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dc.contributor.authorFaisal, Muhammad*
dc.contributor.authorRichardson, D.*
dc.contributor.authorScally, Andy J.*
dc.contributor.authorHowes, R.*
dc.contributor.authorBeatson, K.*
dc.contributor.authorSpeed, K.*
dc.contributor.authorMohammad, Mohammad A.*
dc.date.accessioned2019-05-08T09:01:08Z
dc.date.available2019-05-08T09:01:08Z
dc.date.issued2019-04-08
dc.identifier.citationFaisal 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.en_US
dc.identifier.urihttp://hdl.handle.net/10454/17028
dc.descriptionYesen_US
dc.description.abstractBackground: 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.en_US
dc.description.sponsorshipThe Health Foundation, National Institute for Health Research (NIHR) Yorkshire and Humberside Patient Safety Translational Research Centreen_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1503/cmaj.181418en_US
dc.rights(c) 2019 The Authors. Full-text reproduced in accordance with the CMA self-archiving policy.en_US
dc.subjectVital signsen_US
dc.subjectNational early warning scoreen_US
dc.subjectEmergency admissionen_US
dc.subjectSepsisen_US
dc.subjectComputer aided national early warning scoreen_US
dc.subjectResearch Development Fund Publication Prize Award
dc.titleComputer-aided National Early Warning Score to predict the risk of sepsis following emergency medical admission to hospital: a model development and external validation studyen_US
dc.status.refereedYesen_US
dc.date.Accepted2019-03-20
dc.typeArticleen_US
dc.type.versionAccepted manuscripten_US
dc.description.publicnotesResearch Development Fund Publication Prize Award winner, April 2019.
refterms.dateFOA2019-05-08T09:01:08Z


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