Yes / The National Early Warning Score (NEWS) is being replaced with NEWS2 which adds 3 points for new confusion or delirium. We estimated the impact of adding delirium on the number of medium/high level alerts that are triggers to escalate care. Methods Analysis of emergency medical admissions in two acute hospitals (York Hospital (YH) and Northern Lincolnshire and Goole NHS Foundation Trust hospitals (NH)) in England. Twenty per cent were randomly assigned to have delirium. Results The number of emergency admissions (YH: 35584; NH: 35795), mortality (YH: 5.7%; NH: 5.5%), index NEWS (YH: 2.5; NH: 2.1) and numbers of NEWS recorded (YH: 879193; NH: 884072) were similar in each hospital. The mean number of patients with medium level alerts per day increased from 55.3 (NEWS) to 69.5 (NEWS2), a 25.7% increase in YH and 64.1 (NEWS) to 77.4 (NEWS2), a 20.7% increase in NH. The mean number of patients with high level alerts per day increased from 27.3 (NEWS) to 34.4 (NEWS2), a 26.0% increase in YH and 29.9 (NEWS) to 37.7 (NEWS2), a 26.1% increase in NH. Conclusions The addition of delirium in NEWS2 will have a substantial increase in medium and high level alerts in hospitalised emergency medical patients. Rigorous evaluation of NEWS2 is required before widespread implementation because the extent to which staff can cope with this increase without adverse consequences remains unknown.

Yes / In English hospitals, the patient’s vital signs are monitored and summarised into a National Early Warning Score (NEWS). NEWS is more accurate than the quick sepsis related organ failure assessment (qSOFA) score at identifying patients with sepsis. We investigate the extent to which the accuracy of the NEWS is enhanced by developing computer-aided NEWS (cNEWS) models. We compared three cNEWS models (M0=NEWS alone; M1=M0 + age + sex; M2=M1 + subcomponents of NEWS + diastolic blood pressure) to predict the risk of sepsis. Methods: All adult emergency medical admissions discharged over 24-months from two acute hospitals (YH–York Hospital for model development; NH–Northern Lincolnshire and Goole Hospital for external model validation). We used a validated Canadian method for defining sepsis from administrative hospital data. Findings: The prevalence of sepsis was lower in YH (4.5%=1596/35807) than NH (8.5%=2983/35161). The c-statistic increased across models (YH: M0: 0.705, M1:0.763, M2:0.777; NH:M0: 0.708, M1:0.777, M2:0.791). At NEWS 5+, sensitivity increased (YH: 47.24% vs 50.56% vs 52.69%; NH: 37.91% vs 43.35% vs 48.07%)., the positive likelihood ratio increased (YH: 2.77 vs 2.99 vs 3.06; NH: 3.18 vs 3.32 vs 3.45) and the positive predictive value increased (YH: 11.44% vs 12.24% vs 12.49%; NH: 22.75% vs 23.55% vs 24.21%). Interpretation: From the three cNEWS models, Model M2 is the most accurate. Since it places no additional data collection burden on clinicians and can be automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure. / The Health Foundation, National Institute for Health Research (NIHR) Yorkshire and Humberside Patient Safety Translational Research Centre / Research Development Fund Publication Prize Award winner, April 2019.

National Early Warning Score (NEWS) är ett bedömningsinstrument som används inom vården för att upptäcka och varna när en patient blir kraftigt försämrad. Syftet med litteraturöversikten var att sammanställa kunskap om sjuksköterskors erfarenheter av att använda bedömningsinstrumentet NEWS. En integrerad litteraturöversikt genomfördes där 12 vetenskapliga artiklar togs ut efter en systematisk litteratursökning i PubMed och CINAHL. Av dessa var det sex med kvalitativ metod, fyra kvantitativ metod och två mixad metod, som granskades och analyserades. Analysen resulterade i fyra kategorier. Dessa var ”NEWS användes av sjuksköterskan för att observera, bedöma och planera vård”, ”NEWS kunde både vara ett stöd och skapa merarbete”, ”NEWS förbättrade kommunikationen med andra kollegor” och ” NEWS kunde främja sjuksköterskan i sin profession”. Slutsatserna som drogs var att sjuksköterskorna ansåg NEWS som användbart vid att uppfatta en patients försämring, instrumentet användes även som ett sätt att stödja en egen klinisk bedömning. Sjuksköterskorna ansåg att NEWS behövde anpassas för olika sjukdomstillstånd eftersom falska höga värden ledde till onödiga kontroller och larmutmattning. Kommunikationen mellan kollegor blev bättre och sjuksköterskorna fick stöd och bättre respons när vitalparametrar kommunicerades. Sjuksköterskorna ansåg att NEWS ökade fokuset på vitalparametrar vilket ledde till ökad kunskap om avvikelser samt stimulerade till egna bedömningar. Författarna anser att det finns behov av att arbeta särskilt med kommunikationsmetoder som införlivas med NEWS, där alla parter i vårdkedjan förstår instrumentets relevans. Detta kan förslagsvis göras i samband vid implementering av NEWS med tydliga riktlinjer kring bedömnings-instrumentet, hur det ska användas och varför.

Introduktion: Minskat antal vårdplatser och en ökad ålder på befolkningen gör att trycket på vården ökar. I takt med detta ökar också risken för att patientsäkerheten inte kan upprätthållas. NEWS är ett bedömnings- och screeninginstrument var syfte är att öka patientsäkerheten genom att standardisera bedömningar av vitala parametrar för att effektivt kunna förhindra kritiska tillstånd. Sjuksköterskor kan använda NEWS tillsammans med sin kliniska bedömning för att utföra en adekvat bedömning av patienters aktuella hälsotillstånd. Syfte: Litteraturstudiens syfte var att undersöka sjuksköterskors erfarenhet av att använda National Early Warning Score som bedömningsinstrument för att bedöma patienters hälsostatus. Metod: Litteraturstudien bygger på Polit och Becks (2017) nio steg med induktiv ansats. Relevanta sökord mot syftet identifierades och systematiska artikelsökningar genomfördes i Cinahl och PubMed. Sökningarna resulterade i 11 artiklar relevanta för studien som kvalitetsgranskades utifrån Polit och Becks (2017) granskningsmallar. I databearbetningen framkom tre teman. Resultat: Tre teman framkom utifrån sjuksköterskors erfarenhet av att använda NEWS som bedömningsinstrument för att bedöma patienters hälsostatus, dessa var NEWS som stöd och hinder i klinisk bedömning, NEWS påverkan på arbetsbelastningen, samt hur sjuksköterskors utbildning och yrkeserfarenhet kunde kombineras med NEWS. Resultatet visade att NEWS är ett bra stöd till sjuksköterskors kliniska bedömning. Detta stödjer främst sjuksköterskor med kortare erfarenhet, men kan ändå vara ett bra stöd till sjuksköterskor med längre erfarenhet. Slutsats: Erfarna sjuksköterskor ansåg att sjuksköterskor med kortare erfarenhet än de själva kan behöva stöd i sin kliniska helhetsbedömning samt kommunikation, och då är NEWS ett bra komplement. Sjuksköterskorna i litteraturstudien var inte enade om arbetsbelastningen ökade eller inte vid användandet av NEWS.

Yes / Hospital-acquired Acute Kidney Injury (H-AKI) is a common cause of avoidable morbidity and mortality. To determine if the patients’ vital signs data as defined by a National Early Warning Score (NEWS), can predict H-AKI following emergency admission to hospital. Methods: Analyses of emergency admissions to York hospital over 24-months with NEWS data. We report the area under the curve (AUC) for logistic regression models that used the index NEWS (model A0), plus age and sex (A1), plus subcomponents of NEWS (A2) and two-way interactions (A3). Likewise for maximum NEWS (models B0,B1,B2,B3). Results: 4.05% (1361/33608) of emergency admissions had H-AKI. Models using the index NEWS had the lower AUCs (0.59 to 0.68) than models using the maximum NEWS AUCs (0.75 to 0.77). The maximum NEWS model (B3) was more sensitivity than the index NEWS model (A0) (67.60% vs 19.84%) but identified twice as many cases as being at risk of H-AKI (9581 vs 4099) at a NEWS of 5. Conclusions: The index NEWS is a poor predictor of H-AKI. The maximum NEWS is a better predictor but seems unfeasible because it is only knowable in retrospect and is associated with a substantial increase in workload albeit with improved sensitivity. / The Health Foundation

Yes / Routine administrative data have been used to show that patients admitted to hospitals over the weekend appear to have a higher mortality compared to weekday admissions. Such data do not take the severity of sickness of a patient on admission into account. Our aim was to incorporate a standardized vital signs physiological-based measure of sickness known as the National Early Warning Score to investigate if weekend admissions are: sicker as measured by their index National Early Warning Score; have an increased mortality; and experience longer delays in the recording of their index National Early Warning Score. Methods: We extracted details of all adult emergency medical admissions during 2014 from hospital databases and linked these with electronic National Early Warning Score data in four acute hospitals. We analysed 47,117 emergency admissions after excluding 1657 records, where National Early Warning Score was missing or the first (index) National Early Warning Score was recorded outside ±24 h of the admission time. Results: Emergency medical admissions at the weekend had higher index National Early Warning Score (weekend: 2.53 vs. weekday: 2.30, p

Yes / The novel coronavirus SARS-19 produces 'COVID-19' in patients with symptoms. COVID-19 patients admitted to the hospital require early assessment and care including isolation. The National Early Warning Score (NEWS) and its updated version NEWS2 is a simple physiological scoring system used in hospitals, which may be useful in the early identification of COVID-19 patients. We investigate the performance of multiple enhanced NEWS2 models in predicting the risk of COVID-19. Our cohort included unplanned adult medical admissions discharged over 3 months (11 March 2020 to 13 June 2020 ) from two hospitals (YH for model development; SH for external model validation). We used logistic regression to build multiple prediction models for the risk of COVID-19 using the first electronically recorded NEWS2 within ± 24 hours of admission. Model M0' included NEWS2; model M1' included NEWS2 + age + sex, and model M2' extends model M1' with subcomponents of NEWS2 (including diastolic blood pressure + oxygen flow rate + oxygen scale). Model performance was evaluated according to discrimination (c statistic), calibration (graphically), and clinical usefulness at NEWS2 ≥ 5. The prevalence of COVID-19 was higher in SH (11.0 %=277/2520) than YH (8.7 %=343/3924) with a higher first NEWS2 scores ( SH 3.2 vs YH 2.8) but similar in-hospital mortality (SH 8.4 % vs YH 8.2 %). The c-statistics for predicting the risk of COVID-19 for models M0',M1',M2' in the development dataset were: M0': 0.71 (95 %CI 0.68-0.74); M1': 0.67 (95 %CI 0.64-0.70) and M2': 0.78 (95 %CI 0.75-0.80)). For the validation datasets the c-statistics were: M0' 0.65 (95 %CI 0.61-0.68); M1': 0.67 (95 %CI 0.64-0.70) and M2': 0.72 (95 %CI 0.69-0.75) ). The calibration slope was similar across all models but Model M2' had the highest sensitivity (M0' 44 % (95 %CI 38-50 %); M1' 53 % (95 %CI 47-59 %) and M2': 57 % (95 %CI 51-63 %)) and specificity (M0' 75 % (95 %CI 73-77 %); M1' 72 % (95 %CI 70-74 %) and M2': 76 % (95 %CI 74-78 %)) for the validation dataset at NEWS2 ≥ 5. Model M2' appears to be reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned hospital admissions. / The Health Foundation (Award No 7380) and the National Institute for Health Research (NIHR) Yorkshire and Humber Patient Safety Translational Research Centre (NIHR Yorkshire and Humber PSTRC) (Award No PSTRC-2016-006) / Research Development Fund Publication Prize Award winner, Aug 2021.

Introduction: A recent study at the acute admission unit (AAU), revealed that 13.5 percent ofall patients discharged from the department, were readmitted within 30 days during 2018. Inthe group of 80 years and above, the cause for re-admission was multifactorial. Aim: To identify factors that are associated with unplanned re-admission, palliative decision,or death within 30 days after discharge from the AAU, in patients of 80 years or above. Anotheraim is to examine if longer hospital stay, patient discharge planning and fast follow-up canprotect against these outcomes. Methods: A retrospective cohort study comprising 287 patients. Data on age, sex, length ofstay, comorbidities (Elixhauser comorbidity index), frailty (Clinical frailty scale), NationalEarly Warning Score (NEWS), social status, home care, lab values and outcome were collected.All variables were analyzed using Chi-square test with univariate and multivariate logisticregression, and a p-value < 0.05 was considered statistically significant. Results: 276 patients were included. A NEWS ≥ 3 was associated with significantly increasedrisk for poor outcome (odds ratio 2.4). Living with someone without municipal support wasassociated with a significantly decreased risk for poor outcome (odds ratio 0.21). Conclusions: The results indicate that it is crucial to stabilize patients of 80 years or abovebefore discharge. And that living with someone without municipal support is a protective factor.

Yes / Objectives: To develop a logistic regression model to predict the risk of sepsis following emergency medical admission using the patient’s first, routinely collected, electronically recorded vital signs and blood test results and to validate this novel computer-aided risk of sepsis model, using data from another hospital. Design: Cross-sectional model development and external validation study reporting the C-statistic based on a validated optimized algorithm to identify sepsis and severe sepsis (including septic shock) from administrative hospital databases using International Classification of Diseases, 10th Edition, codes. Setting: Two acute hospitals (York Hospital - development data; Northern Lincolnshire and Goole Hospital - external validation data). Patients: Adult emergency medical admissions discharged over a 24-month period with vital signs and blood test results recorded at admission. Interventions: None. Main Results: The prevalence of sepsis and severe sepsis was lower in York Hospital (18.5% = 4,861/2,6247; 5.3% = 1,387/2,6247) than Northern Lincolnshire and Goole Hospital (25.1% = 7,773/30,996; 9.2% = 2,864/30,996). The mortality for sepsis (York Hospital: 14.5% = 704/4,861; Northern Lincolnshire and Goole Hospital: 11.6% = 899/7,773) was lower than the mortality for severe sepsis (York Hospital: 29.0% = 402/1,387; Northern Lincolnshire and Goole Hospital: 21.4% = 612/2,864). The C-statistic for computer-aided risk of sepsis in York Hospital (all sepsis 0.78; sepsis: 0.73; severe sepsis: 0.80) was similar in an external hospital setting (Northern Lincolnshire and Goole Hospital: all sepsis 0.79; sepsis: 0.70; severe sepsis: 0.81). A cutoff value of 0.2 gives reasonable performance. Conclusions: We have developed a novel, externally validated computer-aided risk of sepsis, with reasonably good performance for estimating the risk of sepsis for emergency medical admissions using the patient’s first, electronically recorded, vital signs and blood tests results. Since computer-aided risk of sepsis places no additional data collection burden on clinicians and is automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure. / Health Foundation

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