Background
The first description of the acute respiratory distress syndrome (ARDS) dates back more than 50 years [
1]. Since then, multiple attempts have followed to provide the ideal definition for this syndrome [
2,
3]. Nonetheless and albeit the definition of ARDS as acute hypoxemia concomitant to diffuse bilateral lung infiltrates of non-cardiac aetiology should have unified diagnosis and treatment of ARDS, an ever-growing body of research has proven its poor ability to effectively detect the syndrome in first place [
4‐
6].
The pathophysiology of ARDS is characterized by an intense lung inflammation caused by the highly heterogeneous interplay between host and insult, depending on the aetiology of the latter [
7]. This heterogeneity in ARDS presentation mainly reflects on the greatly varying severity of hypoxemia, amount of lung oedema, timing of onset and underlying cause of disease, as well as on the presence of the histopathological hallmark of ARDS: diffuse alveolar damage [
2,
8‐
11]. It is thus not surprising that most randomized controlled trials targeting ARDS in its entirety have failed [
2,
12].
In order to identify more homogeneous subgroups of ARDS, several subclassifications have been proposed based on simple variables such as the severity of hypoxemia or the level of positive end-expiratory pressure (PEEP) applied [
13,
14]. Consideration of these subsets of ARDS has enabled the design of successful interventional randomized control trials [
15,
16]. Recently, latent class analysis (LCA), a well-validated statistical method that is able to identify clusters of similar patients [
17], has been used to describe two distinct phenotypes of ARDS characterized by a different degree of inflammatory response [
18,
19]. These
hypo- and
hyperinflammatory phenotypes have been associated with contrasting natural histories, biological characteristics and outcomes to clinical and pharmacological interventions [
19,
20].
Predictive and prognostic enrichment of immunomodulatory trials in ARDS by considering inflammatory phenotypes may potentially prove ground-breaking. Nevertheless, the cornerstone of ARDS therapy—mechanical ventilation—is mainly governed by respiratory mechanics and gas exchange, for which paucity of information regarding ARDS phenotyping exists.
The aim of this study was to identify different phenotypes of ARDS considering respiratory mechanics, gas-exchange and computed tomography (CT)-derived gas- and tissue-volumes by implementing the LCA methodology, further assessing the natural history and response to a standardized recruitment manoeuvre of these phenotypes.
Discussion
The present study identified two distinct ARDS phenotypes with diverging responses to a standardized recruitment manoeuvre and intensive care outcomes by means of LCA. In contrast to other published LCA analyses, only respiratory mechanics, gas-exchange and CT-derived gas- and tissue-volumes at a PEEP of 5 cmH2O were employed for this analysis. In order to simplify pulmonary phenotype identification, a small subset of variables with high explanatory potential was described.
The heterogeneity of the Berlin definition and the disappointing number of randomized controlled trials having attempted to propose pharmacological interventions and ventilator strategies to improve outcome in ARDS have led to a plethora of attempts to identify homogeneous subgroups of this syndrome. In order to minimize heterogeneity, identification of different ARDS subgroups by means of severity of hypoxemia [
13], pulmonary or extrapulmonary origin [
8], focal and non-focal pulmonary consolidations [
37,
38], the fraction of dead space [
49] and response to PEEP [
10,
39] among others have been proposed. Strikingly, the two pulmonary phenotypes described by the LCA model in the present study contain many of these previous ARDS subclassifying characteristics. As such, phenotype 1 was characterized by a lower severity of hypoxemia at clinical PEEP, a lower proportion of ARDS of pulmonary origin, a lower fraction of dead space and a less inhomogeneous lung than phenotype 2. Indeed, the main describing feature of these two phenotypes was their response to a standardized recruitment manoeuvre, therefore leading to the designation of phenotype 1 as
non-recruitable and phenotype 2 as
recruitable phenotype.
The notion that patients with a potentially highly recruitable lung are at a higher risk of mortality is not precisely novel [
10]. Nonetheless, to this moment, it has generally been regarded as a direct correlate to the severity of ARDS as described by the paO
2/FiO
2 ratio [
10,
22,
40]. In the present study nevertheless, the association between higher proportions of potentially recruitable lung and increasing ARDS severity were only patent in the
recruitable phenotype, indicating a more complex relationship than assumed up until now. Furthermore, the increased mortality in the
recruitable phenotype was not primarily precipitated by the increased proportion of lower paO
2/FiO
2 ratios, remaining after statistical correction for the paO
2/FiO
2 ratio and in a subanalysis considering only those patients with moderate ARDS severity. Overall, the two pulmonary phenotypes here presented, congruently contained many previously described risk factors of ARDS, but could not be solely explained by the presence of one characteristic, thus suggesting the existence of two complex and distinct pulmonary entities.
Recent trials having enriched their patient recruitment by selecting patients with paO
2/ FiO
2 ratios below 150–200 mmHg have been able to prove the positive effect of clinical interventions, which were disappointing in previous more heterogeneous trials [
15,
16,
39,
41]. It can be argued that prognostic enrichment, by severity of ARDS, was the main factor responsible for the success of these trials. Indeed, a paO
2/FiO
2 ratio < 150 mmHg concomitant to a PEEP ≥ 10 cmH
2O has been independently associated with mortalities in the range of 60% [
42]. Nonetheless, most of these trials targeted recruitment interventions, such as prone positioning or an increased PEEP [
16,
39], and by recruiting severer patients might have been predictively enriching their studies with a high proportion of patients belonging to the
recruitable phenotype, which, as opposed to the
non-recruitable phenotype, presented a more prominent physiological response to recruitment and higher PEEP.
The
recruitable phenotype was not only characterized by an impaired oxygenation, but also by a concomitantly reduced ventilation capacity. The higher proportion of physiological dead space coincident with a low respiratory system compliance probably characterize the high proportion of inhomogeneously ventilated, mainly collapsed and potentially recruitable lung in the
recruitable phenotype [
10]. A recent trial assessing the use of personalized mechanical ventilation, including recruitment manoeuvres and higher PEEP settings, in non-focal, inhomogeneous, as opposed to focal ARDS, suggested a survival benefit [
43]. Most importantly, misclassification of lung morphology had a large effect on mortality. If a personalized ventilation approach would have led to reduced mortalities in the
recruitable phenotype remains hypothetical; nonetheless, clinical PEEP levels employed in the
recruitable phenotype were lower than personalized approaches would have targeted [
43‐
45] and mortality was as high as in the misclassified lung morphologies in the LIVE study [
43].
The pulmonary phenotypes in the present study differ in their inception from the
hypo- and
hyperinflammatory phenotypes proposed in the seminal study by Calfee et al., as no inflammatory or laboratory parameters were available for the LCA analysis. Indeed, the two pulmonary phenotypes identified in this study differ from the inflammatory phenotypes in multiple aspects. The
recruitable and
non-recruitable phenotype had similar vasopressor requirements at admission, one of the main clinical features differentiating the two inflammatory phenotypes. Furthermore, the phenotypes described by Calfee et al. present similar paO
2/FiO
2 ratios and differ in severity scoring, much opposed to the here presented pulmonary phenotypes. Nonetheless, other features of both phenotype descriptions overlap; as such, the
recruitable and
hyperinflammatory phenotype both present lower respiratory system compliances and a more pronounced acidosis. The lack of biological data in this study prevents identification of a direct correlation between the pulmonary and inflammatory phenotypes. Nonetheless, multiple studies have independently shown associations between high recruitability, pulmonary inhomogeneity, predominance of a primary ARDS, all characteristics of the
recruitable phenotype and the presence of increased pulmonary inflammatory biomarkers such as sRAGE [
46‐
48] which have been linked to the
hyperinflammatory phenotype [
20,
49]. A certain overlap between the
recruitable and the
hyperinflammatory phenotype would also explain the positive response to PEEP in the
hyperinflammatory phenotype [
19].
Differences and overlaps between phenotypes are not surprising; indeed, the description of the
hypo- and
hyperinflammatory phenotype does not preclude the existence of further phenotypes in ARDS. As in many other diseases and syndromes, a plethora of different phenotypes, overlapping in multiple facets and with clear-cut differentiation in others, might very well exist [
50]. Identification of the pulmonary and inflammatory phenotypes may thus be complementary, while enrichment of immunomodulatory trials could profit from phenotypisation by inflammatory phenotypes, trials targeting personalized mechanical ventilation and recruitment strategies might benefit from enrichment by pulmonary phenotypes [
51]. This admittedly complex customization of trials might be the key to success in personalized ARDS medicine, in analogy to the great variance of phenotype-enriched trials in oncology [
52].
The present study has to account for certain limitations. First and foremost, this study is a retrospective analysis of a prospective cohort with all the limitations a post hoc analysis may encompass to the generalizability of the discussed results. Nonetheless, multiple sensitivity analyses suggest internal robustness of the LCA model and the inferred pulmonary phenotypes. Second, due to the extended inclusion period of 16 years, the moderate inclusion rate of one patient per month and the ARDS criteria changing in 2012, the possibility cannot be ruled out, that clinical diagnosis of ARDS was missed and a reduced number of patients were not included in the present cohort. However, as the characteristics of the described ARDS population are comparable to other cohorts and the pulmonary phenotyping was indifferent to temporality in the sensitivity analyses, selection bias can be regarded as residual. Third, the follow-up of the patients was limited to ICU outcome status and no data regarding hospital mortality were available. To mitigate the resulting presence of right informative censoring, Fine and Gray competing risk modelling was employed. Fourth, the presence of a moderate proportion of missing values, albeit mitigated by use of a multiple imputation methodology, might have influenced the final LCA phenotype description. Fifth, no biomarkers were collected in the framework of this study, precluding comparison of the here proposed pulmonary phenotypes with the inflammatory phenotypes and preventing the investigation of a deeper biological association between the phenotypes. Sixth, time from ICU admission to CT-scans and respiratory mechanics assessment was variable between patients, as such, temporal influence on the LCA results cannot be ruled out. Likewise, the stability of the pulmonary phenotypes over time has not been assessed. Seventh, no information on longitudinally employed ventilation settings was available, preventing stratified analysis of the effect of these settings on mortality in the different phenotypes. Finally, this study and the here described phenotypes lack external validation in an independent cohort.
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