Identification of four latent classes of acute respiratory distress syndrome using PaO2/FIO2 ratio: an observational cohort study

Biological phenotypes in patients with the acute respiratory distress syndrome (ARDS) have previously been described. We hypothesized that the trajectory of PaO2/FIO2 ratio could be used to

identify phenotypes of ARDS. We used a retrospective cohort analysis of an ARDS database to identify latent classes in the trajectory of PaO2/FIO2 ratio over time. We included all adult

patients admitted to an intensive care unit who met the Berlin criteria for ARDS over a 4-year period in tertiary adult intensive care units in Manitoba, Canada. Baseline demographics were

collected along with the daily PaO2/FIO2 ratio collected on admission and on days 1–7, 14 and 28. We used joint growth mixture modeling to test whether ARDS patients exhibit distinct

phenotypes with respect to both longitudinal PaO2/FIO2 ratio and survival. The resulting latent classes were compared on several demographic variables. In our study group of 209 patients, we

found that four latent trajectory classes of PaO2/FIO2 ratio was optimal. These four classes differed in their baseline PaO2/FIO2 ratio and their trajectory of improvement during the 28 

days of the study. Despite similar baseline characteristics the hazard for death for the classes differed over time. This difference was largely driven by withdrawal of life sustaining

therapy in one of the classes. Latent classes were identified in the trajectory of the PaO2/FIO2 ratio over time, suggesting the presence of different ARDS phenotypes. Future studies should

confirm the existence of this finding and determine the cause of mortality differences between classes.

The mortality of ARDS remains between 37 and 48%1,2. The disappointing results of many clinical trials is possibly due to different ARDS phenotypes resulting from the heterogeneous causes of

ARDS. Identifying these different ARDS groups early may result in an improved treatment effect within randomized trials3,4.

Several studies have attempted to identify ARDS phenotypes by using biological markers5,6,7. These studies have identified several ARDS phenotypes and, in reanalyzing some of the previous

interventional ARDS trials, found outcome differences based on the underlying phenotype. In the Fluid and Catheter Treatment Trial, the intention to treat trial found no difference in

survival8. However, when data from this trial were re-analyzed comparing patient groups based on a two class sub-phenotype model of ARDS, a mortality difference was present9. Similarly, in a

trial comparing simvastatin with placebo in ARDS patients, the initial trial was negative for a mortality difference, but with re-analysis of the data utilizing the two-phenotype model, the

hyperinflammatory phenotype showed a reduced mortality with simvastatin administration10,11.

These recent studies of ARDS phenotypes have utilized a statistical technique called latent class analysis (LCA). LCA is a type of mixture modeling used to find hidden clusters among

multivariate data, based on the hypothesis that the observed variance and patterns are caused by several unobserved groups or classes. We hypothesized that longitudinal lung function data

may also demonstrate latent groupings (trajectories) in patients with ARDS.

Our hypothesis was that there are phenotypes that can be discovered using PaO2/FIO2 ratio over time in patients with ARDS. We conducted a retrospective cohort analysis of our institutional

ARDS database to determine, as our primary outcome, if there were latent classes present in the trajectories of PaO2/FIO2 ratio. Secondary outcomes were to determine what, if any,

differences were present between the different classes that were discovered.

Two hundred and nine (209) patients met inclusion criteria and had complete data for analysis. Baseline demographics of the patients are presented in Table 1 and are grouped as those who

survived and those who died. There were 121 survivors and 88 patients who died for a mortality rate of 42.1%.

Patients who died were older and had a higher APACHE II score than those who survived, although the Charlson co-morbidity score was similar between patient classes. PaO2/FIO2 ratio and

pulmonary compliance on day 1 were slightly lower in the group who died. Ventilatory parameters (tidal volume/kg ideal body weight, PEEP, and plateau pressure) were similar between groups,

and consistent with current ARDS ventilator management guidelines. The dominant etiology of ARDS was pneumonia, with non-lung sepsis comprising the next largest etiological category.

The Bayesian Information Criteria (BIC) suggested that a 4-class model provided the best fit. Table 2 shows the BIC and the posterior classification probabilities for each class. The 4-class

model had the lowest BIC with a 5-class model demonstrating a slightly higher BIC.

The posterior classification probability is a key indicator of model fit and is shown in Table 3. This is each subjects’ estimated probability of belonging to each latent class, based on

their unique observations of PaO2/FIO2 ratio and survival. If the model has extracted well-separated and predictive latent classes, each subject should map with high probability to one

latent class only and have low probability elsewhere. As can be seen in Table 3, the classes seem well separated, with average posterior classification probabilities for all classes

exceeding 0.8.

Figure 1 shows the PaO2/FIO2 ratio trajectory for the 4 latent classes. All the classes have an initial PaO2/FIO2 ratio consistent with moderate to severe ARDS. Two of these three classes

(class 1, black and 2, red) have an increase in their PaO2/FIO2 ratio over time, with one (black) having a significant increase in PaO2/FIO2 ratio within the first 3 days. One class has a

PaO2/FIO2 ratio that fails to improve during the hospital stay (class 4, blue). The final class (class 3, green) began with a PaO2/FIO2 ratio that would be defined as moderate (borderline

mild) ARDS and fails to show an improvement in their PaO2/FIO2 ratio over time.

Estimated latent trajectories for the 4 different classes based on PaO2/FIO2 ratio. Values are plotted with their 95% confidence interval. Class 1: black; class 2: red; class 3: green; class

4: blue.

The survival probability of the different classes is shown in Fig. 2. Class 4 (blue), the class with the second lowest PaO2/FIO2 ratio, the highest APACHE score, and which showed no

improvement over time had the lowest survival probability of all 4 classes. The hazard ratio for this class was highest within the first 5 days and then began to level off. The two classes

that showed significant improvement in their PaO2/FIO2 ratio within the early phase of their ARDS (class 1 black and class 2, red) showed the best survival, with their hazard ratio for death

showing low initial rates that continued throughout the study period.

Class specific event free survival probability for the different classes. Class 4 (blue), the class with the second lowest PaO2/FIO2 ratio, the highest APACHE score, and which showed no

improvement over time had the lowest survival probability of all 4 classes. The two classes that showed significant improvement in their PaO2/FIO2 ratio within the early phase of their ARDS

(class 1 black and class 2, red) showed the best survival. Class 3 (green) had the least severe form of ARDS based on initial PaO2/FIO2 ratio and did not show an improvement in their

PaO2/FIO2 ratio and showed a survival trend that mimicked the patient class with the most severe form of ARDS (class 4 blue). The hazard function for this class increased exponentially past

day 10.

The final class (class 3 green) was the class that began with what appeared to be the least severe form of ARDS based on initial PaO2/FIO2 ratio. This class did not show an improvement in

their PaO2/FIO2 ratio and showed a survival trend that mimicked the patient class with the most severe form of ARDS (class 4 blue), based on PaO2/FIO2 ratio. The hazard function for this

class increased exponentially past day 10.

To determine if there were any clinical differences between the groups, we analyzed patient demographics between the 4 latent classes. The goal was to determine how the classes differed with

the hope of explaining and predicting class membership.

We first calculated each subject’s posterior classification probability, which tells us their chance of belonging to each latent class, given their longitudinal and survival data. We then

assigned each subject to their likeliest class, and treated it as observed. This process is known as modal assignment. Since the posterior probabilities are high for each likeliest class,

this imparts tolerable amounts of error for this process. The results of this analysis including key variables is presented in Table 4.

The most interesting class to examine in this regard is class 3 (green). This is the class that initially had a PaO2/FIO2 that would be categorized as moderate (borderline mild) ARDS and was

higher than all the other classes yet failed to improve during their ICU stay. This class had the lowest APACHE II score of all 4 classes (Fig. 3), and the distribution of this variable

differed significantly between latent classes (Kruskal–Wallis chi-squared p = 0.008). This suggests a greater severity of non-pulmonary disease than the other classes. This class was also

older than the other classes (data not shown; Kruskal–Wallis chi-squared p = 0.02).

Acute physiology, age and chronic health evaluation II (APACHE) score between classes. Class 3 (green) had the lowest score of all classes, suggesting reduced severity of disease. p = 0.008

Kruskal–Wallis rank sum test. This is despite this class having a decreased survival probability when compared with class 1 and class 2.

The number of days intubated did differ between classes, with class 1 (black) showing the shortest length of intubation amongst the classes (Fig. 4, Table 4, Kruskal–Wallis p = 0.003).

However, this difference in days intubated disappeared if class 1 was excluded. This suggests that class 1 had a milder form of ARDS that improved rapidly. This is confirmed by the PaO2/FIO2

trajectory and hazard ratio in Figs. 1 and 3.

Difference in days intubated between classes. There was a significant difference between classes (p = 0.003, Kruskal–Wallis rank sum test), with class 1 having the shortest intubation time.

When class 1 was removed from analysis, the difference became non-significant between classes (data not shown).

When examining other demographic variables, there was no difference between the classes with respect to distribution of sex (Fisher’s exact test p value = 0.2). There was a significant

difference between classes, however, with respect to the rate at which care was withdrawn. Patients in class 4 (blue) experienced withdrawal of life-sustaining therapies at a significantly

higher rate than those in the other latent classes (Fisher’s exact test p