Biomarker-Based Classification of Patients With Acute Respiratory Failure Into Inflammatory Subphenotypes: A Single-Center Exploratory Study

Abstract

Objectives: Hyper- and hypoinflammatory subphenotypes discovered in patients with acute respiratory distress syndrome predict clinical outcomes and therapeutic responses. These subphenotypes may be important in broader critically ill patient populations with acute respiratory failure regardless of clinical diagnosis. We investigated subphenotyping with latent class analysis in an inclusive population of acute respiratory failure, derived a parsimonious model for subphenotypic predictions based on a small set of variables, and examined associations with clinical outcomes.

Design: Prospective, observational cohort study.

Setting: Single-center, academic medical ICU.

Patients: Mechanically ventilated patients with acute respiratory failure.

Measurements and main results: We included 498 patients with acute respiratory failure (acute respiratory distress syndrome: 143, at-risk for acute respiratory distress syndrome: 198, congestive heart failure: 37, acute on chronic respiratory failure: 23, airway protection: 61, and multifactorial: 35) in our derivation cohort and measured 10 baseline plasma biomarkers. Latent class analysis considering clinical variables and biomarkers determined that a two-class model offered optimal fit (23% hyperinflammatory subphenotype). Distribution of hyperinflammatory subphenotype varied among acute respiratory failure etiologies (acute respiratory distress syndrome: 31%, at-risk for acute respiratory distress syndrome: 27%, congestive heart failure: 22%, acute on chronic respiratory failure 0%, airway protection: 5%, and multifactorial: 14%). Hyperinflammatory patients had higher Sequential Organ Failure Assessment scores, fewer ventilator-free days, and higher 30- and 90-day mortality (all p < 0.001). We derived a parsimonious model consisting of angiopoietin-2, soluble tumor necrosis factor receptor-1, procalcitonin, and bicarbonate and classified subphenotypes in a validation cohort (n = 139). Hyperinflammatory patients (19%) demonstrated higher levels of inflammatory biomarkers not included in the model (p < 0.01) and worse outcomes.

Conclusions: Host-response subphenotypes are observable in a heterogeneous population with acute respiratory failure and predict clinical outcomes. Simple, biomarker-based models can offer prognostic enrichment in patients with acute respiratory failure. The differential distribution of subphenotypes by specific etiologies of acute respiratory failure indicates that subphenotyping may be more relevant in patients with hypoxemic causes of acute respiratory failure and not in patients intubated for airway protection or acute on chronic decompensation.

Keywords: acute respiratory distress syndrome; acute respiratory failure; biomarkers; heterogeneity; latent class analysis; subphenotypes.

Figure 1.

Flow chart of enrolled patients in the derivation and validation cohorts with displayed distribution of hyper- versus hypoinflammatory subphenotypes by different types of analyses. The four-variable internal parsimonious model used levels of bicarbonate, tumor necrosis factor receptor (TNFR)–1, angiopoietin-2, and procalcitonin, whereas the three-variable external model used levels of interleukin-8, bicarbonate, and TNFR-1. Prediction with parsimonious models was not possible for 28 of 498 subjects (5.6%) due to missingness of one or more biomarker levels.

Figure 2.

Hyperinflammatory patients had worse 30-d survival and longer time to liberation from mechanical ventilation. Kaplan-Meier curves for (A) 30-d survival and (B) time to liberation from mechanical ventilation for each subphenotype as derived by the latent class analysis model. p values for differences between subphenotypes were obtained with a log-rank test. Adjusted hazard ratios (HRs) with 95% CIs are displayed for the effects of the hyperinflammatory subphenotype, as derived from multivariate Cox proportional hazards models adjusted for age and clinical category of acute respiratory failure. Ninety-day survival data were very similar to 30-d and are not shown.

Figure 3.

Distribution of subphenotypes among clinical categories of acute respiratory failure. The proportions of subphenotypic classifications were significantly different between the clinical categories of acute respiratory failure (p < 0.01). Patients intubated for airway protection had very low proportion (5%) of hyperinflammatory subphenotype classification, whereas no patients with acute on chronic respiratory failure were classified to the hyperinflammatory subphenotype. A much higher proportion of patients was assigned to the hyperinflammatory subphenotype in acute respiratory distress syndrome (ARDS) (31%), at-risk for ARDS (27%) and congestive heart failure (CHF) (22%) groups.

Figure 4.

Patients classified to the hyperinflammatory subphenotype by the four-variable parsimonious model had higher levels of all other plasma biomarkers not included in the four-variable model. Biomarker values are displayed on logarithmic scale. ****p < 0.0001. BDG = 1-3-beta-D-glucan, IL = interleukin, RAGE = receptor of advanced glycation end product, ST-2 = suppression of tumorigenicity-2.

Figure 5.

Hyperinflammatory patients in the validation cohort had worse 30-d survival compared with hypoinflammatory patients. Kaplan-Meier curves for 30-d survival for each subphenotype, as derived by the four-variable parsimonious model. p values for differences between subphenotypes were obtained with a log-rank test. Adjusted hazard ratios (HRs) with 95% CIs are displayed for the effects of the hyperinflammatory subphenotype, as derived from a multivariate Cox proportional hazards model adjusted for age and clinical category of acute respiratory failure (coronavirus disease 2019 [COVID-19] acute respiratory distress syndrome [n = 40], COVID-19 pneumonia not intubated [n = 40], and non-COVID acute respiratory failure [n = 59]).