Subphenotypes of mechanically ventilated acute respiratory distress syndrome patients based on multi-dimensional...

Acute respiratory distress syndrome (ARDS) is a life-threatening condition with significant heterogeneity in pathophysiology. The integration of pulmonary edema indices, respiratory mechanics, and gas exchange parameters to define subphenotypes in mechanically ventilated patients with ARDS has not yet been investigated.

We conducted a post hoc analysis of a prospective observational study with a derivation cohort (n = 111). We applied K-means clustering to identify distinct subphenotypes based on key physiological parameters: pulmonary edema indices, respiratory mechanics, and gas exchange variables. The primary outcome was 28-day mortality. Between-group differences in 28-day mortality were analyzed using the chi-square test. Survival analysis was performed with Kaplan-Meier curves (compared by log-rank test) and multivariable Cox regression to adjust for covariates. Furthermore, we compared the differential responses to prone positioning ventilation among the identified subphenotypes to evaluate its potential interaction effect on mortality. An independent validation cohort (n = 55) was used to confirm the subphenotype classifications and their relationships with clinical outcomes.

Unsupervised clustering revealed two distinct subphenotypes. Subphenotype 2, characterized by elevated pulmonary vascular permeability index (PVPI) and ventilation ratio (VR), demonstrated significantly higher 28-day mortality compared to Subphenotype 1 (50.0% vs. 28.2%, p = 0.021). This survival disadvantage was confirmed by Kaplan-Meier analysis (log-rank p = 0.016) and a multivariable Cox regression model (adjusted hazard ratio [HR] 2.263, 95% confidence interval [CI] 1.206-4.245; p = 0.011). Furthermore, a statistically significant interaction was observed between subphenotypes and response to prone positioning for 28-day mortality (p-for-interaction = 0.015). Crucially, the prognostic distinction between subphenotypes and their differential treatment response were consistently replicated in an independent validation cohort.

Using unsupervised machine learning, this study identified two distinct ARDS subphenotypes characterized by divergent profiles in pulmonary edema, respiratory mechanics, and gas exchange. These subphenotypes were associated with significantly different clinical outcomes and exhibited a differential response to prone positioning therapy. Future research should prioritize the execution of large-scale, multicenter, randomized controlled trials to validate these findings and advance the clinical implementation of precision medicine in the management of ARDS.