scholarly journals Performance of externally validated enhanced computer-aided versions of the National Early Warning Score in predicting mortality following an emergency admission to hospital in England: a cross-sectional study

BMJ Open ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. e031596 ◽  
Author(s):  
Muhammad Faisal ◽  
Donald Richardson ◽  
Andy Scally ◽  
Robin Howes ◽  
Kevin Beatson ◽  
...  
Keyword(s):  
Early Warning ◽  
Clinical Decision Making ◽  
External Validation ◽  
Model Development ◽  
Emergency Admission ◽  
Early Warning Score ◽  
Cross Sectional ◽  
Risk Of Death ◽  
Computer Aided ◽  
National Early Warning Score

ObjectivesIn the English National Health Service, the patient’s vital signs are monitored and summarised into a National Early Warning Score (NEWS) to support clinical decision making, but it does not provide an estimate of the patient’s risk of death. We examine the extent to which the accuracy of NEWS for predicting mortality could be improved by enhanced computer versions of NEWS (cNEWS).DesignLogistic regression model development and external validation study.SettingTwo acute hospitals (YH—York Hospital for model development; NH—Northern Lincolnshire and Goole Hospital for external model validation).ParticipantsAdult (≥16 years) medical admissions discharged over a 24-month period with electronic NEWS (eNEWS) recorded on admission are used to predict mortality at four time points (in-hospital, 24 hours, 48 hours and 72 hours) using the first electronically recorded NEWS (model M0) versus a cNEWS model which included age+sex (model M1) +subcomponents of NEWS (including diastolic blood pressure) (model M2).ResultsThe risk of dying in-hospital following emergency medical admission was 5.8% (YH: 2080/35 807) and 5.4% (NH: 1900/35 161). The c-statistics for model M2 in YH for predicting mortality (in-hospital=0.82, 24 hours=0.91, 48 hours=0.88 and 72 hours=0.88) was higher than model M0 (in-hospital=0.74, 24 hours=0.89, 48 hours=0.86 and 72 hours=0.85) with higher Positive Predictive Value (PPVs) for in-hospital mortality (M2 19.3% and M0 16.6%). Similar findings were seen in NH. Model M2 performed better than M0 in almost all major disease subgroups.ConclusionsAn externally validated enhanced computer-aided NEWS model (cNEWS) incrementally improves on the performance of a NEWS only model. Since cNEWS places no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated to determine if it can improve care in hospitals that have eNEWS systems.

scholarly journals Use of the first National Early Warning Score recorded within 24 hours of admission to estimate the risk of in-hospital mortality in unplanned COVID-19 patients: a retrospective cohort study

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e043721
Author(s):  
Donald Richardson ◽  
Muhammad Faisal ◽  
Massimo Fiori ◽  
Kevin Beatson ◽  
Mohammed Mohammed
Keyword(s):  
Hospital Mortality ◽  
Early Warning ◽  
Absolute Risk ◽  
Cross Sectional Study ◽  
Early Warning Score ◽  
Cross Sectional ◽  
Risk Of Mortality ◽  
Baseline Mortality ◽  
Acute Hospitals ◽  
National Early Warning Score

ObjectivesAlthough the National Early Warning Score (NEWS) and its latest version NEWS2 are recommended for monitoring deterioration in patients admitted to hospital, little is known about their performance in COVID-19 patients. We aimed to compare the performance of the NEWS and NEWS2 in patients with COVID-19 versus those without during the first phase of the pandemic.DesignA retrospective cross-sectional study.SettingTwo acute hospitals (Scarborough and York) are combined into a single dataset and analysed collectively.ParticipantsAdult (≥18 years) non-elective admissions discharged between 11 March 2020 and 13 June 2020 with an index or on-admission NEWS2 electronically recorded within ±24 hours of admission to predict mortality at four time points (in-hospital, 24 hours, 48 hours and 72 hours) in COVID-19 versus non-COVID-19 admissions.ResultsOut of 6480 non-elective admissions, 620 (9.6%) had a diagnosis of COVID-19. They were older (73.3 vs 67.7 years), more often male (54.7% vs 50.1%), had higher index NEWS (4 vs 2.5) and NEWS2 (4.6 vs 2.8) scores and higher in-hospital mortality (32.1% vs 5.8%). The c-statistics for predicting in-hospital mortality in COVID-19 admissions was significantly lower using NEWS (0.64 vs 0.74) or NEWS2 (0.64 vs 0.74), however, these differences reduced at 72hours (NEWS: 0.75 vs 0.81; NEWS2: 0.71 vs 0.81), 48 hours (NEWS: 0.78 vs 0.81; NEWS2: 0.76 vs 0.82) and 24hours (NEWS: 0.84 vs 0.84; NEWS2: 0.86 vs 0.84). Increasing NEWS2 values reflected increased mortality, but for any given value the absolute risk was on average 24% higher (eg, NEWS2=5: 36% vs 9%).ConclusionsThe index or on-admission NEWS and NEWS2 offers lower discrimination for COVID-19 admissions versus non-COVID-19 admissions. The index NEWS2 was not proven to be better than the index NEWS. For each value of the index NEWS/NEWS2, COVID-19 admissions had a substantially higher risk of mortality than non-COVID-19 admissions which reflects the increased baseline mortality risk of COVID-19.

Glucose as an additional parameter to National Early Warning Score (NEWS) in prehospital setting enhances identification of patients at risk of death: an observational cohort study

2020 ◽  
pp. emermed-2018-208309
Author(s):  
Hanna Vihonen ◽  
Mitja Lääperi ◽  
Markku Kuisma ◽  
Jussi Pirneskoski ◽  
Jouni Nurmi
Keyword(s):  
At Risk ◽  
Blood Glucose ◽  
Regression Model ◽  
Early Warning ◽  
Prehospital Setting ◽  
Early Warning Score ◽  
Calibration Model ◽  
Risk Of Death ◽  
Patients At Risk ◽  
National Early Warning Score

BackgroundTo determine if prehospital blood glucose could be added to National Early Warning Score (NEWS) for improved identification of risk of short-term mortality.MethodsRetrospective observational study (2008–2015) of adult patients seen by emergency medical services in Helsinki metropolitan area for whom all variables for calculation of NEWS and a blood glucose value were available. Survival of 24 hours and 30 days were determined. The NEWS parameters and glucose were tested by multivariate logistic regression model. Based on ORs we formed NEWSgluc model with hypoglycaemia (≤3.0 mmol/L) 3, normoglycaemia 0 and hyperglycaemia (≥11.1 mmol/L) 1 points. The scores from NEWS and NEWSgluc were compared using discrimination (area under the curve), calibration (Hosmer-Lemeshow test), likelihood ratio tests and reclassification (continuous net reclassification index (cNRI)).ResultsData of 27 141 patients were included in the study. Multivariable regression model for NEWSgluc parameters revealed a strong association with glucose disturbances and 24-hour and 30-day mortality. Likelihood ratios (LRs) for mortality at 24 hours using a cut-off point of 15 were for NEWSgluc: LR+ 17.78 and LR− 0.96 and for NEWS: LR+ 13.50 and LR− 0.92. Results were similar at 30 days. Risks per score point estimation and calibration model showed glucose added benefit to NEWS at 24 hours and at 30 days. Although areas under the curve were similar, reclassification test (cNRI) showed overall improvement of classification of survivors and non-survivors at 24 days and 30 days with NEWSgluc.ConclusionsIncluding glucose in NEWS in the prehospital setting seems to improve identification of patients at risk of death.

scholarly journals Predictive accuracy of computer-aided versions of the on-admission National Early Warning Score in estimating the risk of COVID-19 for unplanned admission to hospital: a retrospective development and validation study

2020 ◽  
Author(s):  
Muhammad Faisal ◽  
Mohammed A Mohammed ◽  
Donald Richardson ◽  
Ewout W. Steyerberg ◽  
Massimo Fiori ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Model Validation ◽  
Validation Study ◽  
Early Warning ◽  
Model Development ◽  
Early Warning Score ◽  
Validation Dataset ◽  
Unplanned Admission ◽  
National Early Warning Score ◽  
Development And Validation

AbstractObjectivesTo consider the potential of the National Early Warning Score (NEWS2) for COVID-19 risk prediction on unplanned admission to hospital.DesignLogistic regression model development and validation study using a cohort of unplanned emergency medical admission to hospital.SettingYork Hospital (YH) as model development dataset and Scarborough Hospital (SH) as model validation dataset.ParticipantsUnplanned 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) based on admission NEWS2 electronically recorded within ±24 hours of admission. We used logistic regression modelling to predict the risk of COVID-19 using NEWS2 (Model M0’) versus enhanced cNEWS models which included age + sex (model M1’) + subcomponents (including diastolic blood pressure + oxygen flow rate + oxygen scale) of NEWS2 (model M2’). The ICD-10 code ‘U071’ was used to identify COVID-19 admissions. Model performance was evaluated according to discrimination (c statistic), calibration (graphically), and clinical usefulness at NEWS2 ≥5.ResultsThe prevalence of COVID-19 was higher in SH (11.0%=277/2520) than YH (8.7%=343/3924) with higher index NEWS2 (3.2 vs 2.8) but similar in-hospital mortality (8.4% vs 8.2%). The c-statistics for predicting COVID-19 for cNEWS models (M1’,M2’) was substantially better than NEWS2 alone (M0’) in development (M2’: 0.78 (95%CI 0.75-0.80) vs M0’ 0.71 (95%CI 0.68-0.74)) and validation datasets (M2’: 0.72 (95%CI 0.69-0.75) vs M0’ 0.65 (95%CI 0.61-0.68)). Model M2’ had better calibration than Model M0’ with improved sensitivity (M2’: 57% (95%CI 51%-63%) vs M0’ 44% (95%CI 38%-50%)) and similar specificity (M2’: 76% (95%CI 74%-78%) vs M0’ 75% (95%CI 73%-77%)) for validation dataset at NEWS2≥5.ConclusionsModel M2’ is reasonably accurate for predicting the on-admission risk of COVID-19. It may be clinically useful for an early warning system at the time of admission especially to triage large numbers of unplanned hospital admissions.

scholarly journals Evaluation and Improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study

2020 ◽  
Author(s):  
Ewan Carr ◽  
Rebecca Bendayan ◽  
Daniel Bean ◽  
Matt Stammers ◽  
Wenjuan Wang ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Early Warning ◽  
External Validation ◽  
Early Warning Score ◽  
Minimal Set ◽  
Estimated Gfr ◽  
Risk Stratification Model ◽  
Risk Scoring ◽  
National Early Warning Score ◽  
The Uk

AbstractObjectivesTo evaluate the National Early Warning Score (NEWS2), currently recommended in the UK for risk-stratification of severe COVID-19 outcomes, and subsequently identify and validate a minimal set of common parameters taken at hospital admission that improve the score.DesignRetrospective observational cohort with internal and multi-hospital external validation.SettingSecondary care.InterventionsNot applicable.ParticipantsMain outcome measuresResultsTraining and temporal external validation cohorts comprised 1464 patients admitted to King’s College Hospital NHS Foundation Trust (KCH) with COVID-19 disease from 1st March to 30th April 2020. External validation cohorts included 3869 patients from two UK NHS Trusts (Guys and St Thomas’ Hospitals, GSTT and University Hospitals Southampton, UHS) and two hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji Hospital).The primary outcome was patient status at 14 days after symptom onset categorised as severe disease (transferred to intensive care unit or death). Age, physiological measures, blood biomarkers, sex, ethnicity and comorbidities (hypertension, diabetes, cardiovascular, respiratory and kidney diseases) were included.ConclusionsNEWS2 score on admission was a weak predictor of severe COVID-19 infection (AUC = 0.628). Adding age and common blood tests (CRP, neutrophil count, estimated GFR and albumin) provided substantial improvements to a risk stratification model, particularly in relation to sensitivity, but performance was only moderate (AUC = 0.753). Improvement over NEWS2 remained robust and generalisable in GSTT (AUC = 0.817), UHS (AUC = 0.835) and Wuhan hospitals (AUC = 0.918).Adding age and a minimal set of blood parameters to NEWS2 improves the detection of patients likely to develop severe COVID-19 outcomes. This finding was replicated across NHS and non-UK hospitals. Adding a few common parameters to a pre-existing acuity score allows rapid and easy implementation of this risk-scoring system.Key MessagesThe National Early Warning Score (NEWS2), currently recommended for severe COVID-19 disease in the UK shows overall poor discrimination for severe outcomes (transfer to ICU or death). It can be improved by the addition of a small number of blood and physiological parameters routinely measured at hospital admission.The addition of age and a minimal set of common blood tests (C-reactive protein, neutrophil count, estimated GFR and albumin) provided substantial improvements in a risk stratification model.Although predictive performance varied from hospital to hospital, the improvement over NEWS2 alone was consistent across different patient cohorts.The proposed addition of a limited number of dichotomised parameters is easily derived from a pre-existing acuity score would be substantially easier to implement in a short-time scale compared to novel high-dimensional risk-scoring systems.

scholarly journals Development, Implementation, and Evaluation of an In-Hospital Optimized Early Warning Score for Patient Deterioration

2020 ◽  
Vol 5 (1) ◽  
pp. 238146831989966 ◽  
Author(s):  
Cara O’Brien ◽  
Benjamin A. Goldstein ◽  
Yueqi Shen ◽  
Matthew Phelan ◽  
Curtis Lambert ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Early Warning ◽  
Clinical Decision Making ◽  
Risk Model ◽  
Clinical Status ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Early Warning Score ◽  
Support Tool ◽  
National Early Warning Score

Background. Identification of patients at risk of deteriorating during their hospitalization is an important concern. However, many off-shelf scores have poor in-center performance. In this article, we report our experience developing, implementing, and evaluating an in-hospital score for deterioration. Methods. We abstracted 3 years of data (2014–2016) and identified patients on medical wards that died or were transferred to the intensive care unit. We developed a time-varying risk model and then implemented the model over a 10-week period to assess prospective predictive performance. We compared performance to our currently used tool, National Early Warning Score. In order to aid clinical decision making, we transformed the quantitative score into a three-level clinical decision support tool. Results. The developed risk score had an average area under the curve of 0.814 (95% confidence interval = 0.79–0.83) versus 0.740 (95% confidence interval = 0.72–0.76) for the National Early Warning Score. We found the proposed score was able to respond to acute clinical changes in patients’ clinical status. Upon implementing the score, we were able to achieve the desired positive predictive value but needed to retune the thresholds to get the desired sensitivity. Discussion. This work illustrates the potential for academic medical centers to build, refine, and implement risk models that are targeted to their patient population and work flow.

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