Host-Response Subphenotypes Offer Prognostic Enrichment in Patients With or at Risk for Acute Respiratory Distress Syndrome

Abstract

Objectives: Classification of patients with acute respiratory distress syndrome into hyper- and hypoinflammatory subphenotypes using plasma biomarkers may facilitate more effective targeted therapy. We examined whether established subphenotypes are present not only in patients with acute respiratory distress syndrome but also in patients at risk for acute respiratory distress syndrome (ARFA) and then assessed the prognostic information of baseline subphenotyping on the evolution of host-response biomarkers and clinical outcomes.

Design: Prospective, observational cohort study.

Setting: Medical ICU at a tertiary academic medical center.

Patients: Mechanically ventilated patients with acute respiratory distress syndrome or ARFA.

Interventions: None.

Measurements and main results: We performed longitudinal measurements of 10 plasma biomarkers of host injury and inflammation. We applied unsupervised latent class analysis methods utilizing baseline clinical and biomarker variables and demonstrated that two-class models (hyper- vs hypoinflammatory subphenotypes) offered improved fit compared with one-class models in both patients with acute respiratory distress syndrome and ARFA. Baseline assignment to the hyperinflammatory subphenotype (39/104 [38%] acute respiratory distress syndrome and 30/108 [28%] ARFA patients) was associated with higher severity of illness by Sequential Organ Failure Assessment scores and incidence of acute kidney injury in patients with acute respiratory distress syndrome, as well as higher 30-day mortality and longer duration of mechanical ventilation in ARFA patients (p < 0.0001). Hyperinflammatory patients exhibited persistent elevation of biomarkers of innate immunity for up to 2 weeks postintubation.

Conclusions: Our results suggest that two distinct subphenotypes are present not only in patients with established acute respiratory distress syndrome but also in patients at risk for its development. Hyperinflammatory classification at baseline is associated with higher severity of illness, worse clinical outcomes, and trajectories of persistently elevated biomarkers of host injury and inflammation during acute critical illness compared with hypoinflammatory patients. Our findings provide strong rationale for examining treatment effect modifications by subphenotypes in randomized clinical trials to inform precision therapeutic approaches in critical care.

Figure 1:. Distribution of subphenotypes derived by the LCA and the 3-variable parsimonious predictive models in patients with ARDS and ARFA (A), and differences in standardized values of each continuous variable by LCA subphenotypes in ARDS (B) and ARFA patients (A).

A: The two waffle graphs illustrate the distribution of the hyperinflammatory vs. hypoinflammatory patients in patients with ARDS and ARFA, as well as the agreement of subphenotypic assignments by the two methods used. Hyperinflammatory patients defined by both the LCA and the parsimonious model are shown in dark red boxes, whereas hyperinflammatory patients defined only by the LCA method are shown in light red (with the same depictions in blue color for hypoinflammatory patients). Light blue and red color boxes represent patients misclassified by the parsimonious model if we consider the LCA method as the reference standard in our cohort. The two methods had good agreement by Gwet’s agreement co-efficient (AC1) and Area under the Curve (AUC with standard deviation) statistics. B-C: The variables are sorted on the basis of the degree of separation between the subphenotypes from maximum positive separation on the left (i.e. hyperinflammatory higher than hypoinflammatory). All variables were standardized by mean scaling to zero and standard deviation to 1. Abbreviations: WBC: white blood cell count; RR: respiratory rate; P/F ratio: partial pressure of arterial oxygen / Fractional inhaled concentration of oxygen ratio; Hgb: hemoglobin; BMI: body mass index; SBP: systolic blood pressure; PEEP: positive end expiratory pressure; PaCO2: partial arterial pressure of carbon dioxide.

Figure 2:. Kaplan Meier curves for the outcomes of 30-day survival (left panels) and time-to-liberation from mechanical ventilation (right panels) for ARDS (top row) and ARFA patients (bottom row), stratified by LCA-derived subphenotypes.

P-values for differences between subphenotypes were obtained with a log-rank test. Adjusted Hazard Ratios (HR) with their 95% Confidence Intervals for the effects of the hyperinflammatory subphenotype were obtained from multivariate Cox-proportional hazards models. For 30-day survival, Cox models were adjusted for age, PaO2:FIO2 ratio, and SOFA scores. For time-to-liberation, Cox models were adjusted for PaO2:FIO2 ratio, SOFA scores and Positive End-Expiratory Pressure levels. 90-day survival data were very similar to 30-day and are not shown.

Figure 3:. Patients with ARDS assigned to the hyperinflammatory subphenotype at baseline by LCA had persistently higher levels of TNFR1 and Procalcitonin compared to the hypoinflammatory subphenotype, whereas baseline differences in RAGE and Angiopoietin-2 were attenuated over time.

Levels of statistical significance for between subphenotype comparisons obtained from Wilcoxon test at each follow-up time are shown with red asterisks (ns for non-significant p≥0.05, * for p<0.05, ** for p<0.01, *** for p<0.001 and **** for p<0.0001). P-values for interaction were obtained from mixed linear regression models with random patient intercepts and inclusion of an interaction term subphenotype*follow-up interval. The significant p for interaction in the case of RAGE indicates that the trajectory of RAGE levels is different in the hyperinflammatory subphenotype (declining) compared to the hypo-inflammatory subphenotype (no significant change over time).