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Trajectory patterns of mechanical power and prognosis in ARDS: a longitudinal analysis using group-based trajectory
Wei Song1, Guolong Cai2, Yi Zhou1
1The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, China.
European Journal of Medical Research
|December 7, 2025
Summary
Distinct mechanical power trajectories in Acute Respiratory Distress Syndrome (ARDS) patients predict 28-day mortality. High mechanical power groups showed a significantly increased risk, offering insights for optimizing ventilator management.
Area of Science:
- Critical Care Medicine
- Pulmonary Medicine
- Biostatistics
Background:
- Mechanical power is a prognostic predictor in ARDS.
- Previous studies used cross-sectional data, limiting understanding of dynamic changes.
- Longitudinal data analysis is needed to capture disease progression.
Purpose of the Study:
- To investigate the association between mechanical power trajectories and 28-day mortality in ARDS patients.
- To utilize longitudinal data for a dynamic assessment of mechanical power.
- To identify distinct patterns of mechanical power over time and their prognostic implications.
Main Methods:
- Data extracted from the MIMIC-IV database (version 2.2).
- Mechanical power stratified into quartiles for trend analysis.
- Group-based trajectory modeling (GBTM) identified distinct mechanical power trajectories.
- Multivariate logistic regression analyzed the association between trajectories and 28-day mortality.
Main Results:
- 1,439 ARDS patients included.
- Higher mechanical power correlated with increased physiological parameters (P < 0.001).
- GBTM identified three trajectories: low, medium, and high mechanical power.
- The high-power group had significantly higher 28-day mortality risk (OR 1.33, P = 0.017) after adjustment.
Conclusions:
- Distinct mechanical power trajectories are identifiable in ARDS patients.
- These trajectories are significantly associated with 28-day mortality.
- Findings provide practical insights for optimizing mechanical ventilator management in ARDS.
