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The ideal method of identifying frailty is uncertain, and data on long-term outcomes is relatively limited. We examined frailty indices derived from population-scale linked data on Intensive Care Unit (ICU) and hospitalised non-ICU patients with pneumonia to elucidate the influence of frailty on mortality. Longitudinal cohort study between 2010–2018 using population-scale anonymised data linkage of healthcare records for adults admitted to hospital with pneumonia in Wales. Primary outcome was in-patient mortality. Odds Ratios (ORs [95% confidence interval]) for age, hospital frailty risk score (HFRS), electronic frailty index (eFI), Charlson comorbidity index (CCI), and social deprivation index were estimated using multivariate logistic regression models. The area under the receiver operating characteristic curve (AUC) was estimated to determine the best fitting models. Of the 107,188 patients, mean (SD) age was 72.6 (16.6) years, 50% were men. The models adjusted for the two frailty indices and the comorbidity index had an increased odds of in-patient mortality for individuals with an ICU admission (ORs for ICU admission in the eFI model 2.67 [2.55, 2.79], HFRS model 2.30 [2.20, 2.41], CCI model 2.62 [2.51, 2.75]). Models indicated advancing age, increased frailty and comorbidity were also associated with an increased odds of in-patient mortality (eFI, baseline fit, ORs: mild 1.09 [1.04, 1.13], moderate 1.13 [1.08, 1.18], severe 1.17 [1.10, 1.23]. HFRS, baseline low, ORs: intermediate 2.65 [2.55, 2.75], high 3.31 [3.17, 3.45]). CCI, baseline < 1, ORs: ‘1–10′ 1.15 [1.11, 1.20], > 10 2.50 [2.41, 2.60]). For predicting inpatient deaths, the CCI and HFRS based models were similar, however for longer term outcomes the CCI based model was superior. Frailty and comorbidity are significant risk factors for patients admitted to hospital with pneumonia. Frailty and comorbidity scores based on administrative data have only moderate ability to predict outcome.

There is a need to better define the epidemiology of sepsis in intensive care units (ICUs) around the globe. The Intensive Care over Nations (ICON) audit prospectively collected data on all adult (>16 years) patients admitted to the ICU between May 8 and May 18, 2012, except those admitted for less than 24 hours for routine postoperative surveillance. Data were collected daily for a maximum of 28 days in the ICU, and patients were followed up for outcome data until death, hospital discharge, or for 60 days. Participation was entirely voluntary. The audit included 10069 patients from Europe (54.1%), Asia (19.2%), America (17.1%), and other continents (9.6%). Sepsis, defined as infection with associated organ failure, was identified during the ICU stay in 2973 (29.5%) patients, including in 1808 (18.0%) already at ICU admission. Occurrence rates of sepsis varied from 13.6% to 39.3% in the different regions. Overall ICU and hospital mortality rates were 25.8% and 35.3%, respectively, in patients with sepsis, but it varied from 11.9% and 19.3% (Oceania) to 39.5% and 47.2% (Africa), respectively. After adjustment for possible confounders in a multilevel analysis, independent risk factors for in-hospital death included older age, higher simplified acute physiology II score, comorbid cancer, chronic heart failure (New York Heart Association Classification III/IV), cirrhosis, use of mechanical ventilation or renal replacement therapy, and infection with spp. Sepsis remains a major health problem in ICU patients worldwide and is associated with high mortality rates. However, there is wide variability in the sepsis rate and outcomes in ICU patients around the globe.

Early diagnosis of sepsis and discrimination from SIRS is crucial for clinicians to provide appropriate care, management and treatment to critically ill patients. We describe identification of mRNA biomarkers from peripheral blood leukocytes, able to identify severe, systemic inflammation (irrespective of origin) and differentiate Sepsis from SIRS, in adult patients within a multi-center clinical study. Participants were recruited in Intensive Care Units (ICUs) from multiple UK hospitals, including fifty-nine patients with abdominal sepsis, eighty-four patients with pulmonary sepsis, forty-two SIRS patients with Out-of-Hospital Cardiac Arrest (OOHCA), sampled at four time points, in addition to thirty healthy control donors. Multiple clinical parameters were measured, including SOFA score, with many differences observed between SIRS and sepsis groups. Differential gene expression analyses were performed using microarray hybridization and data analyzed using a combination of parametric and non-parametric statistical tools. Nineteen high-performance, differentially expressed mRNA biomarkers were identified between control and combined SIRS/Sepsis groups (FC>20.0, p<0.05), termed 'indicators of inflammation' (I°I), including CD177, FAM20A and OLAH. Best-performing minimal signatures e.g. FAM20A/OLAH showed good accuracy for determination of severe, systemic inflammation (AUC>0.99). Twenty entities, termed 'SIRS or Sepsis' (S°S) biomarkers, were differentially expressed between sepsis and SIRS (FC>2·0, p-value<0.05). The best performing signature for discriminating sepsis from SIRS was CMTM5/CETP/PLA2G7/MIA/MPP3 (AUC=0.9758). The I°I and S°S signatures performed variably in other independent gene expression datasets, this may be due to technical variation in the study/assay platform.

Stress hyperglycaemia is common in critical illness. We have previously observed that increasing severity of respiratory failure in patients with severe COVID-19 is associated with increased insulin demand. Given previously reported direct effects of hypoxia on insulin action, we reasoned that rapid improvements in oxygenation following prone positioning may improve insulin sensitivity and increase risk of hypoglycaemia. A retrospective multi-centre service evaluation comparing blood glucose and insulin administration in patients with COVID-19 pneumonitis receiving prone mechanical ventilation, comparing the 16 h pre-prone and 16 h post-prone time periods. 155 patients were included in this analysis. Oxygenation improved significantly following prone positioning (change in SpO 2 /FIO 2 per hour prone: 3.01 ± 0.14, P  < 0.0001). Glycaemic control was similar during the supine and prone study periods, and there were no hypoglycaemic events in the prone study period. Prone positioning was associated with an unexpected modest but significant increase in insulin requirements (mean difference in total insulin dose (IU): 8.32 ± 2.14, P  < 0.001) that was robust to several sensitivity analyses, and could not be explained by changes in carbohydrate intake. We did not observe an increased rate of hypoglycaemia during prone ventilation and the adequacy of glycaemic control was comparable during the supine and prone study periods. Unexpectedly, prone ventilation was associated with an increase in insulin requirements despite significant improvement in hypoxaemia. Our findings support the safety of prone ventilation with respect to glycaemic control and identify a novel relationship between ventilation position and insulin requirements in critical illness.

Sepsis is defined as dysregulated host response caused by systemic infection, leading to organ failure. It is a life-threatening condition, often requiring admission to an intensive care unit (ICU). The causative agents and processes involved are multifactorial but are characterized by an overarching inflammatory response, sharing elements in common with severe inflammatory response syndrome (SIRS) of non-infectious origin. Sepsis presents with a range of pathophysiological and genetic features which make clinical differentiation from SIRS very challenging. This may reflect a poor understanding of the key gene inter-activities and/or pathway associations underlying these disease processes. Improved understanding is critical for early differential recognition of sepsis and SIRS and to improve patient management and clinical outcomes. Judicious selection of gene biomarkers suitable for development of diagnostic tests/testing could make differentiation of sepsis and SIRS feasible. Here we describe a methodologic framework for the identification and validation of biomarkers in SIRS, sepsis and septic shock patients, using a 2-tier gene screening, artificial neural network (ANN) data mining technique, using previously published gene expression datasets. Eight key hub markers have been identified which may delineate distinct, core disease processes and which show potential for informing underlying immunological and pathological processes and thus patient stratification and treatment. These do not show sufficient fold change differences between the different disease states to be useful as primary diagnostic biomarkers, but are instrumental in identifying candidate pathways and other associated biomarkers for further exploration.

In critically ill patients with COVID-19, concomitant abnormalities of coagulation have been seen with an unusually high incidence, often despite seemingly appropriate prophylactic anti-coagulation. It appears that standard coagulation tests are limited in their ability to accurately reflect the severity of the prothrombotic phenotype observed in severe COVID-19 infections. In this narrative review we consider the role of a global haemostatic assay, rotational thromboelastometry (ROTEM), as a near bedside test allowing a more comprehensive assessment of haemostatic function in the context of COVID-19 infection. A comprehensive literature search was conducted on PubMed using the keywords “COVID-19” OR “SARS-CoV-2” AND “Rotational thromboelastometry”. Sixteen original articles were included for analysis and two existing literature reviews were considered. Whilst not the perfect substitute for in vivo coagulation, studies utilising rotational thromboelastometry assays in COVID-19 patients have demonstrated increased maximum clot firmness (consistent with hypercoagulability) and reduced maximum lysis (consistent with “fibrinolytic shutdown”). There is a possible association with disease severity and degree of hypercoagulability and hypofibrinolysis as a possible tool for risk stratification and the potential modulation of fibrinogen-dependent maximum clot firmness with enhanced anticoagulation strategies. Precisely how these coagulation abnormalities can be modified by optimum, individualised medical interventions to improve clinical outcomes, however, remains unclear.

On the 9th March 2020, the first patient with COVID-19 was admitted to ICU in the Royal Gwent Hospital (RGH), Newport, Wales. We prospectively recorded the rate of ICU admissions of 52 patients with COVID-19 over 60 days, focusing on the epidemiology of ethnicity and deprivation because these factors have emerged as significant risk factors. Patients were 65% (34 of 52) male and had a median (IQR) age of 55 (48–62) years. Prevalent comorbidities included obesity (52%); diabetes (33%), and asthma (23%). COVID-19 hospital and ICU inpatient numbers peaked on days 23 and 39, respectively—a lag of 16 days. The ICU mortality rate was 33% (17 of 52). People of black, Asian, and minority ethnic descent (BAME group) represented 35% of ICU COVID-19 admissions (18 of 52) and 35% of deaths (6 of 17). Amongst the BAME group, 72% (13 of 18) of patients were found to reside in geographical areas representing the 20% most deprived in Wales, vs. 27% of patients in the Caucasian group (9 of 33). Less than 5% of the population within the area covered by RGH are of BAME descent, yet this group had a disproportionately high ICU admission and mortality rate from COVID-19. The interplay between ethnicity and deprivation, which is complex, may be a factor in our findings. This in turn could be related to an increased prevalence of co-morbidities; higher community exposure; larger proportion of lower band key worker roles; or genetic polymorphisms.

Acute respiratory distress syndrome (ARDS) is a common and highly heterogeneous condition in the critically ill. The association between hyper- and hypo-inflammatory subphenotypes and clinical outcomes has generated significant interest in precise ARDS management. The value of identifying biomarkers to guide treatment and enrollment in future ARDS trials is undisputable. We describe multiple factors complicating the search for subphenotypes and their treatable traits. The observed heterogeneity seen in the clinical course of ARDS is dynamic and influenced by factors beyond lung pathophysiology, including variations in the delivery of best critical care practices, patient comorbidities, and functional status, and patient or family preferences. Current subphenotype definitions lack strong biological plausibility and without clear evidence of benefit from targeted treatments, their use in clinical practice is currently unwarranted.

Atelectasis caused by lung injury leads to increased intrapulmonary shunt, venous admixture, and hypoxaemia. Lung recruitment manoeuvres aim to quickly reverse this scenario by applying increased airway pressures for a short period of time which meant to open the collapsed alveoli. Although the procedure can improve oxygenation, but due to the heart-lung and right and left ventricle interactions elevated intrathoracic pressures can inflict serious effects on the cardiovascular system. The purpose of this paper is to give an overview on the pathophysiological background of the heart-lung interactions and the best way to monitor these changes during lung recruitment.

IntroductionThe emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and control the spread of infection in the general and vulnerable populations. Rapid analyses are needed to inform policy development and target interventions to at-risk groups to prevent serious health outcomes. We aim to provide an accessible research platform to determine demographic, socioeconomic and clinical risk factors for infection, morbidity and mortality of COVID-19, to measure the impact of COVID-19 on healthcare utilisation and long-term health, and to enable the evaluation of natural experiments of policy interventions.Methods and analysisTwo privacy-protecting population-level cohorts have been created and derived from multisourced demographic and healthcare data. The C20 cohort consists of 3.2 million people in Wales on the 1 January 2020 with follow-up until 31 May 2020. The complete cohort dataset will be updated monthly with some individual datasets available daily. The C16 cohort consists of 3 million people in Wales on the 1 January 2016 with follow-up to 31 December 2019. C16 is designed as a counterfactual cohort to provide contextual comparative population data on disease, health service utilisation and mortality. Study outcomes will: (a) characterise the epidemiology of COVID-19, (b) assess socioeconomic and demographic influences on infection and outcomes, (c) measure the impact of COVID-19 on short -term and longer-term population outcomes and (d) undertake studies on the transmission and spatial spread of infection.Ethics and disseminationThe Secure Anonymised Information Linkage-independent Information Governance Review Panel has approved this study. The study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.