In a prospective, observational cohort of mechanically-ventilated patients with acute respiratory failure, we demonstrated that the subphenotypic distinction described for patients with ARDS applies in a broader and more heterogeneous critically-ill population (5, 33). While previous subphenotyping models have mainly been derived from secondary analyses of clinical trial populations in patients with ARDS, we revealed the presence of such subphenotypes in an observational, inclusive cohort study. We generated a simplified LCA model utilizing only discriminatory variables at baseline, and then defined a parsimonious 4-variable regression model consisting of well-described biomarkers in ARDS and other critical illness syndromes. Consistent with prior studies, we found that hyper-inflammatory patients at baseline exhibited higher severity of illness, elevated plasma biomarkers beyond the ones used for the subphenotypic predictions, and worse clinical outcomes. Our model encompasses a broad range of patients presenting with acute respiratory failure and various clinical manifestations, findings that support the use of biomarker-based subphenotyping in broader critically ill patient populations.
Subphenotyping research has gained wide popularity in recent years, not only within ARDS but within other critical care syndromes such as sepsis and AKI. Amongst these syndromes, attempts to subdivide patients solely based on clinical criteria have been largely unsuccessful when compared to biomarker-based modeling. In ARDS, subgroups receiving differential ventilator management strategies based on CT morphology showed no difference in mortality, and this strategy even proved to be harmful if misclassified into the wrong group (34). In AKI, similar to ARDS, no effective therapeutic interventions exist other than supportive care and renal replacement therapy. However, with biomarker-based subphenotyping, two distinct subphenotypes of AKI emerged with differential responses to therapy such as vasopressin (35). Parsimonious modeling revealed that the distinguishing biomarkers in AKI are extremely similar to those of our cohort, including bicarbonate, angiopoietin-2, and TNFR1, findings which raise the possibility of common pathways of systemic inflammatory responses between heterogeneous populations of critically-ill patients.
An important challenge in critical care pertains to the subjective and non-specific nature of diagnostic criteria for critical illness syndromes. ARDS is systematically underrecognized or under-reported as a diagnosis in clinical practice; and even among expert providers, there are significant rates of diagnostic disagreement (11, 36). ARDS recognition is straightforward in cases with typical presentations of diffuse, bilateral infiltrates on imaging with an obvious risk factor, such as pneumonia or sepsis. However less classic radiographic presentations are a source of uncertainty and diagnostic discordance. In a retrospective analysis where expert panels identified ARDS by electronic medical record review, ARDS diagnosis had been documented in the clinical chart only 12.4% of the time, at least in part due to underrecognition of the syndrome at the time of clinical encounter (12). Further investigation into underrecognition of ARDS demonstrated that interobserver agreement of ARDS diagnosis under Berlin criteria has only been moderate, with lack of consensus on chest radiograph interpretation accounting for most differences (11). Inter-rate agreement in ARDS radiographic identification remained low even after educational interventions (9). Given such inherent diagnostic uncertainty in ARDS, there is compelling need to understand the underlying biological mechanisms that drive different outcomes in these conditions. Our parsimonious model is not dependent on the requirement of a definite clinical diagnosis (i.e. ARDS vs. CHF) and allows for objective classification into two distinctly behaving subphenotypes.
Our LCA and parsimonious models were created after combining ARDS, ARFA, and CHF patients. While the sample sizes of each group within our study differ, we confirmed that the same hyper- and hypo-inflammatory subphenotypes exist in all 3 subgroups independently, mitigating concern that any one group may be the dominating factor driving subphenotypic results. The remaining respiratory failure groups demonstrated a very low prevalence of hyper-inflammatory patients (7%), confirming that we included only clinically pertinent groups in our primary analysis. Patients intubated for airway protection or those with acute exacerbation for COPD appear to have extremely low prevalence of a hyper-inflammatory subphenotype, and thus this classification framework would not be relevant in these forms of acute respiratory failure.
CHF is a clinical group with paucity of data on subphenotyping, although it has already been proposed that treatment of systolic heart failure should be shifted away from strategies that simply improve cardiac function towards interventions that modulate the systemic responses to cardiac dysfunction instead (37). Interestingly, our results demonstrate that hyper- and hypo-inflammatory subphenotypes exist in CHF patients in very similar proportion to ARDS and ARFA patients, further suggesting biological commonalities amongst critical illness syndromes, regardless of clinical diagnosis. Exclusion of cardiac edema is a major source of disagreement in the clinical decision to diagnose ARDS, and as seen in the FACTT trial, a significant amount of patients diagnosed with ARDS have elevated pulmonary capillary wedge pressures (38). Consequently, it is probable that a certain unknown proportion of patients with ARDS are misclassified as cardiogenic pulmonary edema, and vice versa. A recent study evaluated the longitudinal evolution of radiographic pulmonary edema in ARDS and found that baseline radiographic pulmonary edema was not associated with ARDS severity, clinical outcomes or hyper-inflammatory subphenotype (13). These findings further highlight the disadvantage in using clinical criteria rather than objective biological markers to subdivide such populations.
Our 4-variable parsimonious model includes two markers that have been associated in cohorts much larger than our current study (bicarbonate and TNFR1) (8, 20), one well-validated marker in sepsis and ARDS (angiopoeitin-2) (23, 24), and one already widely clinically available and utilized test (procalcitonin) (27, 28). Angiopoietin-2 may be a possible causal factor in development of ARDS in septic patients (24), in addition to an independent predictor of increased mortality in ARDS (25, 39). Angiopoietin-2 has been included in prior parsimonious models (25, 35, 40) for ARDS and AKI subphenotypes. Similarly, bicarbonate and TNFR1 have been identified as key predictors in prior parsimonious models for ARDS (6, 41). Notably, these analyses did not include the other two biomarkers in our parsimonious model (angiopoietin-2, procalcitonin) as possible classifier variables. Further verification in larger data sets is therefore required. While a parsimonious model allows for easier clinical applicability than complex LCA, models that depend on biomarker variables will eventually require point-of-care or rapid turnaround tests to ensure timely acquisition of results and application to model predictions.
Our study utilized a simplified approach to variable selection for the LCA models. Prior studies have utilized large amounts of variables without any selectivity. In our approach, we removed non-discriminatory variables prior to analysis and found excellent agreement between models before and after removal (Fig. 2). These findings demonstrate the ability to perform LCA with a smaller set of variables and similar or improved statistical performance of the model.
Our study has several limitations. While our study prospectively enrolled consecutive patients with acute respiratory failure, it is limited by sample size and single center design. We also only examined baseline data (within 48 hours intubation), therefore it is unclear whether patients transition between subphenotypes over time. In one analysis, from day 0 to day 3 the majority (> 94%) of patients remained in the same subphenotype (42), though preliminary data within our own cohort demonstrate the possibility of higher rates of transition by days 3–6 (43). Further examination of the stability of subphenotypes will be important, as transition from one group to another will affect the ability to target clinical interventions. While we had an independent data set for validation demonstrating similar trends with outcomes biomarkers, this validation dataset is small, illustrating the need to validate our model in larger data sets. Additionally, we have not yet investigated any differential therapeutic responses between groups, which will be required for clinical applications. Larger, prospective trials of heterogeneous critically ill populations will be necessary to verify subphenotypic models and and explore treatment effects.