scholarly journals Computer-aided National Early Warning Score to predict the risk of sepsis following emergency medical admission to hospital: a model development and external validation study

2019 ◽  
Vol 191 (14) ◽  
pp. E382-E389 ◽  
Author(s):  
Muhammad Faisal ◽  
Donald Richardson ◽  
Andrew J. Scally ◽  
Robin Howes ◽  
Kevin Beatson ◽  
...  
Keyword(s):  
Validation Study ◽  
Early Warning ◽  
External Validation ◽  
Model Development ◽  
Early Warning Score ◽  
Medical Admission ◽  
Emergency Medical ◽  
External Validation Study ◽  
Computer Aided ◽  
National Early Warning Score

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 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 Development and validation of a novel computer-aided score to predict the risk of in-hospital mortality for acutely ill medical admissions in two acute hospitals using their first electronically recorded blood test results and vital signs: a cross-sectional study

BMJ Open ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. e022939 ◽  
Author(s):  
Muhammad Faisal ◽  
Andrew J Scally ◽  
Natalie Jackson ◽  
Donald Richardson ◽  
Kevin Beatson ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Blood Test ◽  
External Validation ◽  
Model Development ◽  
Vital Signs ◽  
Test Results ◽  
Medical Admission ◽  
Emergency Medical ◽  
Computer Aided ◽  
Acute Hospitals

ObjectivesThere are no established mortality risk equations specifically for emergency medical patients who are admitted to a general hospital ward. Such risk equations may be useful in supporting the clinical decision-making process. We aim to develop and externally validate a computer-aided risk of mortality (CARM) score by combining the first electronically recorded vital signs and blood test results for emergency medical admissions.DesignLogistic regression model development and external validation study.SettingTwo acute hospitals (Northern Lincolnshire and Goole NHS Foundation Trust Hospital (NH)—model development data; York Hospital (YH)—external validation data).ParticipantsAdult (aged ≥16 years) medical admissions discharged over a 24-month period with electronic National Early Warning Score(s) and blood test results recorded on admission.ResultsThe risk of in-hospital mortality following emergency medical admission was 5.7% (NH: 1766/30 996) and 6.5% (YH: 1703/26 247). The C-statistic for the CARM score in NH was 0.87 (95% CI 0.86 to 0.88) and was similar in an external hospital setting YH (0.86, 95% CI 0.85 to 0.87) and the calibration slope included 1 (0.97, 95% CI 0.94 to 1.00).ConclusionsWe have developed a novel, externally validated CARM score with good performance characteristics for estimating the risk of in-hospital mortality following an emergency medical admission using the patient’s first, electronically recorded, vital signs and blood test results. Since the CARM score places no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.

scholarly journals Impact of the level of sickness on higher mortality in emergency medical admissions to hospital at weekends

2017 ◽  
Vol 22 (4) ◽  
pp. 236-242 ◽  
Author(s):  
Mohammed Mohammed ◽  
Muhammad Faisal ◽  
Donald Richardson ◽  
Robin Howes ◽  
Kevin Beatson ◽  
...  
Keyword(s):  
Early Warning ◽  
Odds Ratio ◽  
Vital Signs ◽  
Early Warning Score ◽  
Emergency Admissions ◽  
Clinical Assessments ◽  
Admission Time ◽  
Emergency Medical ◽  
Acute Hospitals ◽  
National Early Warning Score

Objective 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 < 0.001) with a higher mortality (weekend: 706/11,332 6.23% vs. weekday: 2039/35,785 5.70%; odds ratio = 1.10, 95% CI 1.01 to 1.20, p = 0.04) which was no longer seen after adjusting for the index National Early Warning Score (odds ratio = 0.99, 95% CI 0.90 to 1.09, p = 0.87). Index National Early Warning Score was recorded sooner (−0.45 h, 95% CI −0.52 to −0.38, p < 0.001) for weekend admissions. Conclusions Emergency medical admissions at the weekend with electronic National Early Warning Score recorded within 24 h are sicker, have earlier clinical assessments, and after adjusting for the severity of their sickness, do not appear to have a higher mortality compared to weekday admissions. A larger definitive study to confirm these findings is needed.

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.

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