In-hospital mortality is associated with inflammatory response in NAFLD patients admitted for COVID-19

Background & aims Although metabolic risk factors are associated with more severe COVID-19, there is little evidence on outcomes in patients with non-alcoholic fatty liver disease (NAFLD). We here describe the clinical characteristics and outcomes of NAFLD patients in a cohort hospitalised for COVID-19. Methods This study included all consecutive patients admitted for COVID-19 between February and April 2020 at Imperial College Healthcare NHS Trust, with either imaging of the liver available dated within one year from the admission or a known diagnosis of NAFLD. Clinical data and early weaning score (EWS) were recorded. NAFLD diagnosis was based on imaging or past medical history and patients were stratified for Fibrosis-4 (FIB-4) index. Clinical endpoints were admission to intensive care unit (ICU)and in-hospital mortality. Results 561 patients were admitted. Overall, 193 patients were included in the study. Fifty nine patients (30%) died, 9 (5%) were still in hospital, and 125 (65%) were discharged. The NAFLD cohort (n = 61) was significantly younger (60 vs 70.5 years, p = 0.046) at presentation compared to the non-NAFLD (n = 132). NAFLD diagnosis was not associated with adverse outcomes. However, the NAFLD group had higher C reactive protein (CRP) (107 vs 91.2 mg/L, p = 0.05) compared to non-NAFLD(n = 132). Among NAFLD patients, male gender (p = 0.01), ferritin (p = 0.003) and EWS (p = 0.047) were associated with in-hospital mortality, while the presence of intermediate/high risk FIB-4 or liver cirrhosis was not. Conclusion The presence of NAFLD per se was not associated with worse outcomes in patients hospitalised for COVID-19. Though NAFLD patients were younger on admission, disease stage was not associated with clinical outcomes. Yet, mortality was associated with gender and a pronounced inflammatory response in the NAFLD group

Report 29: The impact of the COVID-19 epidemic on all-cause attendances to emergency departments in two large London hospitals: an observational study

The health care system in England has been highly affected by the surge in demand due to patients afflicted by COVID-19. Yet the impact of the pandemic on the care seeking behaviour of patients and thus on Emergency department (ED) services is unknown, especially for non-COVID-19 related emergencies. In this report, we aimed to assess how the reorganisation of hospital care and admission policies to respond to the COVID-19 epidemic affected ED attendances and emergency hospital admissions. We performed time-series analyses of present year vs historic (2015-2019) trends of ED attendances between March 12 and May 31 at two large central London hospitals part of Imperial College Healthcare NHS Trust (ICHNT) and compared these to regional and national trends. Historic attendances data to ICHNT and publicly available NHS situation reports were used to calibrate time series auto-regressive integrated moving average (ARIMA) forecasting models. We thus predicted the (conterfactual) expected number of ED attendances between March 12 (when the first public health measure leading to lock-down started in England) to May 31, 2020 (when the analysis was censored) at ICHNT, at all acute London Trusts and nationally. The forecasted trends were compared to observed data for the same periods of time. Lastly, we analysed the trends at ICHNT disaggregating by mode of arrival, distance from postcode of patient residence to hospital and primary diagnosis amongst those that were subsequently admitted to hospital and compared these data to an average for the same period of time in the years 2015 to 2019. During the study period (January 1 to May 31, 2020) there was an overall decrease in ED attendances of 35% at ICHNT, of 50% across all London NHS Trusts and 53% nationally. For ICHNT, the decrease in attendances was mainly amongst those aged younger than 65 and those arriving by their own means (e.g. personal or public transport). Increasing distance (km) from postcode of residence to hospital was a significant predictor of reduced attendances, which could not be explained by weighted (for population numbers) mean index of multiple deprivation. Non-COVID emergency admissions to hospital after March 12 fell by 48% at ICHNT compared to previous years. This was seen across all disease areas, including acute coronary syndromes, stroke and cancer-related emergencies. The overall non-COVID-19 hospitalisation mortality risk did not differ (RR 1.13, 95%CI 0.94-1.37, p=0.19), also in comparison to previous years. Our findings suggest emergency healthcare seeking to hospitals drastically changed amongst the population within the catchment area of ICHNT. This trend was echoed regionally and nationally, suggesting those suffering a medical emergency may not have attended other (i.e. closer-to-home) hospitals. Furthermore, our time-series analyses showed that, even after COVID-19 cases and deaths decreased (i.e. from early April), non-COVID-19 ED attendances did not increase. The impact of emergency triaging systems (e.g. 111 calls) and alternative (e.g. private hospital, chemist) health services on these trends remains unknown. However, another recent report found increased non-COVID excess deaths in the community, which may be partially explained by people experiencing an emergency and not attending health services at all. Whether those that attended ED services have done so with longer delays from the moment of emergency onset also remains unknown. National analyses into the factors causing reduced attendances to ED services and strategies to revert these negative trends are urgently needed

Vector field and rotational curves in dark galactic halos

We study equations of a non-gauge vector field in a spherically symmetric static metric. The constant vector field with a scale arrangement of components: the time component about the Planck mass m_{Pl} and the radial component about M suppressed with respect to the Planck mass, serves as a source of metric reproducing flat rotation curves in dark halos of spiral galaxies, so that the velocity of rotation v_0 is determined by the hierarchy of scales: \sqrt{2} v_0^2= M/m_{Pl}, and M\sim 10^{12} GeV. A natural estimate of Milgrom's acceleration about the Hubble rate is obtained.Comment: 17 pages, iopart style, misprint remove

Report 17: Clinical characteristics and predictors of outcomes of hospitalised patients with COVID-19 in a London NHS Trust: a retrospective cohort study

Clinical characteristics and determinants of outcomes for hospitalised COVID-19 patients in the UK remain largely undescribed and emerging evidence suggests ethnic minorities might be disproportionately affected. We describe the characteristics and outcomes of patients hospitalised for COVID-19 in three large London hospitals with a multi-ethnic catchment population. We performed a retrospective cohort study on all patients hospitalised with laboratory-confirmed SARS-CoV-2 infection at Imperial College Healthcare NHS Trust between February 25 and April 5, 2020. Outcomes were recorded as of April 19, 2020. Logistic regression models, survival analyses and cumulative competing risk analyses were performed to evaluate factors associated with COVID-19 hospital mortality. Of 520 patients in this cohort (median age 67 years, (IQR 26) and 62% male), 302 (68%) had been discharged alive, 144 (32%) died and 74 (14%) were still hospitalised at the time of censoring. Increasing age (adjusted odds ratio [aOR] 2·16, 95%CI 1·50-3·12), severe hypoxia (aOR 3·75, 95%CI 1·80-7·80), low platelets (aOR 0·65, 95%CI 0.49·0·85), reduced estimated glomerular filtration rate (aOR 4·11, 95%CI 1·58-10·69), bilirubin >21mmol/L (aOR 2·32, 95%CI 1·05-5·14) and low albumin (aOR 0·77, 9%%CI 0·59-1·01) were associated with increased risk of in-hospital mortality. Individual comorbidities were not independently associated with risk of death. Regarding ethnicity, 209 (40%) were from a black and Asian minority, for 115 (22%) ethnicity was unknown and 196 (38%) patients were white. Compared to the latter, black patients were significantly younger and had less comorbidities. Whilst the crude OR of death of black compared to white patients was not significant (1·14, 95%CI 0·69-1·88, p=0.62), adjusting for age and comorbidity showed a trend towards significance (aOR 1·72, 95%CI 0·98-3·02, p=0.06) and further accounting for admission severity (Early Warning Score) showed a significant difference (aOR 1·83 95% CI 1·02-3·30, p=0.04). In the first study to describe the characteristics and predictors of outcome for hospitalised COVID-19 patients in the UK, we find that older age, male sex and admission hypoxia, thrombocytopenia, renal failure, hypoalbuminaemia and raised bilirubin are associated with increased odds of death. Ethnic minority groups were over-represented in our cohort and, compared to whites, people of black ethnicity may be at increased odds of mortality. Further research is urgently needed to investigate these associations on a larger scale

Improving Case Definition of Crohnʼs Disease and Ulcerative Colitis in Electronic Medical Records Using Natural Language Processing

available in PMC 2014 June 01Background: Previous studies identifying patients with inflammatory bowel disease using administrative codes have yielded inconsistent results. Our objective was to develop a robust electronic medical record–based model for classification of inflammatory bowel disease leveraging the combination of codified data and information from clinical text notes using natural language processing. Methods: Using the electronic medical records of 2 large academic centers, we created data marts for Crohn’s disease (CD) and ulcerative colitis (UC) comprising patients with ≥1 International Classification of Diseases, 9th edition, code for each disease. We used codified (i.e., International Classification of Diseases, 9th edition codes, electronic prescriptions) and narrative data from clinical notes to develop our classification model. Model development and validation was performed in a training set of 600 randomly selected patients for each disease with medical record review as the gold standard. Logistic regression with the adaptive LASSO penalty was used to select informative variables. Results: We confirmed 399 CD cases (67%) in the CD training set and 378 UC cases (63%) in the UC training set. For both, a combined model including narrative and codified data had better accuracy (area under the curve for CD 0.95; UC 0.94) than models using only disease International Classification of Diseases, 9th edition codes (area under the curve 0.89 for CD; 0.86 for UC). Addition of natural language processing narrative terms to our final model resulted in classification of 6% to 12% more subjects with the same accuracy. Conclusions: Inclusion of narrative concepts identified using natural language processing improves the accuracy of electronic medical records case definition for CD and UC while simultaneously identifying more subjects compared with models using codified data alone.National Institutes of Health (U.S.) (NIH U54-LM008748)American Gastroenterological AssociationNational Institutes of Health (U.S.) (NIH K08 AR060257)Beth Isreal Deaconess Medical Center (Katherine Swan Ginsburg Fund)National Institutes of Health (U.S.) (NIH R01-AR056768)Burroughs Wellcome Fund (Career Award for Medical Scientists)National Institutes of Health (U.S.) (NIH U01-GM092691)National Institutes of Health (U.S.) (NIH R01-AR059648