UK Efficacy and Mechanism Evaluation Programme and National Institutes of Health.

SummarySleep deprivation is common among intensive care patients and may be associated with delirium. We investigated whether the implementation of a bundle of non-pharmacological interventions, consisting of environmental noise and light reduction designed to reduce disturbing patients during the night, was associated with improved sleep and a reduced incidence of delirium. The study was divided into two parts, before and after changing our practice. One hundred and sixty-seven and 171 patients were screened for delirium pre-and post-intervention, respectively. Compliance with the interventions was > 90%. The bundle of interventions led to an increased mean (SD) sleep efficiency index (60.8 (3.5) before vs 75.9 (2.2) after, p = 0.031); reduced mean sound (68.8 (4.2) dB before vs 61.8 (9.1) dB after, p = 0.002) and light levels (594 (88.2) lux before vs 301 (53.5) lux after, p = 0.003); and reduced number of awakenings caused by care activities overnight (11.0 (1.1) before vs 9.0 (1.2) after, p = 0.003). In addition, the introduction of the care bundle led to a reduced incidence of delirium (55/167 (33%) before vs 24/171 (14%) after, p < 0.001), and less time spent in delirium (3.4 (1.4) days before vs 1.2 (0.9) days after, p = 0.021). Increases in sleep efficiency index were associated with a lower odds ratio of developing delirium (OR 0.90,.

Patients exposed to a surgical safety checklist experience better postoperative outcomes, but this could simply reflect wider quality of care in hospitals where checklist use is routine.

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.