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    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

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    faisal_et_al_2019.pdf (857.9Kb)
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    Publication date
    2019-04-08
    Author
    Faisal, Muhammad
    Richardson, D.
    Scally, Andy J.
    Howes, R.
    Beatson, K.
    Speed, K.
    Mohammad, Mohammad A.
    Keyword
    Vital signs
    National early warning score
    Emergency admission
    Sepsis
    Computer aided national early warning score
    Research Development Fund Publication Prize Award
    Rights
    (c) 2019 The Authors. Full-text reproduced in accordance with the CMA self-archiving policy.
    Peer-Reviewed
    Yes
    
    Metadata
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    Abstract
    Background: 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.
    URI
    http://hdl.handle.net/10454/17028
    Version
    Accepted manuscript
    Citation
    Faisal M, Richardson D, Scally AJ et al (2019) 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. CMAJ. 191(14): E382-E389.
    Link to publisher’s version
    https://doi.org/10.1503/cmaj.181418
    Type
    Article
    Notes
    Research Development Fund Publication Prize Award winner, April 2019.
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    Health Studies Publications

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