Introduction Since the introduction of the UK’s National Early Warning Score (NEWS) and its modification, NEWS2, Coronavirus disease 2019 (COVID-19), has caused a worldwide pandemic. NEWS and NEWS2 have good predictive abilities in patients with other infections and sepsis, however there is little evidence of their performance in COVID-19. Methods Using receiver-operating characteristics analyses, we used the area under the receiver operating characteristic (AUROC) curve to evaluate the performance of NEWS or NEWS2 to discriminate the combined outcome of either death or intensive care unit (ICU) admission within 24 h of a vital sign set in five cohorts (COVID-19 POSITIVE, n = 405; COVID-19 NOT DETECTED, n = 1716; COVID-19 NOT TESTED, n = 2686; CONTROL 2018, n = 6273; CONTROL 2019, n = 6523). Results The AUROC values for NEWS or NEWS2 for the combined outcome were: COVID-19 POSITIVE, 0.882 (0.868−0.895); COVID-19 NOT DETECTED, 0.875 (0.861−0.89); COVID-19 NOT TESTED, 0.876 (0.85−0.902); CONTROL 2018, 0.894 (0.884−0.904); CONTROL 2019, 0.842 (0.829−0.855). Conclusions The finding that NEWS or NEWS2 performance was good and similar in all five cohorts (range = 0.842−0.894) suggests that amendments to NEWS or NEWS2, such as the addition of new covariates or the need to change the weighting of existing parameters, are unnecessary when evaluating patients with COVID-19. Our results support the national and international recommendations for the use of NEWS or NEWS2 for the assessment of acute-illness severity in patients with COVID-19.

For organisms that remain active in one of the last undisturbed and pristine dark environments on the planet-the Arctic Polar Night-the moon, stars and aurora borealis may provide important cues to guide distribution and behaviours, including predator-prey interactions. With a changing climate and increased human activities in the Arctic, such natural light sources will in many places be masked by the much stronger illumination from artificial light. Here we show that normal working-light from a ship may disrupt fish and zooplankton behaviour down to at least 200 m depth across an area of >0.125 km 2 around the ship. Both the quantitative and qualitative nature of the disturbance differed between the examined regions. We conclude that biological surveys in the dark from illuminated ships may introduce biases on biological sampling, bioacoustic surveys, and possibly stock assessments of commercial and non-commercial species.

Increasing contributions of prymnesiophytes such as Phaeocystis pouchetii and Emiliania huxleyi to Barents Sea (BS) phytoplankton production have been suggested based on in situ observations of phytoplankton community composition, but the scattered and discontinuous nature of these records confounds simple inference of community change or its relationship to salient environmental variables. However, provided that meaningful assessments of phytoplankton community composition can be inferred based on their optical characteristics, ocean-colour records offer a potential means to develop a synthesis between sporadic in situ observations. Existing remote-sensing algorithms to retrieve phytoplankton functional types based on chlorophyll-a ( chl-a ) concentration or indices of pigment packaging may, however, fail to distinguish Phaeocystis from other blooms of phytoplankton with high pigment packaging, such as diatoms. We develop a novel algorithm to distinguish major phytoplankton functional types in the BS and apply it to the MODIS-Aqua ocean-colour record, to study changes in the composition of BS phytoplankton blooms in July, between 2002 and 2018, creating time series of the spatial distribution and intensity of coccolithophore, diatom and Phaeocystis blooms. We confirm a north-eastward expansion in coccolithophore bloom distribution, identified in previous studies, and suggest an inferred increase in chl-a concentrations, reported by previous researchers, may be partly explained by increasing frequencies of Phaeocystis blooms. This article is part of the theme issue ‘The changing Arctic Ocean: consequences for biological communities, biogeochemical processes and ecosystem functioning’.

A bio-optical model for the Barents Sea is determined from a set of in situ observations of inherent optical properties (IOPs) and associated biogeochemical analyses. The bio-optical model provides a pathway to convert commonly measured parameters from glider-borne sensors (CTD, optical triplet sensor—chlorophyll and CDOM fluorescence, backscattering coefficients) to bulk spectral IOPs (absorption, attenuation and backscattering). IOPs derived from glider observations are subsequently used to estimate remote sensing reflectance spectra that compare well with coincident satellite observations, providing independent validation of the general applicability of the bio-optical model. Various challenges in the generation of a robust bio-optical model involving dealing with partial and limited quantity datasets and the interpretation of data from the optical triplet sensor are discussed. Establishing this quantitative link between glider-borne and satellite-borne data sources is an important step in integrating these data streams and has wide applicability for current and future integrated autonomous observation systems. This article is part of the theme issue ‘The changing Arctic Ocean: consequences for biological communities, biogeochemical processes and ecosystem functioning’.

Introduction: Coronavirus disease 2019 (COVID-19) placed increased burdens on National Health Service hospitals and necessitated significant adjustments to their structures and processes. This research investigated if and how these changes affected the patterns of vital sign recording and staff compliance with expected monitoring schedules on general wards. Methods: We compared the pattern of vital signs and early warning score (EWS) data collected from admissions to a single hospital during the initial phase of the COVID-19 pandemic with those in three control periods from 2018, 2019 and 2020. Main outcome measures were weekly and monthly hospital admissions; daily and hourly patterns of recorded vital signs and EWS values; time to next observation and; proportions of 'on time', 'late' and 'missed' vital signs observations sets. Results: There were large falls in admissions at the beginning of the COVID-19 era. Admissions were older, more unwell on admission and throughout their stay, more often required supplementary oxygen, spent longer in hospital and had a higher in-hospital mortality compared to one or more of the control periods. More daily observation sets were performed during the COVID-19 era than in the control periods. However, there was no clear evidence that COVID-19 affected the pattern of vital signs collection across the 24-h period or the week. Conclusions: The increased burdens of the COVID-19 pandemic, and the alterations in healthcare structures and processes necessary to respond to it, did not adversely affect the hospitals' ability to monitor patients under its care and to comply with expected monitoring schedules.

Background Risk stratification has become a key part of the care processes for patients having emergency bowel surgery. This study aimed to determine if operative approach influences risk‐model performance, and risk‐adjusted mortality rates in the United Kingdom. Methods A prospectively planned analysis was conducted using National Emergency Laparotomy Audit (NELA) data from December 2013 to November 2018. The risk‐models investigated were P‐POSSUM and the NELA Score, with model performance assessed in terms of discrimination and calibration. Risk‐adjusted mortality was assessed using Standardised Mortality Ratios (SMR). Analysis was performed for the total cohort, and cases performed open, laparoscopically and converted to open. Sub‐analysis was performed for cases with ≤ 20% predicted mortality. Results Data were available for 116 396 patients with P‐POSSUM predicted mortality, and 46 935 patients with the NELA score. Both models displayed excellent discrimination with little variation between operative approaches (c‐statistic: P‐POSSUM 0.801–0.836; NELA Score 0.811–0.862). The NELA score was well calibrated across all deciles of risk, but P‐POSSUM over‐predicted risk beyond 20% mortality. Calibration plots for operative approach demonstrated that both models increasingly over‐predicted mortality for laparoscopy, relative to open and converted to open surgery. SMRs calculated using both models consistently demonstrated that risk‐adjusted mortality with laparoscopy was a third lower than open surgery. Conclusion Risk‐adjusted mortality for emergency bowel surgery is lower for laparoscopy than open surgery, with P‐POSSUM and NELA score both over‐predicting mortality for laparoscopy. Operative approach should be considered in the development of future risk‐models that rely on operative data.

Pre-operative risk stratification is a key part of the care pathway for emergency bowel surgery, as it facilitates the identification of high-risk patients. Several novel risk scores have recently been published that are designed to identify patients who are frail or significantly unwell. They can also be calculated preoperatively from routinely collected clinical data. This study aimed to investigate the ability of these scores to predict 30-day mortality after emergency bowel surgery. A single centre cohort study was performed using our local data from the National Emergency Laparotomy Audit database. Further data were extracted from electronic hospital records (n = 1508). The National Early Warning Score, Laboratory Decision Tree Early Warning Score and Hospital Frailty Risk Score were then calculated. The most abnormal National or Laboratory Decision Tree Early Warning Score in the 24 or 72 h before surgery was used in analysis. Individual scores were reasonable predictors of mortality (c-statistic 0.699-0.740) but all were poorly calibrated. A National Early Warning Score ≥ 4 was associated with a high overall mortality rate (> 10%). A logistic regression model was developed using age, National Early Warning Score, Laboratory Decision Tree Early Warning Score and Hospital Frailty Risk Score as predictor variables, and its performance compared with other established risk models. The model demonstrated good discrimination and calibration (c-statistic 0.827) but was marginally outperformed by the National Emergency Laparotomy Audit score (c-statistic 0.861). All other models compared performed less well (c-statistics 0.734-0.808). Preoperative patient vital signs, blood tests and markers of frailty can be used to accurately predict the risk of 30-day mortality after emergency bowel surgery.

Accurate measurements of absorption data are required for the development and validation of inversion algorithms for upcoming hyperspectral ocean color imaging sensors, such as the NASA Phytoplankton, Aerosol, Cloud, and ocean Ecosystem mission. This study aims to provide uncertainty estimates associated with leading approaches to measure hyperspectral absorption coefficients in complex coastal waters. Absorption spectra were collected at 12 different stations, all located in the Indian River Lagoon, Florida, USA, between 09 January 2017 and 13 January 2017. Measurements included spectral absorption coefficients in the visible range (400-700 nm) associated with dissolved, a CDOM , total particulate, a p , and total nonwater, a nw , fractions, and were made both in situ and from discrete samples. Discrete sample approaches included dual-beam spectrophotometer, liquid waveguide capillary cell, point-source integrating cavity absorption meter (PSICAM) for dissolved matter absorption samples, and quantitative filter technique ICAM measurements and the dual-beam spectrophotometer with center-mounted integrating sphere filter pad technique, while the Turner Designs ICAM, and WET Labs AC-s, and AC-9 instruments were used to determine absorption coefficients in situ. The Gershun approach, determining absorption from measurement of the irradiance quartet with respect to depth was also assessed in situ. Measurement uncertainties and relative accuracies were quantified for each of these approaches. Results showed generally strong agreements between different discrete sample methods, with average percent absolute error %δ abs < 7% for a CDOM and < 9% for a p . In situ approaches showed higher variability and reduced accuracy. For a nw , %δ abs deviation relative to PSICAM data was on average 12% to 20%. Results help identify remaining technological gaps and need for improvements in the different absorption measurement approaches.Light absorption is a fundamental property of natural waters influencing the propagation of the underwater light field (Mobley 1994;Zaneveld et al. 2005;Wo zniak and Dera 2007). Absorption acts as a spectral filter for incident and scattered solar irradiance (e.g., Jerlov 1976;Morel and Prieur 1977;Lewis et al. 1990). Light absorption is commonly quantified as spectral absorption coefficient a(λ) (m À1 ), where λ is the wavelength in vacuum. The accurate quantification of this coefficient and its variability are important for understanding many physical and biological processes in the upper ocean, which are driven by or depend on solar radiation, for example, various photochemical reactions, heating of water column, availability of energy for photosynthesis, or availability of light for animal vision that is important even at mesopelagic depths. Absorption coefficients can provide information on the nature and concentration of various nonwater constituents dissolved and suspended within water (e.g., Twardowski et al. 2005;Wo zniak and Dera 2007;Twardowski et al. 2018a), such as phytoplankton pigments...