Patients with infection can develop sepsis, and their mortality can be high. An important aspect in the treatment of sepsis is adequate management of the infection.

Data sources

Using a modified Delphi approach, the Surviving Sepsis Campaign research committee recently published a series of 26 priorities for sepsis and septic shock.

Study selection

Task force members with specific expertise were tasked with generating expanded reviews for all infection questions and a subset of adjunctive therapy questions from the larger list of sepsis priorities. Each question was addressed by one of the six task force members.

Data extraction

In-depth reviews were then edited by the group as a whole, with added input from the committee cochairs.

Data synthesis

Six questions were addressed: 1) should empiric antibiotic combination therapy be used in sepsis or septic shock? 2) does optimization of antimicrobial pharmacokinetics and pharmacodynamics impact patient outcomes in sepsis? 3) should viral reactivation resulting from sepsis-induced immunosuppression be treated with antiviral therapy in critically ill septic patients? 4) should rapid diagnostic tests be implemented in clinical practice? 5) what is the role of lung-protective ventilation in sepsis patients without acute respiratory distress syndrome? and 6) how do we determine the efficacy of "blood purification" therapies such as endotoxin absorbers, cytokine absorbers, and plasmapheresis.

Conclusions

The research committee members for the Surviving Sepsis Campaign aimed to explore research questions in order to provide existing evidence and highlight areas of uncertainty and future directions.

Objective

To identify research priorities in the management, epidemiology, outcome and underlying causes of sepsis and septic shock.

Design

A consensus committee of 16 international experts representing the European Society of Intensive Care Medicine and Society of Critical Care Medicine was convened at the annual meetings of both societies. Subgroups had teleconference and electronic-based discussion. The entire committee iteratively developed the entire document and recommendations.

Methods

Each committee member independently gave their top five priorities for sepsis research. A total of 88 suggestions (ESM 1 - supplemental table 1) were grouped into categories by the committee co-chairs, leading to the formation of seven subgroups: infection, fluids and vasoactive agents, adjunctive therapy, administration/epidemiology, scoring/identification, post-intensive care unit, and basic/translational science. Each subgroup had teleconferences to go over each priority followed by formal voting within each subgroup. The entire committee also voted on top priorities across all subgroups except for basic/translational science.

Results

The Surviving Sepsis Research Committee provides 26 priorities for sepsis and septic shock. Of these, the top six clinical priorities were identified and include the following questions: (1) can targeted/personalized/precision medicine approaches determine which therapies will work for which patients at which times?; (2) what are ideal endpoints for volume resuscitation and how should volume resuscitation be titrated?; (3) should rapid diagnostic tests be implemented in clinical practice?; (4) should empiric antibiotic combination therapy be used in sepsis or septic shock?; (5) what are the predictors of sepsis long-term morbidity and mortality?; and (6) what information identifies organ dysfunction?

Conclusions

While the Surviving Sepsis Campaign guidelines give multiple recommendations on the treatment of sepsis, significant knowledge gaps remain, both in bedside issues directly applicable to clinicians, as well as understanding the fundamental mechanisms underlying the development and progression of sepsis. The priorities identified represent a roadmap for research in sepsis and septic shock.

Background

Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS.

Methods

In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable.

Findings

The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90-0·95) in EARLI and 0·88 (0·84-0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81-0·94] vs 0·92 [0·88-0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group).

Interpretation

Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated.

Funding

US National Institutes of Health and European Society of Intensive Care Medicine.