An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression.

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.

Clinical review: beyond immediate survival from resuscitation-long-term outcome considerations after cardiac arrest.

A substantial body of literature concerning resuscitation from cardiac arrest now exists. However, not surprisingly, the greater part concerns the cardiac arrest event itself and optimising survival and outcome at relatively proximal time points. The aim of this review is to present the evidence base for interventions and therapeutic strategies that might be offered to patients surviving the immediate aftermath of a cardiac arrest, excluding components of resuscitation itself that may lead to benefits in long-term survival. In addition, this paper reviews the data on long-term impact, physical and neuropsychological, on patients and their families, revealing a burden that is often underestimated and underappreciated. As greater numbers of patients survive cardiac arrest, outcome measures more sophisticated than simple survival are required.

Validation of a classification system for causes of death in critical care: an assessment of inter-rater reliability.

OBJECTIVE: Trials in critical care have previously used unvalidated systems to classify cause of death. We aimed to provide initial validation of a method to classify cause of death in intensive care unit patients. DESIGN, SETTING AND PARTICIPANTS: One hundred case scenarios of patients who died in an ICU were presented online to raters, who were asked to select a proximate and an underlying cause of death for each, using the ICU Deaths Classification and Reason (ICU-DECLARE) system. We evaluated two methods of categorising proximate cause of death (designated Lists A and B) and one method of categorising underlying cause of death. Raters were ICU specialists and research coordinators from Australia, New Zealand and the United Kingdom. MAIN OUTCOME MEASURES: Inter-rater reliability, as measured by the Fleiss multirater kappa, and the median proportion of raters choosing the most likely diagnosis (defined as the most popular classification choice in each case). RESULTS: Across all raters and cases, for proximate cause of death List A, kappa was 0.54 (95% CI, 0.49-0.60), and for proximate cause of death List B, kappa was 0.58 (95% CI, 0.53-0.63). For the underlying cause of death, kappa was 0.48 (95% CI, 0.44-0.53). The median proportion of raters choosing the most likely diagnosis for proximate cause of death, List A, was 77.5% (interquartile range [IQR], 60.0%-93.8%), and the median proportion choosing the most likely diagnosis for proximate cause of death, List B, was 82.5% (IQR, 60.0%-92.5%). The median proportion choosing the most likely diagnosis for underlying cause was 65.0% (IQR, 50.0%-81.3%). Kappa and median agreement were similar between countries. ICU specialists showed higher kappa and median agreement than research coordinators. CONCLUSIONS: The ICU-DECLARE system allowed ICU doctors to classify the proximate cause of death of patients who died in the ICU with substantial reliability.