PEEP trial. RMSE will be calculated in cmH2O; lower values indicate better agreement. A clinically acceptable RMSE threshold is predefined as ≤ 2.0 cmH2O.Unit of Measure: cmH2O
Time frame: At the time of the first standardized decremental PEEP trial, within 72 hours of enrollment.
Detection Accuracy of Patient-Ventilator Asynchrony
Area Under the Receiver Operating Characteristic curve (AUC-ROC) of the AutoEIT model for detecting occult patient-ventilator asynchrony events, including pendelluft, breath-stacking, and ineffective triggering, validated independently against blinded expert review of synchronized video, EIT images, and ventilator waveforms by two intensivists. AUC-ROC will be calculated with 95% confidence intervals. A predefined threshold of AUC-ROC ≥ 0.85 will be considered clinically acceptable.Unit of Measure: AUC-ROC (dimensionless, 0 to 1)
Time frame: Throughout the EIT monitoring period, within 72 hours of enrollment.
Accuracy of V/Q Ratio Prediction
Pearson Correlation Coefficient (PCC) between the AutoEIT-predicted ventilation/perfusion (V/Q) ratio and the physiological ground truth measured by the reference multiple inert gas elimination technique (MIGET) or a surrogate method. PCC will be calculated with 95% confidence intervals using Fisher's z-transformation. A predefined threshold of PCC ≥ 0.70 will be considered clinically acceptable.Unit of Measure: PCC (dimensionless, range -1 to 1)
Time frame: Throughout the EIT monitoring period, within 72 hours of enrollment.
Accuracy of Non-invasive PaO2/FiO2 Ratio Prediction
Root Mean Square Error (RMSE) between the AutoEIT-predicted non-invasive PaO2/FiO2 ratio and the simultaneously measured arterial blood gas (ABG) PaO2/FiO2 ratio. RMSE will be calculated in mmHg; lower values indicate better agreement. A clinically acceptable RMSE threshold is predefined as ≤ 20 mmHg.Unit of Measure: mmHg
Time frame: Throughout the EIT monitoring period, within 72 hours of enrollment.
Prognostic Association with 28-Day Ventilator-Free Days (VFDs)
Correlation between AutoEIT-derived dynamic spatiotemporal features extracted from the foundation model embeddings and the number of ventilator-free days (VFDs) at day 28, defined as the number of days from successful weaning to day 28 during which the patient is alive and free from invasive mechanical ventilation. The association will be assessed using multivariable linear regression models adjusted for Acute Physiology and Chronic Health Evaluation II (APACHE II) score, age, and baseline PaO2/FiO2 ratio. Partial correlation coefficients (r) and 95% confidence intervals will be reported.Unit of Measure: Days (continuous)
Time frame: From enrollment to the end of treatment at day 28.
Intensive Care Unit (ICU) All-Cause Mortality Prediction
Predictive value of AutoEIT-derived subphenotypes, hyperinflammatory versus hypoinflammatory, for ICU all-cause mortality, assessed using Cox proportional hazards regression models. Hazard ratios (HR) with 95% confidence intervals will be reported for the association between subphenotype and mortality, adjusted for age, APACHE II score, and vasopressor use.Hazard ratio (HR, dimensionless)
Time frame: From ICU admission to ICU discharge or death, assessed up to day 28.
28-Day All-Cause Mortality Prediction
Predictive value of AutoEIT-derived subphenotypes, hyperinflammatory versus hypoinflammatory, for 28-day all-cause mortality, assessed using Cox proportional hazards regression models. Hazard ratios (HR) with 95% confidence intervals will be reported for the association between subphenotype and mortality, adjusted for age, APACHE II score, and vasopressor use. Cumulative incidence of mortality will be visualized using Kaplan-Meier curves, and the log-rank test will be used to compare survival distributions between subphenotypes.Hazard ratio (HR, dimensionless)
Time frame: From enrollment to day 28.
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