The pulse oximeter provides regular non-invasive measurements of blood oxygenation and is used in a wide range of clinical settings [1]. The light wave transmission that this technology uses is modified by skin pigmentation and thus may vary by skin colour. A recent study of paired measures of oxygen saturation from pulse oximetry and arterial blood gas reported differing outputs in patients with black skin compared to patients with white skin that has the potential to adversely impact on patient care [2].

ObjeCtivesTo assess the association between maternal glucose concentrations and adverse perinatal outcomes in women without gestational or existing diabetes and to determine whether clear thresholds for identifying women at risk of perinatal outcomes can be identified. DesignSystematic review and meta-analysis of prospective cohort studies and control arms of randomised trials. Data sOurCesDatabases including Medline and Embase were searched up to October 2014 and combined with individual participant data from two additional birth cohorts.eligibility Criteria fOr seleCting stuDies Studies including pregnant women with oral glucose tolerance (OGTT) or challenge (OGCT) test results, with data on at least one adverse perinatal outcome. appraisal anD Data extraCtiOnGlucose test results were extracted for OGCT (50 g) and OGTT (75 g and 100 g) at fasting and one and two hour post-load timings. Data were extracted on induction of labour; caesarean and instrumental delivery; pregnancy induced hypertension; pre-eclampsia; macrosomia; large for gestational age; preterm birth; birth injury; and neonatal hypoglycaemia. Risk of bias was assessed with a modified version of the critical appraisal skills programme and quality in prognostic studies tools. results 25 reports from 23 published studies and two individual participant data cohorts were included, with up to 207 172 women (numbers varied by the test and outcome analysed in the meta-analyses). Overall most studies were judged as having a low risk of bias. There were positive linear associations with caesarean section, induction of labour, large for gestational age, macrosomia, and shoulder dystocia for all glucose exposures across the distribution of glucose concentrations. There was no clear evidence of a threshold effect. In general, associations were stronger for fasting concentration than for post-load concentration. For example, the odds ratios for large for gestational age per 1 mmol/L increase of fasting and two hour post-load glucose concentrations (after a 75 g OGTT) were 2.15 (95% confidence interval 1.60 to 2.91) and 1.20 (1.13 to 1.28), respectively. Heterogeneity was low between studies in all analyses. COnClusiOnsThis review and meta-analysis identified a large number of studies in various countries. There was a graded linear association between fasting and post-load glucose concentration across the whole glucose distribution and most adverse perinatal outcomes in women without pre-existing or gestational diabetes. The lack of a clear threshold at which risk increases means that decisions regarding thresholds for diagnosing gestational diabetes are somewhat arbitrary. Research should now investigate the clinical and cost-effectiveness of applying different glucose thresholds for diagnosis of gestational diabetes on perinatal and longer term outcomes. systematiC review registratiOn PROSPERO CRD42013004608

Key Results1. Among a 787-patient cohort with confirmed COVID-19, three chest radiograph scores (BRIXIA, RALE, and percent opacification) all had good interrater reliability with intraclass correlations of 0.87, 0.86, and 0.72 respectively.2. Radiograph scores predicted intensive care unit (ICU) admission or death after COVID-19 diagnosis. A 50%-75% opacification (compared to 0%-25%) associated with a 2.2-fold increase in these outcomes among those eligible for ICU care after adjustment for clinical risk scoring. SummaryBRIXIA, RALE, and percent opacification produced reliable and reproducible COVID-19 chest radiograph severity scores that improved accuracy for predicting adverse outcomes when incorporated into ISARIC-4C mortality and NEWS2 clinical scoring systems. I n p r e s s ResultsAdmission chest radiographs of 50 patients (mean age, 74 years +/-16 [sd], 28 men) were scored by all 3 radiologists, with good inter-rater reliability for all scores (ICCs (95% CIs) of for RALE 0.87 (0.80, 0.92), BRIXIA 0.86 (0.76, 0.92), and percentage opacification 0.72 (0.48, 0.85)). Of 751 patients with chest radiograph, those with >75% opacification had a median time to ICU admission or death of just 1-2 days. Among 628 patients with data (median age 76 years (IQR 61 -84), and 344 were men), 50-75% opacification increased risk of ICU admission or death by twofold (1.6 -2.8), and over 75% opacification by 4 fold (3.4 -4.7), compared to a 0-25% opacification when adjusted for NEWS2 score. ConclusionBRIXIA, RALE, and percent opacification scores all reliably predicted adverse outcomes in SARS-CoV-2.

Background: Sepsis represents a significant public health burden, costing the NHS £2.5 billion annually, with 35% mortality in 2006. The aim of this exploratory study was to investigate risk factors predictive of 30-day mortality amongst patients with sepsis in Nottingham. Methods: Data were collected prospectively from adult patients with sepsis in Nottingham University Hospitals NHS Trust as part of an ongoing quality improvement project between November 2011 and March 2014. Patients admitted to critical care with the diagnosis of sepsis were included in the study. In all, 97 separate variables were investigated for their association with 30-day mortality. Variables included patient demographics, symptoms of systemic inflammatory response syndrome, organ dysfunction or tissue hypoperfusion, locations of early care, source of sepsis and time to interventions. Results: A total of 455 patients were included in the study. Increased age (adjOR ¼ 1.05 95%CI ¼ 1.03-1.07 p < 0.001), thrombocytopenia (adjOR ¼ 3.10 95%CI ¼ 1.23-7.82 p ¼ 0.016), hospital-acquired sepsis (adjOR ¼ 3.34 95%CI ¼ 1.78-6.27 p < 0.001), increased lactate concentration (adjOR ¼ 1.16 95%CI ¼ 1.06-1.27 p ¼ 0.001), remaining hypotensive after vasopressors (adjOR ¼ 3.89 95%CI ¼ 1.26-11.95 p ¼ 0.02) and mottling (adjOR ¼ 3.80 95%CI ¼ 1.06-13.55 p ¼ 0.04) increased 30-day mortality odds. Conversely, fever (adjOR ¼ 0.46 95%CI ¼ 0.28-0.75 p ¼ 0.002), fluid refractory hypotension (adjOR ¼ 0.29 95%CI ¼ 0.10-0.87 p ¼ 0.027) and being diagnosed in surgical wards (adjOR ¼ 0.35 95%CI ¼ 0.15-0.81 p ¼ 0.015) were protective. Treatment timeliness were not significant factors. Conclusion: Several important predictors of 30-day mortality were found by this research. Retrospective analysis of our sepsis data has revealed mortality predictors that appear to be more patient-related than intervention-specific. With this information, care can be improved for those identified most at risk of death.

BackgroundPatient safety literature has long reported the need for early recognition of deteriorating patients. Early warning scores (EWSs) are commonly implemented as “track and trigger,” or rapid response systems for monitoring and early recognition of acute patient deterioration. This study presents a human factors evaluation of a hospital-wide transformation in practice, engendered by the deployment of an innovative electronic observations (eObs) and handover system. This technology enables real-time information processing at the patient’s bedside, improves visibility of patient data, and streamlines communication within clinical teams.ObjectiveThe aim of this study was to identify improvement and deterioration in workplace efficiency and quality of care resulting from the large-scale imposition of new technology.MethodsA total of 85 hours of direct structured observations of clinical staff were carried out before and after deployment. We conducted 40 interviews with a range of clinicians. A longitudinal analysis of critical care audit and electronically recorded patient safety incident reports was conducted. The study was undertaken in a large secondary-care facility in the United Kingdom.ResultsRoll-out of eObs was associated with approximately 10% reduction in total unplanned admissions to critical care units from eObs-equipped wards. Over time, staff appropriated the technology as a tool for communication, workload management, and improving awareness of team capacity. A negative factor was perceived as lack of engagement with the system by senior clinicians. Doctors spent less time in the office (68.7% to 25.6%). More time was spent at the nurses’ station (6.6% to 41.7%). Patient contact time was more than doubled (2.9% to 7.3%).ConclusionsSince deployment, clinicians have more time for patient care because of reduced time spent inputting and accessing data. The formation of a specialist clinical team to lead the roll-out was universally lauded as the reason for success. Staff valued the technology as a tool for managing workload and identified improved situational awareness as a key benefit. For future technology deployments, the staff requested more training preroll-out, in addition to engagement and support from senior clinicians.

The early recognition and goal-directed management of severely septic patients has been shown to reduce mortality. This study aimed to determine compliance with the ‘Surviving Sepsis' guidelines and to analyse the systems involved in delivering care in a UK teaching hospital. Patients from any hospital department with clinically significant positive blood cultures (n=229) were identified. Adults who met criteria for severe sepsis and who were deemed suitable for active management were included (n=46). None of the patients received all bundle elements within the advised time limits. Only 52% had received broad-spectrum antibiotics within three hours of presentation and only 57% had adequate fluid resuscitation within six hours. The median time from presentation to doctor involvement was 1.1 hours, with a delay of 3.9 hours to see a senior clinician. The median time from presentation to admission to the high dependency unit (HDU) or intensive care unit (ICU) was 12.9 hours. This study has shown that the early recognition and resuscitation of septic patients is unreliable and the capacity to provide early antimicrobial and goal-directed therapy for septic patients is limited in a UK teaching hospital.

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Objectives: Currently used prognostic tools for patients with SARS-CoV-2 infection are based on clinical and laboratory parameters measured at a single point in time, usually on admission. We aimed to determine how dynamic changes in clinical and laboratory parameters relate to SARS-CoV-2 prognosis. Design: retrospective, observational cohort study using routinely collected clinical data to model the dynamic change in prognosis of SARS-CoV-2. Setting: a single, large hospital in England. Participants: all patients with confirmed SARS-CoV-2 admitted to Nottingham University Hospitals (NUH) NHS Trust, UK from 1st February 2020 until 30th November 2020. Main outcome measures: Intensive Care Unit (ICU) admission, death and discharge from hospital. Statistical Methods: We split patients into 1st (admissions until 30th June) and 2nd (admissions thereafter) waves. We incorporated all clinical observations, blood tests and other covariates from electronic patient records and follow up until death or 30 days from the point of hospital discharge. We modelled daily risk of admission to ICU or death with a time varying Cox proportional hazards model. Results: 2,964 patients with confirmed SARS-CoV-2 were included. Of 1,374 admitted during the 1st wave, 593 were eligible for ICU escalation, and 466 had near complete ascertainment of all covariates at admission. Our validation sample included 1,590 confirmed cases, of whom 958 were eligible for ICU admission. Our model had good discrimination of daily need for ICU admission or death (C statistic = 0.87 (IQR 0.85-0.90)) and predicted this daily prognosis better than previously published scores (NEWS2, ISCARIC 4C). In validation in the 2nd wave the score overestimated escalation (calibration slope 0.55), whilst retaining a linear relationship and good discrimination (C statistic = 0.88 (95% CI 0.81 -0.95)). Conclusions: A bespoke SARS-CoV-2 escalation risk prediction score can predict need for clinical escalation better than a generic early warning score or a single estimation of risk at admission.