Although its original clinical description dates from 1873, 1 fat embolism syndrome remains a diagnostic challenge for clinicians. The term fat embolism indicates the often asymptomatic presence of fat globules in the lung parenchyma and peripheral circulation after long bone or other major trauma. The majority (95%) of cases occur after major trauma. Fat embolism syndrome is a serious consequence of fat emboli producing a distinct pattern of clinical symptoms and signs. It is most commonly associated with fractures of long bones and the pelvis, and is more frequent in closed, rather than open, fractures. The incidence increases with the number of fractures involved. Thus, patients with a single long bone fracture have a 1-3% chance of developing the syndrome, but it has been reported in up to 33% of patients with bilateral femoral fractures. 2 Fat embolism syndrome can also occur in relation to other trauma, for example, soft tissue injury, liposuction, bone marrow harvest (Table 1). Non-trauma-related causes (e.g. acute pancreatitis, sickling crisis) are less likely to lead to fat embolism syndrome compared with those associated with trauma. An overall mortality of 5-15% has been described. 3 Clinical presentation Fat embolism syndrome typically presents 24-72 h after the initial injury. Rarely, cases occur as early as 12 h or as much as 2 weeks later. 4 Patients present with a classic triad: (i) respiratory changes; (ii) neurological abnormalities; (iii) petechial rash.

Common causes of death in COVID-19 due to SARS-CoV-2 include thromboembolic disease, cytokine storm and adult respiratory distress syndrome (ARDS). Our aim was to develop a system for early detection of disease pattern in the emergency department (ED) that would enhance opportunities for personalised accelerated care to prevent disease progression. A single Trust’s COVID-19 response control command was established, and a reporting team with bioinformaticians was deployed to develop a real-time traffic light system to support clinical and operational teams. An attempt was made to identify predictive elements for thromboembolism, cytokine storm and ARDS based on physiological measurements and blood tests, and to communicate to clinicians managing the patient, initially via single consultants. The input variables were age, sex, and first recorded blood pressure, respiratory rate, temperature, heart rate, indices of oxygenation and C-reactive protein. Early admissions were used to refine the predictors used in the traffic lights. Of 923 consecutive patients who tested COVID-19 positive, 592 (64%) flagged at risk for thromboembolism, 241/923 (26%) for cytokine storm and 361/923 (39%) for ARDS. Thromboembolism and cytokine storm flags were met in the ED for 342 (37.1%) patients. Of the 318 (34.5%) patients receiving thromboembolism flags, 49 (5.3% of all patients) were for suspected thromboembolism, 103 (11.1%) were high-risk and 166 (18.0%) were medium-risk. Of the 89 (9.6%) who received a cytokine storm flag from the ED, 18 (2.0% of all patients) were for suspected cytokine storm, 13 (1.4%) were high-risk and 58 (6.3%) were medium-risk. Males were more likely to receive a specific traffic light flag. In conclusion, ED predictors were used to identify high proportions of COVID-19 admissions at risk of clinical deterioration due to severity of disease, enabling accelerated care targeted to those more likely to benefit. Larger prospective studies are encouraged.

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Background: COVID-19 is a global health emergency. Recent data indicate a 50% mortality rate across UK intensive care units.Methods: A single institution, two-centre retrospective analysis following implementation of a Decision Support tool and real-time data dashboard for early detection of patients requiring personalised enhanced care, focussing on respiratory rate, diastolic blood pressure, oxygenation indices, C-reactive protein, D-dimer and ferritin. Protocols differing from conventional practice included high-dose prophylactic anticoagulation for all COVID-19 positive patients and prescription of antioxidants.Results: By 22/04/2020, 923 patients tested COVID-19 positive. 569 patients (61.7%) were male. The majority presented with advanced disease: interquartile ranges were C-reactive protein 44.9-179mg/L, D-dimer 1070-3802ng/mL, and ferritin 261-1208μg/L. Completed case fatality rates were 25.1% [95% CI 20.0, 30.0] in females, 40.5% [95% CI 35.9, 45.0] in males. 139 patients were admitted to intensive care where current death rates are 16.2% [95% CI 3.8, 28.7] in females, 38.2% [95% CI 28.6, 47.8] inmales with no trends for differences based on ethnicity. A real-time traffic lights dashboard enabled rapid assessment of patients using critical parameters to accelerate adjustments to management protocols. In total 513 (55.6%) of patients were flagged as high risk for thromboembolic disease, exceeding the numbers flagged for respiratory deteriorations (N=391, 42.4%), or cytokine storm (N=68, 7.4%). There was minimal evidence that age was associated with disease severity, but males had higher levels of all dashboard indices, particularly C-reactive protein and ferritin (p<0.0001) which displayed no relationship with age.Conclusions: Survival rates are encouraging. Protocols employed (traffic light-driven personalised care, protocolised early therapeutic anticoagulation based on D-dimer >1,000ng/mL and/or CRP>200 mg/L, personalised ventilatory strategies and antioxidants) are recommended to other units. Males are at greater risk of severe disease, most likely as the obligate SARS-CoV-2 receptor is encoded by the Xchromosome, and require especially close, and early attention.

IntroductionAutomated systems for ventilator management to date have been either fully heuristic rule-based systems or based on a combination of simple physiological models and rules. These have been shown to reduce the duration of mechanical ventilation in simple to wean patients. At present, there are no published studies that evaluate the effect of systems that use detailed physiological descriptions of the individual patient.The BEACON Caresystem is a model-based decision support system that uses mathematical models of patients’ physiology in combination with models of clinical preferences to provide advice on appropriate ventilator settings. An individual physiological description may be particularly advantageous in selecting the appropriate therapy for a complex, heterogeneous, intensive care unit (ICU) patient population.Methods and analysisIntenive Care weaning (iCareWean) is a single-blinded, multicentre, prospective randomised control trial evaluating management of mechanical ventilation as directed by the BEACON Caresystem compared with that of current care, in the general intensive care setting. The trial will enrol 274 participants across multiple London National Health Service ICUs. The trial will use a primary outcome of duration of mechanical ventilation until successful extubation.Ethics and disseminationSafety oversight will be under the direction of an independent committee of the study sponsor. Study approval was obtained from the regional ethics committee of the Health Research Authority (HRA), (Research Ethic Committee (REC) reference: 17/LO/0887. Integrated Research Application System (IRAS) reference: 226610. Results will be disseminated through international critical care conference/symposium and publication in peer-reviewed journal.Trial registration numberClinicalTrials.gov under NCT03249623. This research is registered with the National Institute for Health Research under CPMS ID: 34831.