Background: Recent research in patients with ARDS has consistently shown the presence of two distinct subphenotypes of host-responses (hyper- and hypo-inflammatory) with markedly different outcomes and responses to therapies. However, inherent uncertainty in reaching the diagnosis of ARDS creates considerable biological and clinical overlap with other broadly-defined syndromes of acute respiratory failure, such as patients with risk factors (e.g. sepsis or pneumonia) for ARDS (at-risk for ARDS [ARFA]) or patients with decompensated congestive heart failure (CHF). Limited data are available for the presence of subphenotypes in such broader critically-ill populations. Methods: We enrolled mechanically-ventilated patients with acute respiratory failure (ARDS, ARFA, and CHF) and measured 11 plasma biomarkers at baseline. We applied latent class analysis (LCA) methods to determine optimal subphenotypic classifications in this inclusive patient cohort by considering clinical variables and biomarkers. We then derived a parsimonious logistic regression model for subphenotype predictions and compared clinical outcomes between subphenotypes.Results: We included 334 patients (123 [37%] ARDS, 177 [53%] ARFA, 34 [10%] CHF) in a derivation cohort and 36 patients in a temporally-independent validation cohort. A two-class LCA model was found to be optimal, classifying 29% of patients in the hyper-inflammatory subphenotype, consistent with prior findings. A 4-variable parsimonious model (angiopoietin-2, soluble tumor necrosis factor receptor-1, procalcitonin and bicarbonate) for subphenotype prediction offered excellent classification (area under the curve = 0.98) compared to LCA classifications. For both LCA- and regression model classifications, hyper-inflammatory patients had higher severity of illness by Sequential Organ Failure Assessment scores, fewer ventilator-free days and higher 30- and 90-day mortality (all p<0.01) compared to the hypo-inflammatory group. Subphenotype predictions in the validation cohort revealed consistent trends for clinical outcomes and higher levels of inflammatory biomarkers in the hyper-inflammatory group (22%). Conclusions: Host-response subphenotypes are observable in broader and heterogeneous patient populations beyond just patients with ARDS, and subphenotypic classifications offer prognostic information on clinical outcomes. Accurate subphenotyping is possible with the use of a simple predictive model to improve clinical applicability.