Risk heterogeneity within hypoinflammatory acute respiratory failure: continuous probabilities identify high-risk...

Binary inflammatory subphenotype classification (hyperinflammatory vs. hypoinflammatory) may guide trial enrollment in acute hypoxemic respiratory failure (AHRF), but assumes within-category homogeneity. We determined whether continuous probabilities reveal clinically meaningful heterogeneity.

We analyzed 575 critically ill adults with AHRF (Pittsburgh Acute Lung Injury Registry) and validated findings in 1134 patients from the EDEN trial, the COVID-19 cohorts, and the RoCI registry. Continuous subphenotype probabilities were calculated using a parsimonious biomarker model (IL-6, sTNFR-1, bicarbonate; probability threshold 0.5). The primary outcome was 90-day mortality.

Among 575 patients, 77 patients (13%) were hyperinflammatory and 498 (87%) hypoinflammatory. Hyperinflammatory patients demonstrated prognostic homogeneity (mortality overall 55%, p = 0.72, across tertiles). Hypoinflammatory patients exhibited marked heterogeneity: 90-day mortality increased from 19 to 31% to 40% across probability tertiles (P < 0.001). Restricted cubic spline modeling demonstrated a strong non-linear relationship between continuous probabilities and mortality, with the steepest risk increases occurring below the 0.5 threshold. Clinical severity scores and biomarkers of immune activation increased progressively across hypoinflammatory tertiles (all P < 0.001). Among 330 patients with longitudinal sampling, rising probability trajectories within hypoinflammatory groups predicted 50-100% mortality vs. 16-40% for stable or declining trajectories (all P < 0.001); hyperinflammatory patients had poor outcomes regardless of trajectory. External validation confirmed heterogeneity and preserved non-linear probability-mortality patterns across cohorts. Similar patterns were observed with the procalcitonin-based model.

Binary classification obscures substantial prognostic heterogeneity within hypoinflammatory AHRF patients. Continuous probability-based stratification may identify additional trial-eligible high-risk patients and improve enrollment strategies for subphenotype-guided trials.