Histopathological findings were correlated to severity and duration of ARDS. Using clinical criteria the revised Berlin definition for ARDS allowed the identification of severe ARDS of more than 72 hours as a homogeneous group of patients characterized by a high proportion of DAD.

Acute respiratory distress syndrome (ARDS) is a major clinical problem with high morbidity and mortality. Diffuse alveolar damage (DAD) is considered the histological hallmark for the acute phase of ARDS. DAD is characterized by an acute phase with edema, hyaline membranes, and inflammation, followed by an organizing phase with alveolar septal fibrosis and type II pneumocyte hyperplasia. Given the difficulties in obtaining a biopsy in patients with ARDS, the presence of DAD is not required to make the diagnosis. However, biopsy and autopsy studies suggest that only one-half of patients who meet the clinical definition of ARDS also have DAD. The other half are found to have a group of heterogeneous disorders, including pneumonia. Importantly, the subgroup of patients with ARDS who also have DAD appears to have increased mortality. It is possible that the response of these patients to specific therapies targeting the molecular mechanisms of ARDS may differ from patients without DAD. Therefore, it may be important to develop noninvasive methods to identify DAD. A predictive model for DAD based on noninvasive measurements has been developed in an autopsy cohort but must be validated. It would be ideal to identify biomarkers or imaging techniques that help determine which patients with ARDS have DAD. We conclude that additional studies are needed to determine the effect of DAD on outcomes in ARDS, and whether noninvasive techniques to identify DAD should be developed with the goal of determining whether this population responds differently to specific therapies targeting the molecular mechanisms of ARDS.

A usual practice in observational studies is the comparison of baseline characteristics of participants between study groups. The overall population can be grouped by clinical outcome or exposure status. A combined table reporting baseline characteristics is usually displayed, for the overall population and then separately for each group. The last column usually gives the P value for the comparison between study groups. In the conventional research model, the variables for which data are collected are limited in number.It is thus feasible to calculate descriptive data one by one and to manually create the table. The availability of EHR and big data mining techniques makes it possible to explore a far larger number of variables. However, manual tabulation of big data is particularly error prone; it is exceedingly time-consuming to create and revise such tables manually. In this paper, we introduce an R package called CBCgrps, which is designed to automate and streamline the generation of such tables when working with big data. The package contains two functions, twogrps() and multigrps(), which are used for comparisons between two and multiple groups, respectively.