Acute respiratory distress syndrome (ARDS) remains a major cause of morbidity and mortality in critically ill patients. Over the past several decades, alcohol abuse and cigarette smoke exposure have been identified as risk factors for the development of ARDS. The mechanisms underlying these relationships are complex and remain under investigation but are thought to involve pulmonary immune impairment and alveolar epithelial and endothelial dysfunction. This review summarizes the epidemiologic data supporting links between these exposures and ARDS susceptibility and outcomes and highlights key mechanistic investigations that provide insight into the pathways by which each exposure is linked to ARDS.

In the 50 years since acute respiratory distress syndrome (ARDS) was first described, substantial progress has been made in identifying the risk factors for and the pathogenic contributors to the syndrome and in characterising the protein expression patterns in plasma and bronchoalveolar lavage fluid from patients with ARDS. Despite this effort, however, pharmacological options for ARDS remain scarce. Frequently cited reasons for this absence of specific drug therapies include the heterogeneity of patients with ARDS, the potential for a differential response to drugs, and the possibility that the wrong targets have been studied. Advances in applied biomolecular technology and bioinformatics have enabled breakthroughs for other complex traits, such as cardiovascular disease or asthma, particularly when a precision medicine paradigm, wherein a biomarker or gene expression pattern indicates a patient's likelihood of responding to a treatment, has been pursued. In this Review, we consider the biological and analytical techniques that could facilitate a precision medicine approach for ARDS.

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2020. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2020. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901.

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2023. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2023 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901 .

Latent class analysis is a probabilistic modeling algorithm that allows clustering of data and statistical inference. There has been a recent upsurge in the application of latent class analysis in the fields of critical care, respiratory medicine, and beyond. In this review, we present a brief overview of the principles behind latent class analysis. Furthermore, in a stepwise manner, we outline the key processes necessary to perform latent class analysis including some of the challenges and pitfalls faced at each of these steps. The review provides a one-stop shop for investigators seeking to apply latent class analysis to their data.

Acute respiratory distress syndrome (ARDS) is an acute respiratory illness characterised by bilateral chest radiographical opacities with severe hypoxaemia due to non-cardiogenic pulmonary oedema. The COVID-19 pandemic has caused an increase in ARDS and highlighted challenges associated with this syndrome, including its unacceptably high mortality and the lack of effective pharmacotherapy. In this Seminar, we summarise current knowledge regarding ARDS epidemiology and risk factors, differential diagnosis, and evidence-based clinical management of both mechanical ventilation and supportive care, and discuss areas of controversy and ongoing research. Although the Seminar focuses on ARDS due to any cause, we also consider commonalities and distinctions of COVID-19-associated ARDS compared with ARDS from other causes.

Purpose

We recently identified interleukin-27 (IL-27) as a sepsis diagnostic biomarker in children. Here we assess IL-27 as a sepsis diagnostic biomarker in critically ill adults with systemic inflammatory response syndrome and sepsis.

Methods

IL-27 and procalcitonin (PCT) were measured from plasma samples in three groups: no sepsis (n = 78), pulmonary source of sepsis (n = 66), and non-pulmonary source of sepsis (n = 43). Receiver operating characteristic curves and classification and regression tree methodology were used to evaluate biomarker performance.

Results

IL-27 did not discriminate effectively between sepsis and sterile systemic inflammatory response syndrome in unselected patients. The highest area under the curve (AUC) was 0.70 (95% C.I. 0.60 - 0.80) for IL-27 in subjects with a non-pulmonary source of sepsis. A decision tree incorporating IL-27, PCT, and age had an AUC of 0.79 (0.71-0.87) in subjects with a non-pulmonary source of sepsis. Compared to children with sepsis, adults with sepsis express less IL-27.

Conclusions

IL-27 performed overall poorly in this cohort as a sepsis diagnostic biomarker. Combining IL-27, PCT, and age reasonably estimated the risk of sepsis in subjects with a non-pulmonary source of sepsis. IL-27 may be a more reliable sepsis diagnostic biomarker in children than in adults.

Background

Using latent class analysis (LCA) in five randomised controlled trial (RCT) cohorts, two distinct phenotypes of acute respiratory distress syndrome (ARDS) have been identified: hypoinflammatory and hyperinflammatory. The phenotypes are associated with differential outcomes and treatment response. The objective of this study was to develop parsimonious models for phenotype identification that could be accurate and feasible to use in the clinical setting.

Methods

In this retrospective study, three RCT cohorts from the National Lung, Heart, and Blood Institute ARDS Network (ARMA, ALVEOLI, and FACTT) were used as the derivation dataset (n=2022), from which the machine learning and logistic regression classifer models were derived, and a fourth (SAILS; n=715) from the same network was used as the validation test set. LCA-derived phenotypes in all of these cohorts served as the reference standard. Machine-learning algorithms (random forest, bootstrapped aggregating, and least absolute shrinkage and selection operator) were used to select a maximum of six important classifier variables, which were then used to develop nested logistic regression models. Only cases with complete biomarker data in the derivation dataset were used for variable selection. The best logistic regression models based on parsimony and predictive accuracy were then evaluated in the validation test set. Finally, the models' prognostic validity was tested in two external ARDS clinical trial datasets (START and HARP-2) by assessing mortality at days 28, 60, and 90 and ventilator-free days to day 28.

Findings

The six most important classifier variables were interleukin (IL)-8, IL-6, protein C, soluble tumour necrosis factor receptor 1, bicarbonate, and vasopressor use. From the nested models, three-variable (IL-8, bicarbonate, and protein C) and four-variable (3-variable plus vasopressor use) models were adjudicated to be the best performing. In the validation test set, both models showed good accuracy (AUC 0·94 [95% CI 0·92-0·95] for the three-variable model and 0·95 [95% CI 0·93-0·96] for the four-variable model) against LCA classifications. As with LCA-derived phenotypes, the hyperinflammatory phenotype as identified by the classifier model was associated with higher mortality at day 90 (87 [39%] of 223 patients vs 112 [23%] of 492 patients; p<0·0001) and fewer ventilator-free days (median 14 days [IQR 0-22] vs 22 days [0-25]; p<0·0001). In the external validation datasets, three-variable models developed in the derivation dataset identified two phenotypes with distinct clinical features and outcomes consistent with previous findings, including differential survival with simvastatin versus placebo in HARP-2 (p=0·023 for survival at 28 days).

Interpretation

ARDS phenotypes can be accurately identified with parsimonious classifier models using three or four variables. Pending the development of real-time testing for key biomarkers and prospective validation, these models could facilitate identification of ARDS phenotypes to enable their application in clinical trials and practice.

Funding

National Institutes of Health.

OBJECTIVES:Early identification of sepsis is critical to improving patient outcomes. Impact of the new sepsis definition (Sepsis-3) on timing of recognition in the emergency department has not been evaluated. Our study objective was to compare time to meeting systemic inflammatory response syndrome (Sepsis-2) criteria, Sequential Organ Failure Assessment (Sepsis-3) criteria, and quick Sequential Organ Failure Assessment criteria using electronic health record data. DESIGN:Retrospective, observational study. SETTING:The emergency department at the University of California, San Francisco. PATIENTS:Emergency department encounters between June 2012 and December 2016 for patients greater than or equal to 18 years old with blood cultures ordered, IV antibiotic receipt, and identification with sepsis via systemic inflammatory response syndrome or Sequential Organ Failure Assessment within 72 hours of emergency department presentation. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:We analyzed timestamped electronic health record data from 16,612 encounters identified as sepsis by greater than or equal to 2 systemic inflammatory response syndrome criteria or a Sequential Organ Failure Assessment score greater than or equal to 2. The primary outcome was time from emergency department presentation to meeting greater than or equal to 2 systemic inflammatory response syndrome criteria, Sequential Organ Failure Assessment greater than or equal to 2, and/or greater than or equal to 2 quick Sequential Organ Failure Assessment criteria. There were 9,087 patients (54.7%) that met systemic inflammatory response syndrome-first a median of 26 minutes post-emergency department presentation (interquartile range, 0-109 min), with 83.1% meeting Sequential Organ Failure Assessment criteria a median of 118 minutes later (interquartile range, 44-401 min). There were 7,037 patients (42.3%) that met Sequential Organ Failure Assessment-first, a median of 113 minutes post-emergency department presentation (interquartile range, 60-251 min). Quick Sequential Organ Failure Assessment was met in 46.4% of patients a median of 351 minutes post-emergency department presentation (interquartile range, 67-1,165 min). Adjusted odds of in-hospital mortality were 39% greater in patients who met systemic inflammatory response syndrome-first compared with those who met Sequential Organ Failure Assessment-first (odds ratio, 1.39; 95% CI, 1.20-1.61). CONCLUSIONS:Systemic inflammatory response syndrome and Sequential Organ Failure Assessment initially identified distinct populations. Using systemic inflammatory response syndrome resulted in earlier electronic health record sepsis identification in greater than 50% of patients. Using Sequential Organ Failure Assessment alone may delay identification. Using systemic inflammatory response syndrome alone may lead to missed sepsis presenting as acute organ dysfunction. Thus, a combination of inflammatory (systemic inflammatory response syndrome) and organ dysfunction (Sequential Organ Failure Assessment) criteria may enhance timely electronic health record-based sepsis identification.