European Society of Anaesthesiology.

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.

SummaryMortality after lower limb amputation is high, with UK 30-day mortality rates of 9-17%. We performed a retrospective analysis of factors affecting early and late outcome after lower limb amputation for peripheral vascular disease or diabetic complications at a UK tertiary referral vascular centre between 2003 and 2010. Three hundred and thirty-nine patients (233 male), of median (IQR [range]) age 73 (62-79 [26-92]) years underwent amputation. Thirty-day mortality was 12.4%. On regression modelling, the risk of 30-day mortality was increased in patients of ASA grade ≥ 4 (OR 4.23, 95% CI 2.07-8.63), p < 0.001 and age between 74 and 79 years (OR 3.8, 95% CI 1.10-13.13), p = 0.04 and older than 79 years (OR 4.08, 95% CI 1.25-13.25), p = 0.02. Peri-operative (30-day) mortality for these groups was 23.2%, 13.7% and 18.8%, respectively. Survival and Cox regression analysis demonstrated that long-term mortality was associated with: age 74-79 years (HR 2.15, 95% CI 1.38-3.35), p = 0.001; age > 79 years (HR 2.78, 95% CI 1.82-4.25), p < 0.001; ASA grade ≥ 4 (HR 2.04, 95% CI 1.51-2.75), p < 0.001; out-of-hours operating (HR 1.51, 95% CI 1.08-2.10), p = 0.02; and chronic kidney disease stage 4-5 (1.57, 95% CI 1.07-2.30), p = 0.02. Anaesthetic technique was associated with long-term mortality on survival analysis (p = 0.04), but not when analysed using regression modelling. Mortality after lower limb amputation relates to patient age, ASA, out-of-hours surgery and renal dysfunction. These data support lower limb amputations' being performed during daytime hours and after modification of correctable risk factors.

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.