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Introduction: National Service Evaluations of COVID-19 ARDS care in the US and UK showed significant variability in clinical practice, and adherence to existing guidelines. To better understand the basis for this, we explored factors influencing decision-making around mechanical ventilation in COVID-19. Method(s): We conducted interprofessional focus groups identifying factors that influenced decision-making through thematic analysis. From this, we developed a questionnaire to validate these themes with a larger sample of critical care professionals across the UK. Kruskal- Wallis or Mann-Whitney U tests were used for data analysis. Result(s): There were 179 complete responses from doctors, nurses and physios. In their usual practice, 66% of clinicians reported adherence to national ARDS guidelines. However, 80% thought COVID-19 ARDS presented differently to their previous clinical knowledge/experience of ARDS and 72% thought deviating from usual practice was necessary. Doctors were more likely to think deviation was necessary (p < 0.001) but there was no difference across level of ICU experience (p = 0.845). Clinicians reported their ventilatory decision-making was most influenced by disease factors, followed by team then contextual and least by environmental factors (p < 0.001). Disease factor was seen as most important across profession and experience level. During COVID-19, 68% of clinicians reported not being confident in their ventilatory decision-making;however, clinicians who felt COVID-19 ARDS presentation fitted with their previous clinical knowledge/experience of ARDS reported greater confidence (p < 0.001). Confidence was not affected by experience (p = 0.522) or profession (p = 0.294) (Fig. 1). Conclusion(s): Clinicians were influenced by the uncertain understanding of COVID-19 ARDS, especially when they considered previous experiences to be less relevant. In the event of another novel disease, developing a consistent, understandable clinical models of disease should be prioritised to optimise decision making.
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BACKGROUND AND AIM: The COVID-19 pandemic highlighted adverse outcomes in Asian, Black, and ethnic minority groups. More research is required to explore underlying ethnic health inequalities. In this study, we aim to examine pre-COVID ethnic inequalities more generally through healthcare utilisation to contextualise underlying inequalities that were present before the pandemic. DESIGN: This was an ecological study exploring all admissions to NHS hospitals in England from 2017 to 2020. METHODS: The primary outcomes were admission rates within ethnic groups. Secondary outcomes included age-specific and age-standardised admission rates. Sub-analysis of admission rates across an index of multiple deprivation (IMD) deciles was also performed to contextualise the impact of socioeconomic differences amongst ethnic categories. Results were presented as a relative ratio (RR) with 95% confidence intervals. RESULTS: Age-standardised admission rates were higher in Asian (RR 1.40 [1.38-1.41] in 2019) and Black (RR 1.37 [1.37-1.38]) and lower in Mixed groups (RR 0.91 [0.90-0.91]) relative to White. There was significant missingness or misassignment of ethnicity in NHS admissions: with 11.7% of admissions having an unknown/not-stated ethnicity assignment and 'other' ethnicity being significantly over-represented. Admission rates did not mirror the degree of deprivation across all ethnic categories. CONCLUSIONS: This study shows Black and Asian ethnic groups have higher admission rates compared to White across all age groups and when standardised for age. There is evidence of incomplete and misidentification of ethnicity assignment in NHS admission records, which may introduce bias to work on these datasets. Differences in admission rates across individual ethnic categories cannot solely be explained by socioeconomic status. Further work is needed to identify ethnicity-specific factors of these inequalities to allow targeted interventions at the local level.
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Introduction: Patients with Black and Asian ethnic background have been disproportionately affected by COVID-19 with increased disease severity, organ failure, intensive care admission, and premature mortality. 1-3 The urea-to-creatinine ratio (UCR) has been described as a biochemical signature of persistent critical illness, its hallmark catabolic state and late mortality during prolonged ICU stay.4 Low serum creatinine reflecting reduced muscle mass, which declines rapidly in acute severe illness in combination with net muscle protein breakdown which contribute substrate for increased hepatic urea synthesis, results in markedly elevated UCR. Objectives: To assess UCR as a candidate biological feature driving ethnicity associated outcomes of COVID-19 disease. Methods: Prospective analysis using registry data from all patients aged ≥16 years with an emergency admission to hospitals within Barts Health NHS Trust with SARSCoV-2 infection during 1 January 2020 - 13 May 2020 (wave 1), and 1 September 2020 -17 February 2021 (wave 2). Trajectories of routine haematology and clinical biochemistry blood results during hospital admission were assessed, and distinct phenotypes defined using unsupervised longitudinal clustering techniques using day 0 to 15 results.We determined distribution of identified phenotypes within patients categorised by ethnic group. Multivariable logistic regression accounting for predefined baseline risk factors was used to assess association between ethnicity, phenotypes, and 30-day mortality. All analyses were performed using R software v4.02 and the kml package for clustering. 5 Results are presented as n (%) and adjusted odds ratios (OR) with 95% confidence intervals. Results: We assessed 459 (wave 1) and 1337 (wave 2) patients after excluding those with unknown ethnicity and those with <7 blood results. Three clusters were identified based on trajectories of UCR. In wave 1, 48.1% of patients had persistently low levels of UCR (A), 38.6% had higher but stable levels (B), and 13.3% had the highest levels peaking after day 7 (C). In wave 2, three clusters were identified in similar proportions: 42.8% (a), 45.1% (b), 12.1% (c). In wave 1, patients in cluster C compared to A had the highest risk of death at 30 days (OR 4.59 [2.27-9.26], p<0.001). In wave 2, both clusters b (OR 1.58 [1.18-2.12], p< 0.001) and c (OR 3.96 [2.62-5.99], p<0.001) had higher risk of death compared to a. Distribution of cluster membership varied by ethnic category. In both waves, greater proportions of patients within cluster A/a were observed in patients with Black ethnicity (65.5% wave 1, 61.1% wave 2) compared to Asian (50.0% wave 1, 37.3% wave 2) and White (39.7% wave 1, 39.6% wave 2) ethnicity. Black ethnicity patients also had lowest proportions in cluster C/c (6.9% wave 1, 6.3% wave 2) compared to Asian (17.4% wave 1, 14.2% wave 2) and White (13.2% wave 1, 12.9% wave 2) ethnicity. Inclusion of UCR trajectory attenuated the higher risk of death seen in Asian patients in wave 1. Conclusion: Phenotypes based on UCR trajectories during hospital admission are associated with adverse outcomes following COVID-19 infection. Further work is needed to understand phenotypes of prolonged COVID-19 disease muscle wasting and its association with longerterm outcomes.
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Introduction: Previous studies on acute respiratory distress syndrome (ARDS) relate trajectories to initial degree of hypoxia1,2 Further work is required to deduce whether previous ARDS frameworks are applicable to COVID-19 ARDS patients. Objectives: How does hypoxia progression influence outcomes in non-COVID ARDS patients and does this differ from COVID-19 ARDS patients? Methods: Mechanically ventilated patients that met the Berlin ARDS Criteria1 were selected from the Medical Information Mart for Intensive Care (MIMIC) database.3 Daily blood gas and ventilatory settings were analysed, from the point of intubation to death or discharge, allowing longitudinal analysis with high granularity. Our primary outcome was how the trajectory of patients was dependent on their hypoxia progression. Secondary outcomes included how base characteristics and initial clinical parameters affect trajectory and outcomes. Comparative analysis was performed between the results of this study and a previous large COVID-19 ARDS study4 Results: 1,575 ICU admissions were included in the study. All results report this study first followed by the COVID-19 study.4 Overall survival rate was higher (70.2% vs 57.7%);less patients had initial moderate or severe hypoxia (54.5% vs 76.8%);less patients had worsening of hypoxia over the first 7 days (18.9% vs 31.8%);and more patients improved their hypoxia status (33.1% vs 23.5%). This study showed a smaller proportion of hypoxia nonresolvers compared to the COVID study (32.6% vs 57.9%). However, non-resolvers in the two studies had similar survival rates (58.6% vs 60.4%). Length of ICU stay (LOS) and duration of invasive mechanical ventilation (IMV) was significantly less in this study compared to the COVID-19 study regardless of hypoxia resolver status. Conclusions: Non-COVID ARDS patients have a more predictable natural history and trajectory compared to COVID-19 ARDs patients. Respiratory failure occurs less frequently and is quicker to resolve, resulting in a lower proportion of hypoxia non-resolvers. However hypoxia non-resolvers of both populations have similar survival outcomes. Despite this, COVID ARDS patient have much longer ICU length of stay and length of ventilation which has significant implications for provision of critical care resources. Further analysis of the impact of COVID-19 therapies on these outcomes is needed.
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Introduction: Nutritional optimisation is recognised as having significant impact on clinical and functional outcomes of critically ill patients.1 Clinical recommendations suggest use of indirect calorimetry guided nutrition in the intensive care unit (ICU),2 and a recent systematic review demonstrated improved outcomes from its use.3 The COVID-19 pandemic has seen a greater proportion of patients with prolonged critical illness, a cohort for whom nutritional optimisation is a key unmet need.4 Objectives: To assess rates of over and underfeeding in a tertiary centre ICU and how these relate to markers of catabolism and persistent critical illness. Methods: Serial measurements of REE (resting energy expenditure) and RQ (respiratory quotient) by indirect calorimetry were performed using Q-NRG+ device (COSMED, Rome, Italy). Nutritional intake and estimations of requirements were recorded concurrently together with routine clinical observations, and markers of critical illness, catabolism and over or underfeeding. Results: Across 30 patients, REE was lower than estimated energy requirements, 24.2 (IQR 20.0-28.1) kcal/ day/kg IBW vs. 29.1(IQR 25.4-33.1) kcal/day/kg IBW, p<0.001. 41.8% of measurements showed overfeeding (actual calorie intake >110% of REE), and 23.3% showed underfeeding (actual calorie intake <85% of REE). Obese patients (n=15) were underfed (-98kcal/day deficit) compared to non-obese patients (n=15), who were on average overfed (+256kcal/day surplus), p=0.021. Overfeeding was also associated with greater length of ICU admission (R2 0.159, p<0.005). Median day of ICU admission in overfed patients was 39 days (IQR 24-56), and in underfed patients 21.5 (IQR 7.5-45.25). However, there was no significant association between calorie surplus or deficit, and other markers of overfeeding PaCO2, insulin use, ureacreatinine-ratio. Conclusion: This service evaluation recorded measurements of REE and RQ in critically ill patients with high lengths of ICU stay (up to 66 days). We observed increased rates of overfeeding with increased duration of ICU admission, and increased overfeeding in non-obese patients.
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It is unclear if changes in public behaviours, developments in COVID-19 treatments, improved patient care, and directed policy initiatives have altered outcomes for minority ethnic groups in the second pandemic wave. This was a prospective analysis of patients aged ≥ 16 years having an emergency admission with SARS-CoV-2 infection between 01/09/2020 and 17/02/2021 to acute NHS hospitals in east London. Multivariable survival analysis was used to assess associations between ethnicity and mortality accounting for predefined risk factors. Age-standardised rates of hospital admission relative to the local population were compared between ethnic groups. Of 5533 patients, the ethnic distribution was White (n = 1805, 32.6%), Asian/Asian British (n = 1983, 35.8%), Black/Black British (n = 634, 11.4%), Mixed/Other (n = 433, 7.8%), and unknown (n = 678, 12.2%). Excluding 678 patients with missing data, 4855 were included in multivariable analysis. Relative to the White population, Asian and Black populations experienced 4.1 times (3.77-4.39) and 2.1 times (1.88-2.33) higher rates of age-standardised hospital admission. After adjustment for various patient risk factors including age, sex, and socioeconomic deprivation, Asian patients were at significantly higher risk of death within 30 days (HR 1.47 [1.24-1.73]). No association with increased risk of death in hospitalised patients was observed for Black or Mixed/Other ethnicity. Asian and Black ethnic groups continue to experience poor outcomes following COVID-19. Despite higher-than-expected rates of hospital admission, Black and Asian patients also experienced similar or greater risk of death in hospital since the start of the pandemic, implying a higher overall risk of COVID-19 associated death in these communities.